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ffe4a610-aead-4300-8fef-a70aeecd3fb7.221 | **Norihiko Misawa**
Research Institute for Bioresources and Biotechnology, Ishikawa Prefectural University, Suematsu, Nonoichi-machi, Ishikawa 921-8836, Japan; E-Mail: [email protected];
Tel.: +81-76-227-7525; Fax: +81-76-227-7557
*Received: 21 March 2011; in revised form: 19 April 2011 / Accepted: 26 April 2011 / Published: 6 May 2011*
**Abstract:** Marine bacteria belonging to genera *Paracoccus* and *Brevundimonas* of the
΅*-Proteobacteria* class can produce C40-type dicyclic carotenoids containing two Ά-end groups (Ά rings) that are modified with keto and hydroxyl groups.
These bacteria produce astaxanthin, adonixanthin, and their derivatives, which are ketolated by carotenoid Ά-ring 4(4ȝ)-ketolase (4(4ȝ)-oxygenase; CrtW) and hydroxylated by carotenoid Ά-ring 3(3ȝ)-hydroxylase (CrtZ). In addition, the genus *Brevundimonas* possesses a gene for carotenoid Άring 2(2<sup>ȝ</sup>)-hydroxylase (CrtG). This review focuses on these carotenoid Άring-modifying enzymes that are promiscuous for carotenoid substrates, and pathway engineering for the production of xanthophylls (oxygencontaining carotenoids) in *Escherichia coli*, using these enzyme genes. Such pathway engineering researches are performed towards efficient production not only of commercially
important xanthophylls such as astaxanthin, but also of xanthophylls minor in nature (e.g., Ά-ring(s)-2(2ȝ)-hydroxylated carotenoids).
**Keywords:** *Paracoccus*; *Brevundimonas*; marine bacteria; ketocarotenoid; functional xanthophyll
| doab | 2025-04-07T04:13:04.398490 | 1-5-2021 17:07 | {
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.222 | **1. Introduction**
Many bacteria that have been isolated from marine environments can synthesize a variety of carotenoid pigments [1]. For example, acyclic C30-type carotenoic acids were identified in some marine bacteria such as *Planococcus maritimus* [2] and *Rubritalea squalenifaciens* [3]. *Algoriphagus* sp. KK10202C of the *Flexibacteraceae* family, which was isolated from a marine sponge, was found to produce flexixanthin ((3*S*)- 3,1<sup>ȝ</sup>-dihydroxy-3<sup>ȝ</sup>,4<sup>ȝ</sup>-didehydro-1<sup>ȝ</sup>2<sup>ȝ</sup>-dihydro-Ά,Μ-caroten-4-one) and deoxyflexixanthin (1ȝ-hydroxy-3<sup>ȝ</sup>,4<sup>ȝ</sup>-didehydro-1<sup>ȝ</sup>2<sup>ȝ</sup>-dihydro-Ά,Μ-caroten-4-one) [4], which are C40-type monocyclic carotenoids containing one Ά-end group ( Ά ring) (called monocyclic carotenoids in this review). Other marine bacteria including strain P99-3, which belong to the *Flavobacteriaceae* family, were shown to produce monocyclic carotenoids, myxol ((3 *<sup>R</sup>*,2<sup>ȝ</sup>*S*)-3<sup>ȝ</sup>,4<sup>ȝ</sup>-didehydro-1<sup>ȝ</sup>,2<sup>ȝ</sup>-dihydro-Ά,Μ-carotene-3,1<sup>ȝ</sup>,2<sup>ȝ</sup>-triol) and saproxanthin ((3 *<sup>R</sup>*)-3<sup>ȝ</sup>,4<sup>ȝ</sup>-didehydro-1<sup>ȝ</sup>,2<sup>ȝ</sup>-dihydro-Ά,Μ-carotene-3,1<sup>ȝ</sup>diol), and zeaxanthin ((3 *R*,3<sup>ȝ</sup>*R*)- Ά,Ά-carotene-3,3<sup>ȝ</sup>-diol) [5,6], which are a C40-type dicyclic carotenoid containing two Ά-end groups (called dicyclic carotenids in this review). Marine bacteria belonging to genus *Paracoccus*, *Brevundimonas* or *Erythrobacter* in the ΅*-Proteobacteria* class have been revealed to synthesize dicyclic carotenoids that are ketolated at the 4(4ȝ)-position(s) (called ketocarotenoids), e.g., astaxanthin ((3*S*,3<sup>ȝ</sup>*S*)-3,3<sup>ȝ</sup>-dihydroxy-Ά,Ά-carotene-4,4<sup>ȝ</sup>-dione) and adonixanthin ((3*S*,3<sup>ȝ</sup>*R*)-3,3<sup>ȝ</sup>-dyhydroxy-Ά,Ά-caroten-4-one) (Figure 1) [7–9].
**Figure 1.** Chemical structures of ketocarotenoids produced in marine bacteria, *Paracoccus* sp. and *Brevundimonas* sp., and feasible functions of the carotenoid biosynthesis enzymes. These bacteria synthesize dicyclic carotenoids. *Paracoccus* sp. and *Brevundimonas* sp. are demonstrated to possess the unique genes *crtX* and *crtG*, respectively, in addition to the common genes, *crtE*, *crtB*, *crtI*, *crtY*, *crtZ*, and *crtW* [10,11].
Among ketocarotenoids, astaxanthin and canthaxanthin ( Ά,Ά-carotene-4,4<sup>ȝ</sup>-dione) (specifically the former), are commercially important pigments as nutraceuticals and cosmetics that have anti-oxidation and anti-aging effects as well as colorants in aquaculture, while other ketocarotenoids are likely to have industrial potentials [12– 16]. This review focuses on carotenoid Ά-ring 4(4ȝ)-ketolase (4-oxygenase), carotenoid Ά-ring 3(3ȝ)-hydroxylase, and carotenoid Ά-ring 2(2ȝ)-hydroxylase, derived from the marine bacteria that belong to ΅*-Proteobacteria*, and pathway engineering for the production of functional xanthophylls via the incorporation of these Ά-ring-modifying enzyme genes.
| doab | 2025-04-07T04:13:04.398591 | 1-5-2021 17:07 | {
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.223 | **2. Bacterial Strains Producing Ketocarotenoids**
*Paracoccus* sp. strain N81106 (NBRC 101723), isolated from surface seawater near Aka island, Okinawa, Japan, was first shown to produce astaxanthin in bacteria [7,17]. This bacterium was also found to synthesize adonixanthin, adonixanthin Ά-D-glucoside, and astaxanthin Ά-D-glucoside [7,18]. *Paracoccus haeundaesis* BC74171T, isolated from the Haeundae Coast, Korea, was shown to produce astaxanthin mainly [19]. *Paracoccus marinus* KKL-A5T (NBRC 100637T), isolated from coastal seawater in Tokyo Bay, Japan, was found to produce adonixanthin diglucoside predominantly [20,21]. On the other hands, a marine bacterium *Brevundimona*s sp. strain SD212 (NBRC 101024) was revealed to synthesize not only astaxanthin and adonixanthin but also their 2(2ȝ)-hydroxylated metabolites, that is, 2-hydroxyastaxanthin ((2*R*,3*S*,3<sup>ȝ</sup>*S*)-2,3,3<sup>ȝ</sup>-trihydroxy-Ά,Ά-carotene-4,4<sup>ȝ</sup>-dione), 2-hydroxyadonixanthin ((2*R*,3*S*,3<sup>ȝ</sup>*R*)-2,3,3<sup>ȝ</sup>-trihydroxy-Ά,Ά-caroten-4-one), erythroxanthin ((3*S*,2<sup>ȝ</sup>*R*,3<sup>ȝ</sup>*R*)-3,2<sup>ȝ</sup>,3<sup>ȝ</sup>trihydroxy-Ά,Ά-caroten-4-one), 4-ketonostoxanthin ((2*R*,3*S*,2<sup>ȝ</sup>*R*,3<sup>ȝ</sup>*R*)-2,3,2<sup>ȝ</sup>,3<sup>ȝ</sup>tetrahydroxy-Ά,Ά-caroten-4-one) and 2,2ȝ-dyhydroxyastaxanthin ((2*<sup>R</sup>*,3*S*,2<sup>ȝ</sup>*R*,3<sup>ȝ</sup>*S*)- 2,3,2<sup>ȝ</sup>,3<sup>ȝ</sup>-tetrahydroxy-Ά,Ά-carotene-4,4<sup>ȝ</sup>-dione) [9]. Figure 1 shows the structures of the ketocarotenoids shown in this section and their feasible biosynthetic pathway. Figure 2 shows phylogenetic tree of the marine bacteria that produce astaxanthin and other ketocarotenoids, which were isolated in Marine biotechnology Institute (Kamaishi, Japan), along with the type strains relative to these bacteria, many of which are not marine bacteria but soil bacteria. Interestingly, the phylogenetically closest strains to the marine bacteria *Paracoccus* sp. N81106 and *Brevundimona*s sp. SD212 were soil bacteria *Paracoccus marcusii* DSM 11574T [22] and *Brevundimonas aurantiaca* ATCC 15266T, respectively (Figure 2).
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.224 | **3. Ketocarotenoid Biosynthesis Genes**
Genes required for the biosynthesis of dicyclic carotenoids were first isolated from soil bacteria *Pantoea ananatis* (reclassified from *Erwinia uredovora*) [23] and *Pantoea agglomerans* (*Erwinia herbicola*) [24,25], which cannot produce ketocarotenoids and belong to the *Enterobacteriaceae* family of class ·*-Proteobacteria* (the same family to *Escherichia coli*). The *Pantoea* carotenoid biosynthesis genes composed a gene cluster for the synthesis of zeaxanthin Ά-D-diglucoside from farnesyl diphosphate (farnesyl pyrophosphate; FPP) [25–27], and comprised six genes that encode geranygeranyl diphosphate (GGPP) synthase (CrtE) [27,28], phytoene synthase (CrtB) [27,29], phytoene desaturase (CrtI) [23,30], lycopene (Μ,Μcarotene) Ά-cyclase (CrtY) [23,31], Ά-carotene (Ά,Ά-carotene) 3-hydrocylase (CrtZ) [23], and zeaxanthin glucosyltransferase (CrtX) [23,32] (Figure 3).
**Figure 2.** Phylogenetic positions of *Paracoccus* sp., *Erythrobacer* sp., and *Brevundimonas* sp. strains deduced from their 16S rRNA sequences. ż represents marine bacteria. Bacterial strains, whose carotenoid biosynthesis genes were elucidated, are shown in boldface, and the second accession numbers in the parentheses shows those of carotenoid biosynthesis genes. *Paracoccus* sp. strain N81106 (MBIC01143 = NBRC 101723) and *Paracoccus* sp. strain PC1 (MBIC03024 = NBRC 101025) were formerly classified as *Agrobacterium aurantiacum* [17] and *Alacaligenes* sp. PC-1 [33], respectively. The phylogenetic tree was constructed as described [10]. The scale bar indicates a genetic distance of 0.02 (*Knuc*).
**Figure 3.** Pathway engineering for the production of functional xanthophylls using the carotenoid biosynthesis genes, *crtW*, *crtZ*, and/or *crtG*, which were isolated from the marine bacteria, *Paracoccus* sp. strain N81106 or *Brevundimonas* sp. strain SD212, in addition to the *crtE*, *crtB*, *crtI*, and *crtY* genes (and *crtX*) from *P. ananatis*.
These *crt* genes have widely used for complementation analysis of carotenoid biosynthesis genes isolated from other organisms, since they are functionally expressed in *E. coli* with ease [11,34–37]. The *P. agglomerans* gene cluster contained a gene encoding isopentenyl diphosphate (IPP) isomerase (Idi; type 2) [38] in addition to the six *crt* genes [39]. These seven carotenogenic (carotenoid-biosynthetic) genes were also found to exist in a carotenoid biosynthesis gene cluster of *Paracoccus* sp. strain N81106 [10,39]. This cluster included an additional gene, designated CrtW, which was elucidated to code for an enzyme responsible for ketocarotenoid formation, that is, CrtW proved to catalyze the synthesis of canthaxanthin from Άcarotene by complementation analysis using recombinant *E. coli* cells that contains the *P. ananatis crtE*, *crtB*, *crtI*, and *crtY* genes [33] (Figure 3). The hydropathy and transmembrane prediction analyses indicated that CrtW from *Paracoccus* sp. N81106 contains four transmembrane domains and two other hydrophobic regions, and its topology model is very similar to those for fatty acid desaturases [40]. It should be noted that it is recalcitrant to purify active CrtW and CrtZ proteins, which both are very likely iron-dependent integral membrane proteins, from the recombinant hosts as well as the native hosts, precluding their close enzymatic characterizations.
## **4. Carotenoid 4,4ȝ-Ketolase**
It has been revealed that only two enzymes, carotenoid 4,4ȝ-ketolase (4,4<sup>ȝ</sup>oxygenase) (Ά-ring 4(4ȝ)-ketolase; CrtW) and carotenoid 3,3ȝ-hydroxylase (Ά-ring 3(3<sup>ȝ</sup>)-hydroxylase; CrtZ), are sufficient to biosynthesize astaxanthin from Ά-carotene via eight intermediates including zeaxanthin, canthaxanthin and adonixanthin [35,40,41]. CrtW can convert not only the (un-substituted) Ά ring but also the 3- hydroxylated Ά ring into the respective 4-ketolated groups, and CrtZ can convert not only the (un-substituted) Ά ring but also the 4-ketolated Ά-ring into the respective 3- hydroxylated groups, as shown in Figure 4 [42–46]. An *in vitro* analysis with the crude enzymes of CrtW and CrtZ from the *E. coli* cells expressing the corresponding genes indicated that these enzymes are likely 2-oxoglutarate (΅-ketoglutarate)- dependent dioxygenases [42].
**Figure 4.** Catalytic functions of carotenoid 4,4ȝ-ketolases (oxygenases) and carotenoid 3<sup>ȝ</sup>3<sup>ȝ</sup>-hydroxylases. BKT means BKT1 or BKT2 from *H. pluvialis*.
The *crtW* genes were present not only in the above-mentioned ΅*-Proteobacteria* (Figure 2) but also in the marine bacterium *Algoriphagus* sp. KK10202C [4] and cyanobacterial strains such as *Anabaena* (*Nostoc*) sp. PCC 7120 and *N. punctiforme* [47,48]. These cyanobacteria produced not astaxanthin but echinenone (Ά,Ά-caroten4-one), and 4-ketomyxol 2ȝ-fucoside, a monocyclic carotenoid that includes the 4- ketolated Ά-ring [49]. Conversion efficiency to astaxanthin in several CrtWs was compared with recombinant *E. coli* cells that synthesize the carotenoid substrate zeaxanthin due to the presence of the *P. ananatis crtE*, *crtB*, *crtI*, *crtY*, and *crtZ* genes, in which each *crtW* gene from *Paracoccus* sp. N81106, *Paracoccus* sp. PC1, *Brevundimona*s sp. SD212, *Anabaena* sp. PCC7120, and *N. punctiforme* was expressed [44,46]. It was consequently shown that the *Brevundimona*s sp. SD212 CrtW, which exhibited the highest amino acid identity (96.3%) with that of the *B. aurantiaca* ATCC 15266 CrtW (accession no. AY166610), converted Ά-carotene to astaxanthin with the highest efficiency, along with the *P. ananatis* CrtZ [44,46]. In the case of the *Paracoccus* CrtWs, not only astaxanthin but also adonixanthin tended to accumulate, and this intermediate was difficult to be converted to astaxanthin [43,44]. The cyanobacterial CrtWs poorly converted zeaxanthin to astaxanthin via adonixanthin [46].
Two paralogous genes exhibiting significant homology to *crtW* were isolated from *H. pluvialis*, and designated *bkt* [50] or *crtO* [51]. These genes were renamed *bkt1* from *crtO* and *bkt2* from *bkt*, since "*crtO*" has been used for the other type of cyanobacterial Ά-ring 4(4ȝ)-ketolase genes, as shown later [52]. The BKT1 and BKT2 enzymes are very likely to have catalytic function same to the *Paracoccus* (or *Brevundimonas*) CrtWs, considering results from the *in vitro* study on BKT2 with
*E. coli* [42] and pathway engineering researches in higher plants as well as *E. coli* as the hosts [16,50,51,53].
A gene encoding a new type of Ά-ring 4(4ȝ)-ketolase (named CrtO) that showed apparent homology not to CrtW-type ketolase but to CrtI-type phytoene desaturase was first found in cyanobacterium *Synechocystis* sp. strain PCC 6803 [54], which produced 3ȝ-hydroxyechinenone (3ȝ-hydroxy-Ά,Ά-caroten-4-one), zeaxanthin and myxol 2ȝ-dimethyl-fucoside [55]. The *crtO* genes were also present in *Anabaena* sp. PCC 7120 [48], and an actinomycete *Rhodococcus erythropolis* and *Deinococcus radiodurans* R1 highly resistant to · and UV radiation [56], which produced other monocyclic carotenoids, e.g., the latter strain produced deinoxanthin (2,1<sup>ȝ</sup>dihydroxy-3<sup>ȝ</sup>,4<sup>ȝ</sup>-didehydro-1<sup>ȝ</sup>2<sup>ȝ</sup>-dihydro-Ά,Μ-caroten-4-one) [1]. An *in vivo* analysis on *crtO* was performed with recombinant *E. coli* cells that synthesize the carotenoid substrate Ά-carotene or zeaxanthin, into which each *crtO* gene from *Synechocystis* sp. PCC 6803 and *R. erythropolis* was introduced and expressed there [57]. This result along with previous finding [48] suggested that the CrtO-type of Ά-ring 4(4ȝ)- ketolases can accept only the (un-substituted) Ά ring(s) in Ά-carotene and probably in monocyclic carotenoids as the substrates (Figure 4).
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.225 | **5. Carotenoid 3,3ȝ-Hydroxylase**
The *crtZ* genes have been found not only in carotenogenic bacteria belonging to genera *Pantoea*, *Paracoccus* and *Brevundimonas*, but also in those belonging to the *Flavobacteriaceae* family [6,39]. Conversion efficiency to astaxanthin in several CrtZs was compared with recombinant *E. coli* cells that synthesize the carotenoid substrate canthaxanthin due to the presence of the *P. ananatis crtE*, *crtB*, *crtI* and *crtY* gens, and the *Paracoccus* N81106 *crtW* gene, into which each *crtZ* gene from *P. ananatis*, *Paracoccus* sp. N81106, *Paracoccus* sp. PC1, *Brevundimona*s sp. SD212, and marine bacterium strain P99-3 of the *Flavobacteriacea* family was introduced and expressed there [45]. It was consequently shown that the CrtZ enzymes from *Brevundimona*s sp. SD212 and the bacterial strain P99-3 converted Ά-carotene to astaxanthin with the highest and lowest efficiency, respectively, along with the *Paracoccus* N81106 CrtW [45].
On the other hands, no *crtZ* sequences have not been found in cyanobacteria, instead genes encoding a new type of Ά-ring 3(3ȝ)-hydroxylases (named CrtR) that exhibited moderate homology to CrtW have been found there [58,59]. The *crtR* genes were isolated from *Synechocystis* sp. strain PCC 6803, *Anabaena* sp. PCC 7120, *Anabaena variabilis*, and *N. punctiforme* [46,58]. An *in vivo* analysis on *crtR* was performed with recombinant *E. coli* cells that synthesize the carotenoid substrate Άcarotene or canthaxanthin, into which each *crtR* gene from *Synechocystis* sp. PCC 6803, *Anabaena* sp. PCC 7120, and *A. variabilis* was introduced and expressed there [46]. This result along with another result [60] indicated that the CrtR-type enzymes can hydroxylate the (un-substituted) Ά ring of monocyclic carotenoids such as deoxymyxol and deoxymyxol 2ȝ-fucoside at the 3 position (Figure 4). Among them, only the *Synechocystis* sp. PCC 6803CrtR was able to convert Ά-carotene to zeaxanthin [46,58,60]. A thermophilic bacterium *Thermus thermophilus* HB27, which grows at temperatures above 75 °C, was found to possess another new type of Ά-ring 3(3ȝ)- hydroxylase of the cytochrome P450 superfamily, named CYP175A1 [61]. The *in vivo* analysis with the gene strongly suggested that this thermostable P450 accepts only the (un-substituted) Ά ring of Ά-carotene as the substrate to form zeaxanthin [45,61].
## **6. Carotenoid 2,2ȝ-Hydroxylase**
Carotenoid 2,2ȝ-hydroxylase (Ά-ring 2(2ȝ)-hydroxylase) was first found in the marine bacterium *Brevundimonas* sp. strain SD212, and named CrtG [11]. An *in vivo* analysis on *crtG* was performed with recombinant *E. coli* cells that synthesize each carotenoid substrate (Ά-carotene, zeaxanthin, canthaxanthin, or astaxanthin), into which the *crtG* gene was introduced and expressed there [11]. The result indicated that the CrtG can hydroxylate the Ά rings substituted with 3-hydroxy and/or 4-keto groups in dicyclic carotenoids at the 2(2ȝ)-positions (Figures 1 and 3) [11]. The *crtG* genes were also isolated from soil bacteria *Brevundimonas vesicularis* DC263 and *B. aurantiaca* ATCC 15266 [62]. The *in vivo* analysis with these genes indicated that the *B. aurantiaca* CrtG enzyme (accession no. DQ497427), which exhibited the highest amino acid identity (98.8%) to that of the *Brevundimonas* SD212 CrtG, accepted the (un-substituted) Ά rings of Ά-carotene in addition to the substituted Ά rings as the substrates [62]. A *crtG* gene sequence, whose encoded amino acid sequence was 41% identical to the *Brevundimonas* sp. SD212 CrtG, was found in a thermophilic cyanobacterium *Thermosynechococcus elongatus*, which synthesized 2-hydroxylated carotenoids such as caloxanthin ((2*R*,3*R*,3<sup>ȝ</sup>*R*)-Ά,Ά-carotene-2,3,3<sup>ȝ</sup>-triol), nostoxanthin ((2*R*,3*R*,2<sup>ȝ</sup>*R*,3<sup>ȝ</sup>*R*)-Ά,Ά-carotene-2,3,2<sup>ȝ</sup>,3<sup>ȝ</sup>-tetrol) (Figure 3), and 2-hydroxymyxol 2<sup>ȝ</sup>fucoside [63].
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.226 | **7. Pathway Engineering for the Synthesis of Functional Xanthophylls via the Incorporation of** *crtW***,** *crtZ***, and/or** *crtG* **Genes**
Figure 3 shows xanthophylls that were produced in recombinant *E. coli* cells via the incorporation of the marine bacterial *crtW*, *crtZ*, and/or *crtG* genes along with the *Pantoea crtE*, *crtB*, *crtI*, and *crtY* genes. The recombinant *E. coli* strain that expresses the four *Pantoea crt* genes can produce Ά-carotene predominantly (approximately 0.2–1 mg·gƺ1 dry cell weight). The coexpression of the *crtW*, *crtZ*, and/or *crtG* genes in the Ά-carotene-synthesizing *E. coli* cells confer the ability to produce not only commercially important xanthophylls such as astaxanthin but also xanthophylls minor in nature (e.g., Ά-ring(s)-2(2ȝ)-hydroxylated carotenoids), which are difficult to synthesize chemically. Particularly, the chemical synthesis of 2(2ȝ)- hydroxycarotenoids are likely to be recalcitrant, due to high-density around the 1,2- positions of the Ά ring in these xanthophylls. We showed that the coexpression of the *Brevundimonas* sp. SD212 *crtW* gene and the *P. ananatis crtZ* gene in the Άcarotene-synthesizing *E. coli* due to the presence of the four *crt* genes of *P. ananatis* resulted in predominant production of astaxanthin [44,46]. The *Paracoccus* sp. N81106 *crtW* gene was evolved by random mutagenesis to have improved activity [40]. It is also demonstrated that the coexpression of the *crtW* gene and the *crtG* gene from *Brevundimonas* sp. SD212 or from *B. aurantiaca* ATCC 15266 in the Ά-carotenesynthesizing *E. coli* resulted in dominant production of 2,2ȝ-dihydroxycanthaxanthin and 2-hydroxycanthaxanthin, while the substrate canthaxanthin accumulated [11,62]. The coexpression of the *crtZ* gene and the *crtG* gene in the Ά-carotenesynthesizing *E. coli* resulted in predominant production of nostoxanthin along with small amounts of caloxanthin [11,62]. The coexpression of all the three genes (*crtW*, *crtZ*, and *crtG*) in the Ά-carotene-synthesizing *E. coli* resulted in dominant production of 2,2ȝ-dihydroxyastaxanthin and 2-hydroxyastaxanthin [11]. When the *P. ananatis crtX* gene was coexpressed in addition to appropriate combinations of the above *crt* genes in *E. coli*, resultant *E. coli* cells were able to synthesize carotenoid-glycosides such as caloxanthin Ά-D-glucoside [64] and astaxanthin Ά-D-diglucoside [65], as shown in Figure 3.
The ·-ray-tolerant bacterium *D. radiodurans* R1 produces the monocyclic carotenoid including the 2-hydroxy-4-keto-Ά-ring, deinoxanthin [1]. 2,2<sup>ȝ</sup>-Dihydroxycanthaxanthin was shown to have strong inhibitory effect against lipid peroxidation in a rat brain homogenate [11]. Such minor ketocarotenoids, which include the 2-hydroxy-4-keto-Ά-ring, may have beneficial effects on human health as well as anti-oxidation function, while few works are present examining their biological functions.
When carotenoid biosynthesis genes starting from the utilization of FPP are introduced in *E. coli*, as above-mentioned, amounts of carotenoids produced with the recombinant *E. coli* cells are far from the practical use, which was difficult to exceed 1 mg·gƺ1 dry weight. In order to overcome this problem, many pathway engineering researches in *E. coli* have been performed for increasing intracellular concentration of FPP (e.g., recently reviewed [66,67]). For example, the coexpression of the *idi* (type 1) gene from *H. pluvialis*, *Xanthophyllomyces dendrorhous* (renamed from *Phaffia rhodozyma*), or *Saccharomyces cerevisiae*, as well as the *idi* (type 2) from *Streptomyces* sp. strain CL190, was shown to be effective to increase FPP content [68,69]. The introduction of heterologous mevalonate pathway genes in *E. coli* along with an *idi* (type 2) gene has been described to efficiently improve the productivity of carotenoids or sesquiterpenes that are synthesized from FPP [69–73]. For example, Yoon *et al.* [73] produced 22 mg·gƺ1 dry cell weight of lycopene in 72 h using such mevalonate-pathway-engineered *E. coli* cells. On the other hand, production of lycopene reached high levels (near to 20 mg·gƺ1 dry cell weight) in 24-h batch flask culture in pathway-engineered *E. coli*, which reflected results of multi-dimensional gene target search or gene-knockout analysis [74]. These finding should be applied to efficient production of the above-mentioned functional xanthophylls with *E. coli* cells.
Pathway engineering researches in higher plants have also been performed for efficient production of astaxanthin, which utilized the marine bacterial *crtW* genes from *Paracoccus* sp. N81106 or *Brevundimonas* sp. SD212, or the *H. pluvialis bkt1* or *bkt2* genes, as reviewed [16,39,53]. For example, the *Brevundimonas* sp. SD212 *crtW* and *crtZ* genes, whose nucleotide sequence is modified to codon usage of higher plants, were successfully overexpressed in the chloroplasts of tobacco plants (*Nicotiana tabacum*), and astaxanthin level produce there reached 5.44 mg·gƺ1 dry weight (74% of total carotenoids) [75].
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.227 | **8. Conclusions**
This review has focused on the carotenoid Ά-ring-modifying enzymes, CrtW, CrtZ and CrtG, derived from the marine bacteria of the ΅-*Proteobacteria* class, and pathway engineering for the production of xanthophylls in *E. coli*, using these enzyme genes. Such pathway engineering researches are performed towards efficient production not only of commercially important xanthophylls such as astaxanthin, but also of xanthophylls minor in nature, which are difficult to synthesize chemically, and expected to have beneficial effects on human health as well as anti-oxidation function.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.228 | **Acknowledgements**
The author gratefully acknowledges the Marine Biotechnology Institute (MBI) that was closed on 30 June 2008, and Kirin Holdings Company, Limited (Kirin Brewery Co., Ltd.). This work was also supported by New Energy and Industrial Technology Development Organization (NEDO) of Japan.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.232 | **Carlos Vílchez 1,\*, Eduardo Forján 1, María Cuaresma 1, Francisco Bédmar 2, Inés Garbayo 1 and José M. Vega 3**
E-Mail: [email protected]
*Received: 31 January 2011; in revised form: 15 February 2011 / Accepted: 17 February 2011 / Published: 3 March 2011*
**Abstract:** Carotenoids are the most common pigments in nature and are synthesized by all photosynthetic organisms and fungi. Carotenoids are considered key molecules for life. Light capture, photosynthesis photoprotection, excess light dissipation and quenching of singlet oxygen are among key biological functions of carotenoids relevant for life on earth. Biological properties of carotenoids allow for a wide range of commercial applications. Indeed, recent interest in the carotenoids has been mainly for their nutraceutical properties. A large number of scientific studies have confirmed the benefits of carotenoids to health and their use for this purpose is growing rapidly. In addition, carotenoids have traditionally been used in food and animal feed for their color properties. Carotenoids are also known to improve consumer perception of quality; an example is the addition of carotenoids to fish feed to impart color to farmed salmon.
**Keywords:** carotenoids; microalgae; applications; nutraceuticals; health benefits
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.233 | **1. Marine Carotenoids: Biological Functions and Benefits to Human Health**
In photosynthetic organisms including plants and microalgae, carotenoids play various roles. Essentially, carotenoids may act as accessory pigments in light harvesting functions during the light phase of photosynthesis and are also able to photoprotect the photosynthetic machinery from excess light by scavenging reactive oxygen species (ROS) like singlet oxygen and other free radicals [1].
In humans, the most relevant biological functions of carotenoids are linked to their antioxidant properties, which directly emerge from their molecular structure. In recent years, the understanding of ROS-induced oxidative stress mechanisms and the search for suitable strategies to fight oxidative stress has become one the major goals of medical research efforts. A number of studies have been reported which implicate oxidative stress involvement in degenerative pathogenesis, e.g., Alzheimer and Parkinson [1,2]. In parallel, a carotenoid-enriched diet has been found to diminish the risk of suffering from degenerative diseases [2].
Moreover, far from being just a speculative hypothesis, the benefits of carotenoids (lutein, -carotene, lycopene) to human health have been shown based on the positive impacts of the antioxidant bioactivity of carotenoids in inmunoresponse modulation, in signaling transduction between cells and in anti-inflammatory response mechanisms [3–5]. These positive consequences are the result of either the direct chemical action of carotenoids on biological molecules and structures or through expression of different genes involved in antioxidant responses [2]. The main biological functions of carotenoids and benefits to health are listed in Table 1.
## *1.1. Provitamin A Activity*
One of the most important functions of carotenoids in the human body is their ability to convert into retinol (provitamin A function), a faculty that about 10% of carotenoids identified in nature
possess [6]. Vitamin A is well recognized as a factor of great importance for child health and survival, its deficiency causes disturbances in vision and various related lung, trachea and oral cavity pathologies [7]. Animals and humans cannot synthesize carotenoids *de novo*, although they are able to convert them into vitamin A. Diet is the only source for these precursors for retinol synthesis, fruits, vegetables and microalgae being the major suppliers of provitamin A active carotenoids. As a reference value, a recommended daily intake of 6 mg of carotenoids has been proposed. This value is based on the contribution of compounds with provitamin A activity, specially Ά-carotene, which has been assigned a provitamin A activity of 100% [8].
## *1.2. Carotenoids and Cancer*
In recent years, epidemiological evidence supporting a protective effect of carotenoids to the development of chronic and degenerative diseases has grown considerably. We must not forget that cancer and cardiovascular diseases are the leading causes of death in the world and that approximately 50% of all tumors are attributed to the diet [9].
From a nutritional point of view, an antioxidant can be defined as any substance present in foods that significantly reduces the adverse effects of reactive oxygen species in normal physiological conditions in humans [10]. Antioxidants, in particular carotenoids, are essential for cell health due to their protective action on cellular components against oxidative damage [2]. These activities have generated two lines of research related to the physiological functionality of carotenoids: on one hand, their activity as membrane antioxidants, therefore involved in the oxidative cell cycle [11] and, on the other hand, their involvement in control processes of cell differentiation and proliferation [12]. As an example of the first, a recent study showed that antioxidant enzymes including catalase, superoxide dismutase and peroxidase levels in plasma and liver of mice increased significantly when the animals were fed with microalgae biomass (*Haematococcus pluvialis*, *Scenedesmus platensis* or *Botryococcus braunii*), which reveals an increased antioxidant protection against free radicals [13].
It is well known that cellular proliferation is controlled by the communication established between the cells in a tissue. Cell communications reset or stimulation becomes essential if abnormal cell proliferation occurs. In that respect, it has been mentioned that carotenoids might stimulate expression of genes directly involved in regulation of cell communication processes. In more detail, carotenoids would directly act on DNA in order to regulate the production of RNA that is responsible for *gap-junctions* communications, which could successfully explain some antitumor activities of carotenoids [2,12]. Immune system cells also require intercellular communication to conduct their activity efficiently, so the previous action mechanism of carotenoids could also apply for supporting the immune system activity. As an example, high doses of Ά-carotene increase the CD4 to CD8 lymphocyte ratio, which is very low in patients suffering from HIV disease [14].
In the last decades, many laboratory and epidemiological studies have been conducted which suggest that intake of carotenoids and cancer prevalence are inversely related [4,15–17]. Among the carotenoids, lycopene has been one of the most extensively studied [4,18–20] probably due to the greater anticancer capacity shown with respect to other carotenoids [21]. Within the wide frame of research carried out by Giovannucci *et al.* [4], lycopene intake and prostate cancer were found to be inversely related. The inverse relation was based on *in vivo* and *in vitro* studies on the effect of lycopene in tumor cell lines that showed tumor cells growth inhibition by the action of lycopene [19,20]. Although the functional meaning of the lycopene distribution in the organism has not been fully elucidated, it is particularly interesting that this carotenoid predominates in testes and adrenal glands, with an abundance of about 60 to 80% of the total carotenoids [22]. It has also been inferred that astaxanthin could be effective against benign prostatic hyperplasia and against prostatic cancer through inhibition of the enzyme 5-a-reductase which is involved in abnormal prostate growth [2,23].
Antitumoral activity of carotenoids toward other type of cancer has also been observed. In particular, -carotene, astaxanthin, cantaxanthin and zeaxanthin have been shown to promote reduction in size and number of liver neoplasias *in vivo* [21,24]. Other studies have shown that inclusion of carotenoids in the diet and reduced risk of colon cancer might be directly related [25–27]. The antitumoral effect of b-carotene has also been associated to the nutritional situation of the studied population. As an example, -carotene implementation studies carried out at Linxian (China) in population that suffered from a diet deficient in vitamins and mineral salts, led to reduced incidence of total mortality from gastrointestinal cancer [28]. Interestingly, in population not affected by nutritional deficiency but included in cancer risk groups (e.g., smokers or asbestos-exposed groups) it has been shown that -carotene supplements even might increase cancer risk, probably due to generation of metabolites that increase the cell oxidative state and led to reduced control of cell differentiation and cell proliferation processes [29–32].
Although carotenoids including zeaxanthin, criptoxanthin and lutein antitumoral activities have still been scarcely studied, the strategy of using carotenoids as chemoprotecting agents is not yet endorsed by clinical trials. More on the contrary, in spite of using -carotene as pure drug for producing an intense punctual effect after any dosage intake, the derived positive action of carotenoids should be produced through continuous intake of usual quantities. This idea is in line with current dietary recommendations that suggest consumption of five fruit and vegetables portions a day, which will provide water, vitamins, fiber and phytochemical compounds including carotenoids in sufficient quantities to meet our body needs [10,16].
## *1.3. Carotenoids and Cardiovascular Diseases*
Cardiovascular diseases are the leading cause of death in developed countries, and have become the main health problem also in developing countries [33]. These include acute myocardial infarction and disorder of high morbidity and mortality [34]. Oxidative stress and inflammation are the main factors contributing to the pathophysiology of these disorders [35,36]. In particular, the oxidative stress induced by ROS can cause low density lipoproteins oxidation (LDL), an aspect that plays a key role in the pathogenesis of atherosclerosis [37,38].
Another major feature of carotenoids is protection of LDL against oxidation [39,40], which confers carotenoids antiatherogenic properties [2,36,41]. In addition, carotenoids have been shown to inhibit *in vivo* lipid peroxidation processes [42], by which the presence of carotenoids in cell membranes is essential to act as stabilizing elements of these structures [8,43]. In this sense, the antioxidant activity of some carotenoids during radical peroxide-induced cholesterol oxidation was investigated by Palozza *et al.* [44], showing that carotenoids exerted a significant antioxidant activity, in the decreasing activity order indicated: astaxanthin, cantaxanthin, lutein and Ά–carotene. Several authors have published that daily dietary -carotene supplementation in mammals led to decreased plasma levels of total lipids, cholesterol and triglycerides [45,46].
Numerous epidemiological studies suggest that diets rich in carotenoids could protect the human body from certain cardiovascular diseases due to the involvement of oxidizing substances and oxidative stress in the development and clinical expression of coronary heart disease [47]. In fact, high lycopene levels in plasma and tissues have been inversely linked to coronary heart disease [48], myocardial infarction [49] and risk to suffer from arteriosclerosis [50]. Low lutein levels in plasma have also been associated with an increased tendency to suffer from myocardial infarction [51], while a high intake of lutein has been inversely related with the risk of stroke [52].
Likewise, low ΅-carotene levels in serum have been shown to inversely correlate prevalence of coronary artery disease and formation of arterial plaque, by which ΅- carotene has been proposed as a potential marker for human atherosclerosis. In addition, carotenoids displaying high levels of provitamin A activity, including ΅- carotene, Ά-carotene and Ά-cryptoxanthin, have been associated with reduced risk of angina pectoris disease [53,54]. Other epidemiological studies have also found low levels of oxygenated carotenoids (namely xanthophylls: lutein, zeaxanthin, lycopene, Ά-cryptoxanthin, Ά-carotene and ΅-carotene) in plasma of patients with acute and chronic coronary syndromes [55,56]. Particularly, in the recent study by [38], high levels of Ά-cryptoxanthin and lutein in plasma have been shown to decrease risk for suffering from myocardial infarction, but no statistically significant associations with other carotenoids were found.
## *1.4. Carotenoids and Eye Health*
Many research studies showed that lutein and zeaxanthin are the main responsible pigments for both the yellowing and the maintenance of normal visual function of the human eye macula [57,58], while other major carotenoids in serum ( -carotene, Ά-carotene, lycopene and Ά-cryptoxanthin), are absent or are found in trace amounts in the human macula [59]. In the eye macula, lutein and zeaxanthin absorb blue light and also attenuate pernicious photooxidative effects caused by the excess blue light, while reducing eye chromatic aberration. Due to their antioxidant properties, carotenoids protect the eye macula from adverse photochemical reactions [60]. In people over the age of 64, visual sensitivity directly depends on lutein and zeaxanthin concentrations in retina [61].
Major prevalency of cataracts has also been linked to people with low levels of lutein and zeaxanthin [62]. Also macular degeneration, the main cause of irreversible loss of vision in people above 65 years in industrialized countries, has been associated with very low levels of lutein and zeaxanthin [63,64].
The spectra of lutein and zeaxanthin show a wide absorption band with a peak at 450 nm, which is thought to be involved in absorbing excess blue light before it comes to photoreceptors, therefore preventing the eye macula from being damaged by blue light [65]. Moreover, due to lutein's and zeaxanthin's biophysical and biochemical properties for ROS scavenging, these carotenoids might also preserve the membrane structure in the eye photoreceptors from lipid peroxidation processes [66], in contrast to non-polar carotenoids as lycopene and Ά-carotene [67]. Concentration of lutein and zeaxanthin in the retina can be increased on diet bases (spinach and maize) and on supplements of both pigments [60,68].
## *1.5. Other Physiological Functions of Carotenoids*
Carotenoids provide skin photoprotection against UV light [69–71]. Due to their scavenging action on ROS, carotenoids also possess anti-inflammatory properties [72–74]. In this sense, it has been recently described that astaxanthin raises antiinflammatory effects while preserving essential lipids and proteins of human lymphocytes [74]. Astaxanthin would act by inducing superoxide dismutase and catalase enzyme activities [74]. Other studies have shown astaxanthin to protect from CCl4-induced hepatic damage by inhibiting lipid peroxidation, stimulating the cellular antioxidant system and modulating the inflammatory process [73]. Table 1 resumes biological functions, benefits to health and applications of the main carotenoids, including their role in prevention of cataracts [75,76], macular degeneration [77–80], retinitis [81–83] and gastric infection [84].
Carotenoids have been used as preservatives in cosmetics and, combined with other antioxidants or algal bioactive substances, also in creams and lotions for sun protection [85]. The beneficial effect of carotenoids has also been shown in patients with psoriasis, skin inflammatory pathology. Lima and Kimball [86] found low levels of carotenoids in the skin correlate well with psoriasis prevalence.
Finally, it is interesting to note that, in recent years, carotenoids are being considered as important protective molecules in gastric disorders. It has been published that a high intake of carotenoids prevents the development of disorders caused by *Helicobacter pylori* [84,87,88], a Gram negative bacteria genus that colonizes the gastric mucosa of at least half of the human population [89].
**Table 1.** Biological functions, benefits to health and applications of the main carotenoids.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.234 | **2. Marine Carotenoids: Applications**
Carotenoids have been traditionally used in food and animal feed due to their color properties. The natural carotenoids are used to reinforce fish color, which increases consumers' perception of quality. An example is the addition of carotenoids to fish feed to impart color to farmed salmon. The nutraceutical properties of carotenoids also attracted attention of the food industry. Large numbers of scientific studies have confirmed the benefits of carotenoids to health and use for this purpose is growing rapidly. Besides, carotenoids have been proposed as added-value compounds that could contribute to make microalgal biofuel production economically feasible [90,91].
Among all existing natural carotenoids, five can be considered to be the most relevant ones in economical terms (Table 2). The main applications of carotenoids are currently as dietary supplements, fortified foods, food color, animal feed and pharmaceuticals and cosmetics.
**Table 2.** Main commercial carotenoids and origin.
Ά-carotene, the most widely known of the carotenoids, is known to be a vitamin A precursor, likely several other carotenoids. Carotenoids have antioxidant properties and a large number of studies have confirmed their benefits to health. In particular, carotenoids are thought to reduce the risk of degenerative diseases and cancer especially in elderly people, as explained above [29,32,41].
The health industry uses carotenoids in over-the-counter (OTC) dietary supplements and fortified foods. This is one of the fastest growing segments of the industry but is still relatively small compared to the color segment. The pharmaceutical and cosmetics industries also use carotenoids mainly for their coloring properties, though their use by the pharmaceutical and cosmetics companies is growing rapidly due to their nutraceutical properties. An example of a new product from this segment is a 'beauty pill' containing the carotenoid lycopene. This product belongs to a new market segment known as 'cosmeceuticals', which aims to combine cosmetics and nutraceutical food ingredients to create products to improve skin and hair.
Chemically synthesized nature identical carotenoids dominate the market but naturally extracted carotenoids are growing in popularity due to increasing demand for natural products from consumers. Natural carotenoids can be extracted from plant material such as tomatoes, algae and fungi. Individual carotenoids are available in a variety of forms. The most common forms are cold water soluble powder, oil emulsion and beadlets. Concentrations range from 0.2 to 100%. The most common concentration is 10%. Blends or mixed carotenoids are also available containing two or more different carotenoids. Like the individual carotenoids, blends are available in a variety of forms including, water dispersible powder, oil suspension and beadlet forms. The concentration of blends ranges from 1 to 30%, with the most common concentration being 10% [91–93].
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.235 | *2.1. Dietary Supplements and Food Color*
Carotenoids are widely used as color enhancers in natural foods including egg yolk, chicken meat or fish [90]. However, among more than 400 known carotenoids just few of them have been commercially used, including -carotene, lycopene, asthaxanthin and lutein [91]. One of the main advantages in the use of microalgae as a carotenoid carrier in the food industry is that many other antioxidant compounds present in the microalgal biomass have positive impact on human health, sometimes acting with carotenoids synergistically [92].
In addition, if carotenoids are disposed within the microalgal matrix (carotenoid enriched dry biomass) also a number of minerals whose presence is inherent to the algal biomass are provided in the formula. These mineral have positive effects to human health, especially in enhancing anabolic activities. Carotenoids have also been used as preservatives in cosmetics and solar protection products [85].
Because of the content of carotenoids, the commercial value of microalgae increased and their use extended widely into many applications of the food market. That includes the use of *Arthrospira*, *Chlorella*, *Dunaliella, Spirulina* and *Aphanizomenon* as functional foods which can be found in the market in the form of pills, tablets and capsules. These microalgae have also been integrated in nutritional formula of pasta, snacks, sweets, drinks and bubble gum [91,93].
Microalgae are also used in fish color quality improvement in aquaculture. Salmonids are supplied with astaxanthin-enriched microalgae species, in particular *Haematococcus pluvialis* [2].
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.236 | *2.2. Environmental Applications: Carotenoids in Biorefining*
Microalgae have gained interest as promising feedstocks for biofuels. The productivity of these photosynthetic microorganisms in converting carbon dioxide into carbon-rich lipids greatly exceeds that of agricultural oleaginous crops, without competing for arable land. However, large scale production of lipid-enriched algal biomass is not yet economically feasible and still requires major efforts in developing suitable technology which allows for reducing biomass production costs at large scale by at least an order of magnitude. Recent advances in systems biology, genetic engineering and methods to profit from the fractions of the biomass residue open new scenarios to make biofuel production from microalgae economically suitable within a period of about 15 years. Production of biodiesel and other bio-products from microalgae can be more cost-effective and profitable if combined with processes such as wastewater and flue gas treatments [94,95]. Carotenoids are, indeed, one of the main bio-products whose production is required to make biofuel production economically feasible. The paradox, therefore, is that production of highadded value compounds as carotenoids should so far be the only way to approach economical production of a low value energy source as biofuel from microalgae.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.237 | *2.3. Commercial Value for Carotenoids*
In recent years, production of carotenoids has become one of the most successful activities in microalgal biotechnology. The demand for carotenoids obtained from natural sources is increasing. This has promoted major efforts to improve carotenoid production from biological sources instead of chemical synthesis [96]. According to the report published by Business Communications in March, 2008, the global market for all commercial carotenoids accounted for 766 million dollars, with expectations of rising to 919 million dollars in 2015. In particular, beta-carotene market volume in 2007 was 247 million dollars, with expectations of reaching 285 million dollars in 2015. Besides lycopene and -carotene, xanthophylls lutein, astaxanthin and cantaxanthin appear as the most demanded and valuable carotenoids. Astaxanthin market volume in aquaculture in 2009 was 260 million dollars and about 2500 \$ kgƺ1. In addition, lutein market volume in 2010 accounted for about 190 million dollars, the carotenoid experiencing the most rapid growth in sales [97]. Therefore, carotenoid-containing microalgae find many applications in a wide range of commercial activities, the reason for which carotenoid-enriched microalgae production is steeply becoming an attractive business (Table 3).
## **Acknowledgements**
This work has been supported by grant AGR-4337 (Proyecto de Excelencia, Junta de Andalucía) and grant Bioándalus (Junta de Andalucía, Estrategia de Impulso a la Biotecnología).
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.239 | **New and Rare Carotenoids Isolated from Marine Bacteria and Their Antioxidant Activities**
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.240 | **Kazutoshi Shindo 1,\* and Norihiko Misawa 2**
1 Department of Food and Nutrition, Japan Women's University, 2-8-1 Mejirodai, Bunkyo-ku,
Tokyo 112-8681, Japan
2 Research Institute for Bioresources and Biotechnology, Ishikawa Prefectural University,
1-308 Suematsu, Nonoichi-shi, Ishikawa 921-8836, Japan; E-Mail: [email protected]
**\*** Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel./Fax: +81-35-981-3433.
*Received: 10 February 2014; in revised form: 3 March 2014 / Accepted: 4 March 2014 / Published: 19 March 2014*
**Abstract:** Marine bacteria have not been examined as extensively as land bacteria. We screened carotenoids from orange or red pigmentsproducing marine bacteria belonging to rare or novel species. The new acyclic carotenoids with a C30 aglycone, diapolycopenedioc acid xylosylesters A–C and methyl 5-glucosyl-5,6-dihydro-apo-4,4<sup>ȝ</sup>lycopenoate, were isolated from the novel Gram-negative bacterium *Rubritalea squalenifaciens*, which belongs to phylum Verrucomicrobia, as well as the low-GC Gram-positive bacterium *Planococcus maritimus* strain iso-3 belonging to the class Bacilli, phylum Firmicutes, respectively. The rare monocyclic C40 carotenoids, (3*R*)-saproxanthin and (3*<sup>R</sup>*,2<sup>ȝ</sup>*S*)-myxol, were isolated from novel species of Gram-negative bacteria belonging to the family Flavobacteriaceae, phylum Bacteroidetes*.* In this review, we report the structures and antioxidant activities of these carotenoids, and consider relationships between bacterial phyla and carotenoid structures.
**Keywords:** diapolycopenedioc acid xylosylesters A–C; methyl 5- glucosyl-5,6-dihydro-apo-4,4ȝ-lycopenoate; (3*R*)-saproxanthin; (3*<sup>R</sup>*,2<sup>ȝ</sup>*S*)- myxol; antioxidant activity
## **1. Introduction**
Some species of bacteria, yeast, and fungi, as well as algae and higher plants, synthesize a large number of carotenoids with different molecular structures, and more than 750 carotenoids with different structures have been isolated from natural sources [1]. Many beneficial pharmaceutical effects of carotenoids have recently been reported*.* Therefore, evaluating the pharmaceutical potentials of various carotenoids may represent an interesting field in medical research. However, the number of carotenoid species that have been examined for this purpose has been limited, and has included C40 carotenoids possessing skeletons composed of 40 carbon atoms, such as dicyclic carotenoids, e.g., Ά-carotene, ΅-carotene, Άcryptoxanthin, zeaxanthin, lutein, canthaxanthin, astaxanthin, and fucoxanthin, and the acyclic carotenoid lycopene [2–8]. Difficulties have been associated with identifying natural sources to supply sufficient amounts of new or rare carotenoids, with the exception of carotenoids that can be isolated from a species of higher plants or algae or chemically synthesized. It has therefore been desirable to find cultivable bacteria that produce new or rare carotenoids, since they can easily be reproduced.
Marine bacteria have not been examined as extensively as land bacteria. Thus, the Marine Biotechnology Institute Co., Ltd. (MBI, Kamaishi, Japan) was established in December, 1988, and continued to isolate novel or rare marine bacteria until March, 2008, the number of which reached more than ten thousand [9–12]. Many bacteria have been shown to produce dicyclic or monocyclic
C40 carotenoids, in addition to some acyclic C30 carotenoids with a 30 carbon skeleton [1,13]. The
MBI isolated new or rare dicyclic C40 carotenoids with the Ά-carotene ( Ά,Ά-carotene) skeleton
from Gram-negative marine bacteria belonging to the class ΅-Proteobacteria, phylum
Proteobacteria, e.g., astaxanthin glucoside from *Paracoccus* sp. strain N81106 (reclassified from
*Agrobacterium aurantiacum*) [14], 2-hydroxyastaxanthin from *Brevundimonas* sp. strain SD212 [15], and 4-ketonostoxanthin 3ȝ-sulfate from *Erythrobacter* sp. strain. PC6 (reclassified from *Flavobacterium* sp. PC-6; MBIC02351) [16]. These marine bacteria were also able to produce astaxanthin [17]. The carotenoid biosynthesis gene clusters of these marine bacteria have been elucidated in detail [17–19].
The generation of free radicals has been suggested to play a major role in the progression of a wide range of pathological disturbances, including myocardial and cerebral ischemia [20], atherosclerosis [21], renal failure [22], inflammation [23], and rheumatoid arthritis [24]. The subsequent peroxidative disintegration of cells and organelle membranes has also been implicated in various pathological processes [25]. Carotenoid pigments, which have been shown to possess strong antioxidant activities, have been attracting increasing attention due to their beneficial effects on human health, e.g., their potential to prevent diseases such as cancer and cardiovascular diseases [26].
We have attempted to identify novel or rare types of carotenoids from yellow or red
pigment-producing marine bacteria that were classified to belong to rare or novel species by 16S rRNA analyses since 2002. The results of this screening led to the isolation of diapolycopenedioc
acids xylosylesters A–C (new carotenoids) from *Rubritalea squalenifaciens* [27,28], methyl
5-glucosyl-5,6-dihydro-apo-4,4ȝ-lycopenoate (a new carotenoid) from *Planococcus maritimus* [29], and
(3*R*)-saproxanthin and (3*<sup>R</sup>*,2<sup>ȝ</sup>*S*)-myxol (rare carotenoids) from a novel species belonging to the family
Flavobacteriaceae [30]. In this review, we report the structures and antioxidant activities of these carotenoids, and consider relationships between bacterial phyla and carotenoid structures.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.241 | **2. Results**
*2.1. Diapolycopenedioc Acid Xylosylesters A–C from Rubritalea Squalenifaciens [27,28]*
A yellow pigment-producing bacterium (strain HOact23T) that was found to produce squalene was isolated from the homogenate of the marine sponge *Halicondria okadai*, which had been collected
from the Miura peninsula (Kanagawa, Japan), and was subsequently classified as a novel species in the genus *Rubritalea*, belonging to phylum Verrucomicrobia, based on 16S rRNA gene sequence data. The name proposed for the new taxon was *Rubritalea squalenifaciens* [31], with the type strain
HOact23T (=MBIC08254T = DSM 18772T).
*R. squalenifaciens* was cultured in 100 mL of medium (1.0% starch, 0.4% yeast extract, and
0.2% peptone in seawater) in a 500 mL Erlenmeyer flask at 30 °C on a rotary shaker at 120 rpm for 2 days, and the carotenoids produced were purified from the cells using chromatographic methods (EtOAc/H2O partition ė silica gel column chromatography CH2Cl2–MeOH (20:1) ė preparative silica gel HPLC CH2Cl2– MeOH (15:1) ė preparative ODS HPLC (MeOH)). Three carotenoids were purified from cells in the 42-liter culture (diapolycopenedioc acids xylosylesters A (**1**) 10.2 mg, B (**2**) 3.0 mg, and C (**3**) 2.2 mg, respectively). The structures of compounds **<sup>1</sup>**–**<sup>3</sup>** were determined by HRESI-MS and spectroscopic (UV-Vis, NMR (1D and 2D investigations on 1H and 13C nuclei), and [΅]D) analyses as shown in Figure 1. Compounds **<sup>1</sup>**–**3** were all new carotenoids.
Compounds **<sup>1</sup>**–**3** possessed diapolycopenedioc acid (C30 carotenoid) [32,33] as their aglycone. Diapolycopenedioic acid glucosyl ester and diapolycopenedioic acid diglucosyl were previously shown to be carotenoids that possessed diapolycopenedioc acid as the aglycone [32]. Compounds **<sup>1</sup>**–**3** were the first carotenoids to include 2-acyl-D-xylose in their structures.
The antioxidant activity of compound **1** was evaluated using 1O2 suppression activity. Its IC50 was 5.1 ΐM (the IC50 values of astaxanthin and Ά-carotene were 8.9 ΐM and >100 ΐM, respectively).
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.242 | **Figure 1.** The structures of diapolycopenedioc acids A (**1**), B (**2**) and C (**3**).
*2.2. Methyl 5-Glucosyl-5,6-Dihydro-Apo-4,4ȝ-Lycopenoate from Planococcus Maritimus [29]*
A yellow pigment-producing bacterium (strain iso-3), which was found to be solvent-tolerant, was isolated as an orange-pigmented colony from the microbial analysis of a sample derived from an intertidal sediment from the Clyde estuary, UK. The 16S rRNA gene sequence of strain iso-3 was the most similar to that of type strain *Planococcus maritimus* (99.5 as a similarity score, and 96.4 as an s\_ab score, from the Sequence match analysis of RDP), which belongs to the class Bacilli, phylum Firmicutes, and was identified as *Planococcus maritimus* strain iso-3.
Strain iso-3 was cultured in 100 mL of medium (Marine Broth 2216, Difco, Sparks, MD, USA) in a
500 mL Erlenmeyer flask at 30 °C on a rotary shaker at 120 rpm for 1 day, and the carotenoid produced was purified from the alkaline-digested cells using chromatographic methods (EtOAc/H2O partition ė silica gel column chromatography (CH2Cl2–MeOH (10:1) ė preparative silica gel
HPLC CH2Cl2–MeOH (10:1) ė preparative ODS HPLC (96% MeOH)). A total of 2.5 mg of pure methyl 5-glucosyl-5,6-dihydro-apo-4,4ȝ-lycopenoate (**4**) was obtained from the cells in the 18-liter culture, and the structure of compound **<sup>4</sup>** was determined by HRESI-MS and spectroscopic (UV-Vis, NMR (1D and 2D investigations on 1H and 13C nuclei), and [΅]D) analyses, as shown in Figure 2.
Compound **4** was a new carotenoid. Compound **<sup>4</sup>** possessed 5,6-dihydro-5-hydroxy-
apo-4,
4ȝ-lycopene-4ȝ-oic acid (C30 carotenoid) as its aglycone. Although 4,4ȝ-diapocarotene-4-oic acid [32] was previously reported to be a related C30 carotenoid aglycone, 5,6- dihydro and 5-hydroxy functions in the aglycone of compound **4** were demonstrated for the first time. The antioxidant activity
of compound **4** was evaluated using 1O2 suppression activity, and its IC50 value was 5.1 ΐM. We previously described the isolated carotenoid as methyl glucosyl-3,4-dihydroapo-8<sup>ȝ</sup>-lycopenoate [29], but confirmed its structure as methyl 5-glucosyl-5,6- dihydro-apo-4,4ȝ-lycopenoate, as shown in this review. Corrigenda is currently being prepared for the previous study.
> **Figure 2.** The structure of methyl 5-glucosyl-5,6-dihydro-apo-4,4<sup>ȝ</sup>lycopenoate (**4**).
*2.3. (3R)-Saproxanthin and (3R,2ȝS)-Myxol [30]*
Strain 04OKA-13-27 (MBIC08261) was isolated from the dense mats of filamentous algae from within the territory of damselfish (*Stegastes nigricans*). Strain YM6-073 (MBIC06409) was isolated from a sediment sample collected 0.1 m below the surface of the sea by cultivating for 30 days on an HSV medium. The two marine bacteria, which had been collected off the coast of Okinawa Prefecture, were classified on the basis of this 16S rRNA gene sequences. A similarity search in the databases
of the DNA Data Bank of Japan (DDBJ) and RNA Database Project II (RDPII) showed the 16S rRNA gene sequences of the both strains (04OKA-13-27 and YM6-073) to be 96.5% (1408 bp/1459 bp) similar to *Stanierella latercula* ATCC 23177T, 95.5% (1324 bp/1386 bp) similar to *Gaetbulimicrobium brevivitae* strain SMK-19T, and 94.2% (1306 bp/1386 bp) similar to *Robiginitalea biformata* strain HTCC2501T. The phylogenetic relationship between these strains was deduced with already known species in the family Flavobacteriaceae. The result obtained revealed that the two bacterial strains should be classified as novel species of the family Flavobacteriaceae.
Both 04OKA-13-27 and YM6-073 were cultured in 100 mL of medium (Marine Broth 2216, Difco) in a 500 mL Sakaguchi flask at 30 °C on a rotary shaker at 100 rpm for 1 day, and the carotenoids produced were each purified from the cells using chromatographic methods (EtOAc/H2O partition ė silica gel column chromatography hexane–ethyl acetate (2:1) ė preparative silica gel high performance thin layer chromatography (HPTLC; Merck, Darmstadt, Germany)
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.243 | CH2Cl2–MeOH
(10:1) ė preparative ODS HPLC (MeOH)). A total of 0.3 mg (04OKA-13-27) and 0.5 mg (YM6-073) of pure carotenoids were obtained from the cells of each 2 liter culture, and the carotenoids were identified as (3*R*)-saproxanthin (04OKA-13-27) (**5**) and (3*R*,2<sup>ȝ</sup>*S*)-myxol (YM6-073) (**6**) by MS, 1H-NMR, and CD analyses, respectively (Figure 3).
The antioxidative activities of compounds **5** and **6** were examined using rat brain homogenate model. Compounds **5** and **6** showed potent antioxidant activities (IC50 2.1 ΐM (**5**) and 6.2 ΐM (**6**))
(IC50 10.9 ΐM (Ά-carotene)).
> **Figure 3.** The structures of (3*R*)-saproxanthin (**5**) and (3*R*,2<sup>ȝ</sup>*S*)-myxol (**6**).
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.244 | **3. Discussion**
The MBI has isolated approximately 1000 pigment-producing marine bacteria. We selected
10 strains, which were identified as rare or novel species by 16S rRNA, including strain HOact23T (*Rubritalea squalenifaciens* sp. nov., phylum Verrucomicrobia), strain iso-3 (*Planococcus maritimus*, the class Bacilli, phylum Firmicutes), strain 04OKA-13- 27 (novel species of the family Flavobacteriaceae), and strain YM6-073 (novel specie of the family Flavobacteriaceae) from these isolated bacteria.
We found two-type new C30 carotenoids diapolyconedioc acid xylosylesters (compound **<sup>1</sup>**–**<sup>3</sup>**) from HOact23T and methyl 5-glucosyl-5,6-dihydro-apo-4,4<sup>ȝ</sup>lycopenoate (compound **4**) from iso-3 through the isolation and structural analyses of carotenoids produced by these strains. Acyclic C30 carotenoids were previously shown to be contained in land bacteria including *Staphylococcus aureus*, belonging to the class Bacilli, and the methanotrophs *Methylobacterium rhodium* (formerly *Pseudomonas rhodos*), belonging to the class ΅*-*Proteobacteria, and *Methylomonas* sp., belonging to the class
·-Proteobacteria [17,34]. Thus, acyclic C30 carotenoids are likely to widely exist in domain bacteria (prokaryotes), *i*.*<sup>e</sup>*., they are present not only in some low-GC Grampositive bacteria, but also in some phyla in Gram-negative bacteria. The strong singlet-oxygen-quenching activities of our C30 carotenoids also indicated that such C30 carotenoids are promising as functional carotenoids, although these *in vivo* functional analyses have not yet been conducted.
We isolated two rare monocyclic C40 carotenoids with one 3-hydroxy-Ά-ring ((3*R*)- saproxanthin (compound **5**) from 04OKA-13-27 and (3*R*,2<sup>ȝ</sup>*S*)-myxol (compound **6**) from YM6-073), which belong to the family Flavobacteriaceae, phylum Bacteroidetes. (3*R*)-Saproxanthin has only previously
been detected from *Saprospira grandis*, which belongs to the family Saprospiracea, phylum Bacteroidetes [35]. Hence, marine bacterial strain 04OKA-13-27 was the second species to produce saproxanthin. (3*<sup>R</sup>*,2<sup>ȝ</sup>*S*)-Myxol has only previously been detected in marine bacterial strain
P99-3 (MBIC03313), belonging to the family Flavobacteriaceae [15], and in the cyanobacterium *Anabaena variabilis* ATCC 29413, phylum Cyanobacteria [36]. Therefore, marine bacterial strain YM6-073 was the third species to produce myxol. Myxoxanthophyll (myxol 2ȝ-fucoside), which is widely distributed in phylum Cyanobacteria, contains myxol as its aglycone. These findings indicated that such monocyclic C40 carotenoids with one 3-hydroxy-Ά-ring exist in phylum Bacteroidetes as well as phylum Cyanobacteria.
The carotenoids produced by the six other strains isolated were all zeaxanthin, which is a common carotenoid in domain bacteria. Our study may be effective for identifying rare and new carotenoids based on its ratio (4/10). In addition, all the rare and new carotenoids (**1**–**<sup>6</sup>**) isolated possessed potent antioxidant activities.
## **4. Conclusions**
Marine bacteria are likely to produce carotenoids to protect themselves from activated oxygen produced by sunlight (mainly 1O2); therefore, their potent antioxidant activities were expected
and reasonable. Therefore, the techniques performed in our study effectively identified new
antioxidant carotenoids.
## **Author Contributions**
Kazutoshi Shindo performed the experiments and wrote the text; Norihiko Misawa supervised the project and corrected the manuscript.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.245 | **Conflicts of Interest**
The authors declare no conflict of interest.
## **References**
by astaxanthin. *J. Atheoscler. Thomb.* **2000**, *7*, 216–222.
4. Krinsky, N.I.; Landrum, J.T.; Bone, R.A. Biological mechanism of the protective role of lutein
and zeaxanthin in the eye. *Annu. Rev. Nutr.* **2003**, *23*, 171–201.
Mander, L., Liu, H.-W., Eds.; Elsevier: Oxford, UK, 2010; Volume 1, pp. 733–753.
carotenoid levels in foods and the likely systemic effects. *J. Sci. Food Agric.* **<sup>2000</sup>**, *80*, 880–912.
glycol-C30-carotenoic acids produced by a new marine bacterium *Rubritalea squalenifaciens*.
*J. Antibiot.* **2008**, *61*, 185–191.
29. Shindo, K.; Endo, M.; Miyake, Y.; Wakasugi, K.; Morritt, D.; Bramley, M.P.; Fraser, D.P.; Kasai, H.; Misawa, N. Methyl glucosyl-3,4-dehydro-apo-8ȝ-lycopenoate, a novel antioxidative glycol-C30-carotenoic acid produced by a marine bacterium
*Planococcus maritimus*. *J. Antibiot.* **2008**, *61*, 729–735.
activity. *J. Oleo Sci*. **2013**, *62*, 955–960.
*Mar. Drugs* **2014,** *12*, 1690–1698
© 2014 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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"author": "",
"title": "Marine Carotenoids",
"publisher": "MDPI - Multidisciplinary Digital Publishing Institute",
"isbn": "9783039431908",
"section_idx": 245
} |
ffe4a610-aead-4300-8fef-a70aeecd3fb7.246 | **Carotenoids of Sea Angels** *Clione limacina* **and** *Paedoclione doliiformis* **from the Perspective of the Food Chain**
| doab | 2025-04-07T04:13:04.402787 | 1-5-2021 17:07 | {
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.247 | **Takashi Maoka 1,\*, Takashi Kuwahara 2 and Masanao Narita 3**
1 Research Institute for Production Development, Shimogamo-Morimoto-cho 15, Sakyo-ku,
Kyoto 606-0805, Japan
- E-Mail: [email protected]
| doab | 2025-04-07T04:13:04.402819 | 1-5-2021 17:07 | {
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.248 | *Received: 16 January 2014; in revised form: 19 February 2014 / Accepted: 3 March 2014 / Published: 10 March 2014*
**Abstract:** Sea angels, *Clione limacina* and *Paedoclione doliiformis*, are small, floating sea slugs belonging to Gastropoda, and their gonads are a bright orange-red color. Sea angels feed exclusively on a small herbivorous sea snail, *Limacina helicina*. Carotenoids in
*C. limacina*, *P. doliiformis*, and *L. helicina* were investigated for comparative biochemical points of view. Ά-Carotene, zeaxanthin, and diatoxanthin were found to be major carotenoids in *L. helicina*. *L. helicina* accumulated dietary algal carotenoids without modification. On the other hand, ketocarotenoids, such as pectenolone, 7,8-didehydroastaxanthin, and adonixanthin were identified as major carotenoids in the sea angels *C. limacina* and *P. doliiformis*. Sea angels oxidatively metabolize dietary carotenoids and accumulate them in their gonads. Carotenoids in the gonads of sea angels might protect against oxidative stress and enhance reproduction.
**Keywords:** carotenoids; sea angels; food chain; metabolism
## **1. Introduction**
*Clione limacina* is a small, floating sea slug (0.5~3 cm body length) belonging to the family Clionidae, which is a group of pelagic marine gastropods. *Paedoclione doliiformis* is a very small, floating sea slug (<0.5 cm body length) that also belongs to the family Clionidae. Their shells are lost during development and their body is gelatinous and transparent. On the other hand, their gonads and viscera are a bright orange-red color. They float by flapping their "wings". Their floating styles resemble angels and so they are called "sea angels" [1]. From spring to autumn, sea angels live at a depth of 200 m in the Sea of Okhotsk. In winter, they migrate to the coast of north Hokkaido with drift ice. The sea angels, *C. limacina* and *P. doliiformis*, are carnivorous and feed exclusively on *Limacina helicina*, which is a small, swimming predatory sea snail belonging to the family Limacinidae (Gastropoda) which feed on micro algae such as diatoms and dinoflagellates [2]. Chum salmon, *Oncorhynchus keta*, is one of the major predators of sea angels in the Okhotsk Sea of north Hokkaido [3,4].
Marine animals, especially marine invertebrates, contain various carotenoids, showing structural diversity [5–8]. New carotenoids are still being discovered in marine animals [9]. In general, animals do not synthesize carotenoids *de novo*, and so those found in animals are either directly accumulated from food or partly modified through metabolic reactions [6–8]. The major metabolic conversions of carotenoids found in marine animals are oxidation, reduction, the translation of double bonds, oxidative cleavage of double bonds, and cleavage of epoxy bonds. Therefore, structural diversity is found in carotenoids of marine animals [6–8].
We have studied carotenoids in several marine animals from chemical and comparative biochemical points of view [8–10]. We have been interested in the orange-red pigments, which were assumed to be carotenoids, of sea angels. Thus, we studied the carotenoids of the sea angels *C. limacina* and
*P. doliiformis*. Furthermore, carotenoids in the small snail *L. helicina* and chum salmon *O. keta* were studied from the perspective of the food chain (Figure 1). In the present paper, we describe the carotenoids of these marine animals from the viewpoints of comparative biochemistry and the food chain.
**Figure 1.** Food chains from phytoplankton to salmon via sea angels in the Okhotsk Sea of north Hokkaido.
## **2. Results**
Structural formulae of carotenoids identified from the sea angels *C. limacina* and *P. doliiformis* and the small herbivorous sea snail *L. helicina* are shown in Figure 2.
**Figure 2.** Structure of carotenoids found in *C. limacina*, *P. doliiformis*, and *L. helicina*.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.249 | *2.1. Carotenoids of L. helicina*
The carotenoid content and composition of the small herbivorous sea snail *L. helicina* are shown in Table 1. The total carotenoid content of *L. helicina* was 21.0 ΐg/g wet weight. Ά-Carotene (32.2%), zeaxanthin (24.2%), diatoxanthin (11.1%), and Ά-
cryptoxanthin (10.4%) were found to be major carotenoids. Characteristic algal carotenoids, fucoxanthin (5.2%) and diadinoxanthin (2.4%), were also found.
## *2.2. Carotenoids of C. limacinea*
The carotenoid content and composition of the sea angel *C. limacina* are shown in Table 1. The total carotenoid content of *C. limacina* was 47.0 ΐg/g wet weight. Fifteen carotenoids were identified. Ά-Carotene (27.6%), Ά-cryptoxanthin (13.5%), and echinenone (9.2%) were found to be major components. Monoacetylenic carotenoids, such as diatoxanthin, 7,8-didehydroastaxanthin, pectenolone, pectenol A, pectenol B, and 4ȝ-hydroxypectenolone, comprised 25.9% of the total carotenoids. Diacetylenic carotenoids, such as alloxanthin, 7,8,7<sup>ȝ</sup>,8<sup>ȝ</sup>-tetradehydroastaxanthin, 4-ketoalloxanthin, and 4ȝ-hydroxy-4-ketoalloxanthin, comprised 13.3% of the total carotenoids.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.250 | *2.3. Carotenoids of P. doliiformis*
The carotenoid content and composition of the sea angel *P. doliiformis* are shown in Table 1. *P. doliiformis* contained 159.8 ΐg/g wet weight carotenoid in the body. This was about three times higher than that of *C. limacina*. It was uncertain why *P. doliiformis* accumulated carotenoids three times higher than *C. limacina*. *P. doliiformis* showed more bright red color than *C. limacina*. This might reflect difference of species. The carotenoid composition of *P. doliiformis* was similar to that of *C. limacina*. Pectenolone (30.5%) was found to be a major component, followed by Άcryptoxanthin (12.8%) and Ά-carotene (10.2%). The monoacetylenic carotenoid diatoxanthin and its oxidative metabolites, 7,8-didehydroastaxanthin, pectinolone, pectenol A, pectenol B, and 4ȝ-hydroxypectenolone, comprised with 25.9% of the total carotenoids. Diacetylenic carotenoids, alloxanthin, 7,8,7<sup>ȝ</sup>,8<sup>ȝ</sup>tetradehydroastaxanthin, 4-ketoalloxanthin, and 4ȝ-hydroxy-4-ketoalloxanthin, comprised 13.3% of the total carotenoids.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.251 | *2.4. Carotenoids of the Chum Salmon O. keta*
The carotenoid content and composition of flesh of the chum salmon *O. keta*, collected in Monbetsu bay, are shown in Table 2. Acetylenic carotenoids, pectenolone and 7,8-didehydroastaxanthin, were found in *O. keta* as minor carotenoids, along with astaxanthin.
**Table 2.** Carotenoids content and composition of flesh of the chum salmon *O. keta* collected in Monbetsu bay.
\* Astaxanthin consisted of three optical isomers (3 *R*,3<sup>ȝ</sup>*R*),(3 *R*,3<sup>ȝ</sup>*S*), and (3*S*,3<sup>ȝ</sup>*S*) at the ratio of 82:2:16.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.252 | **3. Discussion**
It has been reported that animals do not synthesize carotenoids *de novo*, and so those found in animals are either directly accumulated from food or partly modified through metabolic reactions [6–8]. *L. helicina* is a herbivorous animal that feeds on micro algae such as diatoms and dinoflagellates [2]. Sea angels, *C. limacina* and *P.* *doliiformis* are carnivorous animals that exclusively feed on the small mollusk *L. helicina* [1]. Therefore, carotenoids produced by micro algae are made available to sea angels through *L. helicina* in the food chain. As shown in Table 1, Ά-carotene, zeaxanthin, diatoxanthin, and Ά-cryptoxanthin were found to be major carotenoids along with alloxanthin, fucoxanthin, and diadinoxanthin in *L. helicina*. They are characteristic carotenoids in diatoms and microalgae belonging to Cyanophyceae, Rhodophyceae, *etc.* [5,6]. The results indicate that *L. helicina* directly absorbs carotenoids from dietary algae and accumulates them without metabolic modification. On the other hand, keto-carotenoids such as pectenolone,
7,8-didehydroastaxanthin, 4-ketoalloxanthin, and echinenone were found to be major components in sea angels. The results clearly indicate that sea angels oxidatively metabolize ingested carotenoids from *L. helicina*. So, Ά-carotene was oxidatively converted to astaxanthin via echinenone and canthaxanthin. Ά-Cryptoxanthin was also metabolized to astaxanthin via asteroidenone and adonirubin, as shown in Figure 3. There are three optical isomers of astaxanthin in nature. However, sea angels contain only one (3*S*,3*<sup>ȝ</sup>S*) isomer. This shows that hydroxylation at C-3 and/or C-3ȝ of 4-keto and/or
4ȝ-keto Ά-end group of carotenoid in sea angels is stereo-selective to form (3*S*,3*<sup>ȝ</sup>S*)- astaxanthin. This stereo-selective hydroxylation has also been reported in other snails: *Fushinus perplexus*, *F. perplexus ferrugineus*, *F. forceps* [11,12], *Cipangopaludina chinensis laeta*, *Semisulcospia libertina* [13], and *Pomacea canaliculata* [14].
**Figure 3.** Accumulation and metabolic pathways of carotenoids that originated from phytoplankton in the sea angels *C. limacina* and *P. doliiformis*.
Sea angels also introduced a carbonyl group at C-4 and/or C-4ȝ in the 3-hydroxyand/or 3ȝ-hydroxy-Ά-end group. Namely, zeaxanthin was metabolized to astaxanthin via adonixanthin and idoxanthin. Similarly, an acetylenic carotenoid, diatoxanthin, was metabolized to 7,8-didehydroastaxanthin via pectenol, pectenolone, and 4ȝ-hydroxypectenolone. Alloxanthin was also oxidatively metabolized to 7,8,7<sup>ȝ</sup>,8<sup>ȝ</sup>-tetradehydroastaxanthin via 4ȝ-hydroxy-4-ketoalloxanthin, and 4-ketoalloxanthin, as shown in Figure 3. By introducing a carbonyl group at C-4 and/or C-4ȝ in the 3-hydroxy- and/or 3ȝ-hydroxy-Ά-end group, carotenoids changed their color from yellow to red. Therefore, the red color of the gonads of sea angels is due to the presence of keto-carotenoids such as pectenolone, 7,8- didehydroastaxanthin, and 7,8,7<sup>ȝ</sup>,8<sup>ȝ</sup>-tetradehydroastaxanthin. Epoxy carotenoids, diadinoxanthin and fucoxanthin, which are present in *L. helicina*, were not found in sea angels*.* It is suggested that sea angels cannot absorb epoxy carotenoids.
Chum salmon, *O. keta*, feeds not only on micro crustaceans but also on sea angels [3–5]. Astaxanthin, which consists of three optical isomers, was found to be a major carotenoid, along with the acetylenic carotenoids pectenolone and 7,8- didehydroastaxanthin, in *O. keta*. It is well-known that astaxanthin in crustaceans such as krill also consists of three optical isomers [6–8,15]. Therefore it is clear that astaxanthin in salmon originates from crustaceans. On the other hand, the acetylenic carotenoids pectenolone and 7,8-didehydroastaxanthin were not found in these crustaceans [6–8,15]. So, they are suggested to originate from sea angels.
It has been reported that marine animals accumulate carotenoids in their gonads, such as astaxanthin in salmon, pectenolone in scallops, and echinenone in sea urchins and that carotenoids are essential for reproduction in marine animals [8]. For example, astaxanthin supplementation in cultured salmon and red sea bream increased ovary development, fertilization, hatching, and larval growth [16]. In the case of sea urchins, supplementation with Ά-carotene, which was metabolized to echinenone, also increased reproduction and the survival of larvae [17].
As described above, sea angels converted dietary carotenoids to corresponding keto-carotenoids by introducing a carobonyl group and accumulated these ketocarotenoids in their gonads. Several investigators have reported that introducing a carobonyl group at C-4 and/or C-4ȝ of the Ά-end group of carotenoids enhanced their antioxidant effects, such as the quenching of singlet oxygen (1O2), inhibiting lipid peroxidation, and protection from photo-oxidation [18–21]. As well as astaxanthin, pectenolone, an oxidative metabolite of diatoxanthin, showed excellent antioxidative activity by inhibiting lipid peroxidation [22] and quenching singlet oxygen (1O2). Therefore, keto-carotenoids such as pectenolone may contribute to protection against oxidative stress and promote the reproduction of sea angels through antioxidative activity.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.254 | *4.1. General*
The UV-visible (UV-VIS) spectra were recorded with a Hitachi U-2001 (Hitachi High-Technologies Corporation, Tokyo, Japan) in diethyl ether (Et2O). The positive ion electro spray ionization time of flight mass (ESI-TOF MS) spectra were recorded using a Waters Xevo G2S Q TOF mass spectrometer (Waters Corporation, Milford, CT, USA). The 1H-NMR (500 MHz) spectra were measured with a Varian UNITY INOVA 500 spectrometer (Agilent Technologies, Santa Clara, CA, USA) in CDCl3 with TMS as an internal standard. HPLC was performed on a Shimadzu LC-6AD with a Shimadzu SPD-6AV spectrophotometer (Shimadzu Corporation, Kyoto, Japan) set at 470 nm. The column used was a 250 × 10 mm i.d., 10 ΐm Cosmosil 5C18- II (Nacalai Tesque, Kyoto, Japan) with acetone:hexane (3:7, v/v) at a flow rate of 1.0 mL/min, run time of 60 min. The optical purity of astaxanthin was analyzed by chiral HPLC using a 300 × 8 mm i.d., 5 ΐm Sumichiral OA-2000 (Sumitomo Chemicals, Osaka, Japan) with *n*-hexane/CHCl3/ethanol (48:16:0.8, v/v) at a flow rate of 1.0 mL/min [23].
## *4.2. Animal Specimens*
The sea angel *C. limacina* (30 specimens, 464 mg wet weight) was collected at Monbetsu bay, Monbetsu City, Hokkaido, Japan in December 2011. Another sea angel, *P. doliiformis* (60 specimens, 1041 mg wet weight), was also collected at Monbetsu bay in April 2013. The small sea snail
*L. helicina* (6 specimens, 200 mg wet weight) was collected at Monbetsu bay in May 2013. Chum salmon, *O. keta* (3 specimens, five to six years of age), was collected at Monbetsu in September 2013.
## *4.3. Analysis of Carotenoids*
The extraction and identification of carotenoids were carried out according to our routine methods [24]. Carotenoids were extracted from living or fresh animal specimens with acetone. The acetone extract was translated to an ether-hexane (1:1) layer by the addition of water. The total carotenoid contents were calculated employing an extinction coefficient of E1%cm = 2100 [25] at Ώ max. The ether-hexane solution was evaporated. The residue was subjected to HPLC on silica gel. Carotenoid compositions were estimated by the peak area of the HPLC on silica gel with acetone–hexane (2:8)–(4:6) monitored at 450 nm.
Individual carotenoids were identified by retention time in HPLC, UV-vis (ether), ESI-TOF MS, and 1H NMR (500 MHz, CDCl3) in the case of pecetenolone.
*4.4. Identification of Carotenoids*
Ά-Carotene (**1**). ESI-TOF MS: *m/z* 536.4372 [M]+ (calcd for C40H56, 536.4382); UV-VIS: 425, 449, 475 nm.
Echinenone (**2**). ESI-TOF MS: *m/z* 551.4271 [M + H]+ (calcd for C40H53O, 551.4253); UV-VIS: 460 nm.
Canthaxanthin (**3**). ESI-TOF MS: *m/z* 565.4044 [M + H]+ (calcd for C40H53O2, 565.4046); UV-VIS 470 nm.
Ά-Cryptoxanthin (**4**). ESI-TOF MS: *m/z* 553.4511 [M + H]+ (calcd for C40H53O, 553.4409); UV-VIS: (425), 450, 475 nm.
Zeaxanthin (**5**). ESI-TOF MS: *m/z* 569.4353 [M + H]+ (calcd for C40H57O2,569.4359); UV-VIS: (425) 450, 475 nm.
Adonixanthin (**6**). ESI-TOF MS: *m/z* 583.4139 [M + H]+ (calcd for C40H55O3, 583.4151); UV-VIS 460 nm.
Idoxanthin (**7**). ESI-TOF MS: *m/z* 599.4090 [M + H]+ (calcd for C40H55O4, 599.4100); UV-VIS 460 nm.
Astaxanthin (**8**). ESI-TOF MS: *m/z* 597.3942 [M + H]+ (calcd for C40H53O4, 597.3944); UV-VIS 472 nm, Chiral HPLC [13] revealed that astaxanthin fraction in sea angels was consisted of only (3*S*,3<sup>ȝ</sup>*S*) optical isomers.
Diatoxanthin (**9**). ESI-TOF MS: *m/z* 567.4225 [M + H]+ (calcd for C40H55O2, 567.4202); UV-VIS: (426), 451, 478 nm.
Pectenol A (**10**). ESI-TOF MS: *m/z* 583.4173 [M + H]+ (calcd for C40H55O3, 583.4152); UV-VIS: (426), 451 478 nm.
Pectenol B (**11**). ESI-TOF MS: *m/z* 583.4170 [M + H]+ (calcd for C40H55O3, 583.4152); UV-VIS: (426), 451, 478 nm.
Pectenolone (**12**). ESI-TOF MS: *m/z* 581.3983 [M + H]+ (calcd for C40H53O3, 581.3995); UV-VIS: 460 nm; 1H-NMR (CDCl3, 500 MHz) Έ 1.15 (H3-16<sup>ȝ</sup>, s), 1.20 (H3- <sup>17</sup><sup>ȝ</sup>, s), 1.21 (H3-17, s), 1.32 (H3-16, s), 1.45 (H-2ȝΆ, dd, *J* = 12, 11), 1.82 (H-2Ά, d, *J* = 13, 13), 1.84 (H-2ȝ΅, ddd, *J* = 12, 4, 1.5), 1.92 (H3-19<sup>ȝ</sup>, s), 1.95 (H3-19, s), 2.07 (H-2ȝΆ, dd, *J* <sup>=</sup> 18, 10), 2.15 (H-2΅, dd, *J* = 13, 6), 2.43 (H-4ȝ΅, ddd, *J* = 18, 6, 1.5), 3.68 (OH-3, d, *J* = 2), 3.99 (H-3<sup>ȝ</sup>, m), 4.32 (H-3, ddd, *J* = 13, 6, 2), 6.22 (H-7, d, *J* = 16), 6,28 (H-14<sup>ȝ</sup>, d, *J* = 11), 6.30 (H-10, d, *J* = 11), 6.30 (H-14, d, *J* = 11), 6.36 (H-12<sup>ȝ</sup>, d, *J* = 15), 6.43 (H-8, d, *J* = 16), 6.45 (H-12, d, *J* = 15), 6.45 (H-10<sup>ȝ</sup>, d, *J* = 11), 6.53 (H-11<sup>ȝ</sup>, dd, *J* = 15, 11), 6.63 (H-15 and H-15<sup>ȝ</sup>, m), 6.65 (H-11, dd, *J* = 15, 11).
4ȝ-Hydroxypectenolone (**13**). ESI-TOF MS: *m/z* 597.3942 [M + H]+ (calcd for C40H53O4, 597.3944); UV-VIS: 460 nm.
7,8-Didehydroastaxanthin (**14**). ESI-TOF MS: *m/z* 595.3789 [M + H]+ (calcd for C40H51O4, 595.3787); UV-VIS: 474 nm.
Alloxanthin (**15**). ESI-TOF MS: *m/z* 565.4028 [M + H]+ (calcd for C40H53O2, 565.4046); UV-VIS: (426), 451 478 nm.
4-Ketoalloxanthin (**16**). ESI-TOF MS: *m/z* 579.3851 [M + H]+ (calcd for C40H51O3, 579.3838); UV-VIS: 460 nm.
4ȝ-Hydroxy-4-Ketoalloxanthin (**17**). ESI-TOF MS: *m/z* 595.3801 [M + H]+ (calcd for C40H51O4,595.3787); UV-VIS: 469 nm.
7,8,7<sup>ȝ</sup>,8<sup>ȝ</sup>-Tetradehydroastaxanthin (**18**). ESI-TOF MS: *m/z* 593.3649 [M + H]+ (calcd for C40H49O4,593.3631); UV-VIS: 476 nm.
Diadinoxanthin (**19**). ESI-TOF MS: *m/z* 583.4173 [M + H]+ (calcd for C40H55O3, 583.4151); UV-VIS: 420, 433, 470 nm.
Fucoxanthin (**20**). ESI-TOF MS: *m/z* 659.4333 [M + H]+ (calcd for C42H59O6,659.4312); UV-VIS: 445, 470 nm.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.255 | *4.5. 1O2 Quenching Activity of Carotenoids*
Quenching activity of 1O2 was measured according to the method described in the literature [26]. 1O2 quenching activities (IC50 values) of pectenolone and astaxanthin were 7.9 and 6.5 ΐM, respectively.
## **5. Conclusions**
Carotenoids originating from phytoplankton are accumulated in the sea angels, *C. limacina* and *P. doliiformis*, through eating the herbivorous sea snail, *L. helicina*, in the food chain. In sea angels, dietary carotenoids were oxidatively metabolized, as shown in Figure 3. Sea angels mainly accumulate carotenoids in their gonads. Carotenoids in the gonads of sea angels might protect against oxidative stress and enhance reproduction. Furthermore, carotenoids in sea angels can then be found in salmon through the food chain.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.256 | **Acknowledgements**
We wish to thank Kazutoshi Shindo and Ayako Osawa; Department of Food and Nutrition; Japan Women's University for measurement of the 1O2 quenching activities of pectenolone and astaxanthin.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.257 | **Authors Contributions**
T.M. analyzed carotenoids of marine animals. T.K. and M.N. collected marine animals and studied their ecology.
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ffe4a610-aead-4300-8fef-a70aeecd3fb7.258 | **Conflicts of Interest**
The authors declare no conflict of interest.
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ffe82432-4883-4adc-b03b-937c1baf5090.0 | *Edited by Amalia Stefaniu,*
*Azhar Rasul and Ghulam Hussain*
Cheminformatics has emerged as an applied branch of Chemistry that involves multidisciplinary knowledge, connecting related fields such as chemistry, computer science, biology, pharmacology, physics, and mathematical statistics.The book is organized in two sections, including multiple aspects related to advances in the development of informatic tools and their specific use in compound structure databases with various applications in life sciences, mainly in medicinal chemistry, for identification and development of new therapeutically active molecules. The book covers aspects related to genomic analysis, semantic similarity, chemometrics, pattern recognition techniques, chemical reactivity prediction, drug-likeness assessment, bioavailability, biological target recognition, machine-based drug discovery and design. Results from various computational tools and methods are discussed in the context of new compound design and development, sharing promising opportunities, and perspectives.
ISBN 978-1-83880-067-3
Cheminformatics and its Applications
Published in London, UK © 2020 IntechOpen © monsitj / iStock
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ffe82432-4883-4adc-b03b-937c1baf5090.1 | Cheminformatics and its Applications
*Edited by Amalia Stefaniu, Azhar Rasul and Ghulam Hussain*
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ffe82432-4883-4adc-b03b-937c1baf5090.2 | Cheminformatics and its Applications
*Edited by Amalia Stefaniu, Azhar Rasul and Ghulam Hussain*
Published in London, United Kingdom
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ffe82432-4883-4adc-b03b-937c1baf5090.3 | *Supporting open minds since 2005*
Cheminformatics and its Applications http://dx.doi.org/10.5772/intechopen.83236 Edited by Amalia Stefaniu, Azhar Rasul and Ghulam Hussain
#### Contributors
Dionisio Antonio Olmedo A., José Luis Medina-Franco, João D Ferreira, Francisco M Couto, Daniel Glossman-Mitnik, Norma Flores-Holguín, Juan Frau, José Ciriaco-Pinheiro, Heriberto Bitencourt, José Lobato, Antonio Florêncio De Figueiredo, Marcos Antonio Dos Santos, Fabio Gil, Raimundo Ferreira, Luã Felipe De Oliveira, Sady Alves, Edilson Luiz C De Aquino, Márcio De Souza Farias, Yu-Chen Lo, Hiroshi Honda, Gui Ren, Azhar Rasul, Ammara Riaz, Iqra Sarfraz, Ayesha Sadiqa, Javaria Nawaz, Rabia Zara, Samreen Gul Khan, Zeliha Selamoglu, Wolfgang Fecke, Bahne Stechmann, Kenji Sorimachi, Sonia Aroui, Amalia Stefaniu, Kara L. Davis, Abderraouf Kenani
#### © The Editor(s) and the Author(s) 2020
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Cheminformatics and its Applications Edited by Amalia Stefaniu, Azhar Rasul and Ghulam Hussain p. cm. Print ISBN 978-1-83880-067-3 Online ISBN 978-1-83880-068-0 eBook (PDF) ISBN 978-1-83962-518-3
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ffe82432-4883-4adc-b03b-937c1baf5090.6 | Meet the editors
Amalia Stefaniu has a background in chemical engineering, acquiring her Bachelor's degree at Politehnica University of Bucharest, Faculty of Engineering in Foreign Languages. She followed postgraduate academic studies with *Drugs and Cosmetics* specialization and she obtained a Masters degree in *Biotechnologies and food safety*. She completed her PhD in Exact Sciences - Chemistry Domain in 2011 at the University Politehnica of Bucharest, Fac-
ulty of Applied Chemistry and Materials Science, Department of Inorganic Chemistry, Physical Chemistry and Electrochemistry. She joined the National Institute for Chemical Pharmaceutical Research and Development, Bucharest in 2001, where she worked first as Chemical Research Engineer in the Pharmaceutical Biotechnologies domain. Her current position is Senior Research Scientist. Her research focuses on properties prediction, mathematical modeling, molecular docking, and therapeutic compounds design.
Dr. Azhar Rasul is an Assistant Professor at the Government College University, Faisalabad. He obtained his PhD fellowship jointly awarded by MoE, Pakistan and CSC, China and completed his Ph.D. in Chemical Cancer Biology from the Northeast Normal University, China. He later received China Postdoctoral Fellowship in 2012, Japanese Society for Promotion of Science (JSPS) Postdoctoral Fellowship in 2013, and subsequently Tokyo
Biochemical Research Foundation (TBRF) Fellowship in 2015. He has published over 100 peer-reviewed articles with a cumulative impact factor over 208 and with over 1670 citations. He has presented several invited talks at the national and international level. He has obtained several national and international research grants. His laboratory is actively engaged in interdisciplinary research on novel tumor biomarkers and identification of non-toxic anti-cancer compounds for various hallmarks of cancer from natural sources. He is a reviewer and editorial board member of several well-reputed journals.
Dr. Ghulam Hussain is working as an Assistant Professor at the Department of Physiology, Government College University, Faisalabad. Dr. Hussain has served as a visiting scientist at Huaqiao University, Xiamen, China. He earned his MPhil and Ph.D. in Neurosciences from the University of Strasbourg, France under the Overseas Scholarship Program of Higher Education Commission of Pakistan. He has published 70 peer-reviewed articles
with a cumulative impact factor of 120 and 480 citations. He has presented his work at both the national and international level. He is a recipient of two research grants from HEC Pakistan. He is also working as a reviewer for well-reputed research journals. His laboratory is involved in elucidating the possibilities of promoting peripheral nerve regeneration following traumatic injury.
Contents
**Section 1**
Tools, Let's Think Forward!
Semantic Similarity in Cheminformatics *by João D. Ferreira and Francisco M. Couto*
Tools to Design Bioactive Compounds
Angiotensin II Vasoconstrictor Octapeptide
*by Dionisio A. Olmedo and José L. Medina-Franco*
*and José Ciríaco-Pinheiro*
*by Amalia Stefaniu*
*by Kenji Sorimachi*
**Preface XI**
Insights of Chemical Structures by Chemoinformatics Approaches **1**
**Chapter 1 3**
**Chapter 2 9**
**Chapter 3 31**
**Chapter 4 47**
**Chapter 5 75**
**Chapter 6 83**
Prologue: Deep Insights of Chemical Structures by Chemoinformatics
Visible Evolution from Primitive Organisms to *Homo sapiens*
Molecular Electrostatic Potential and Chemometric Techniques as
*by Marcos Antônio B. dos Santos, Luã Felipe S. de Oliveira, Antônio Florêncio de Figueiredo, Fábio dos Santos Gil, Márcio de Souza Farias, Heriberto Rodrigues Bitencourt, José Ribamar B. Lobato, Raimundo Dirceu de P. Farreira, Sady Salomão da S. Alves, Edilson Luiz C. de Aquino*
Chemical Reactivity Properties and Bioactivity Scores of the
*by Norma Flores-Holguín, Juan Frau and Daniel Glossman-Mitnik*
Chemoinformatic Approach: The Case of Natural Products of Panama
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ffe82432-4883-4adc-b03b-937c1baf5090.7 | Contents
Preface
Cheminformatics has emerged as an applied branch of Chemistry that involves multidisciplinary knowledge, connecting related fields such as chemistry, computer science, biology, pharmacology, physics, and mathematical statistics. Computational methods are used to visualize simple structures or macromolecular assemblies, to model properties by mathematical and statistical models, to create, store and process chemical data (databases, data mining), to realize virtual screening of large compound libraries and to analyze the chemical information and optimize structure in order to develop novel compounds, materials,
The book is organized in two sections, covering plural aspects related to advances in the development of informatic tools and their specific use in compound databases and concerted efforts to link them in research platforms and networks with various purposes and applications in life sciences. Applications in medicinal chemistry, for identification and development of new therapeutically active molecules are described, but the book is not limited to these topics. For instance, the chapter titled "Visible Evolution from Primitive Organisms to Homo sapiens" covers the area of genomic analysis and development of evolutionary equations based on genome structure. It represents an important approach to explain the origin and evolution of life, providing mathematical proofs on the genomic amino acid composition homogeneity. It illustrates the use of mathematics to explain biological organisms' evolution and reduces complex structural genetic information to simple linear regression relationships. This chapter allows inexperienced readers to understand the basic concepts and theory, but also invites them to go forward, offering deep
The chapter titled "Semantic similarity in cheminformatics" presents a great overview of chemical ontologies, explaining how it works, how the relationships between different chemical or biological entities are constructed in order to bind chemical information given by structures with other aspects as chemical classifications, reaction mechanisms, metabolites, toxicity, biological pathways and so on. The authors describe the fundamental concepts of ontology-based semantic similarity, pointing to the applications in cheminformatics and discussing the efforts in ontology development to link chemical databases with related fields such as medical
Computational tools of chemometrics and pattern recognition techniques are used for the design of various compounds. Such examples are illustrated in the chapter titled "Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design of Bioactive Compounds", where authors use *ab initio* calculation of properties based on charge density and topological indices for the design of nitrofurans derivatives. The key features and descriptors, acting in the recognition process with the biological target, are elucidated and can be further used to design
or processes.
biological and chemical molecular insights.
chemistry, genomics, or proteomics.
new biologically active molecules.
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ffe82432-4883-4adc-b03b-937c1baf5090.8 | Preface
Cheminformatics has emerged as an applied branch of Chemistry that involves multidisciplinary knowledge, connecting related fields such as chemistry, computer science, biology, pharmacology, physics, and mathematical statistics. Computational methods are used to visualize simple structures or macromolecular assemblies, to model properties by mathematical and statistical models, to create, store and process chemical data (databases, data mining), to realize virtual screening of large compound libraries and to analyze the chemical information and optimize structure in order to develop novel compounds, materials, or processes.
The book is organized in two sections, covering plural aspects related to advances in the development of informatic tools and their specific use in compound databases and concerted efforts to link them in research platforms and networks with various purposes and applications in life sciences. Applications in medicinal chemistry, for identification and development of new therapeutically active molecules are described, but the book is not limited to these topics. For instance, the chapter titled "Visible Evolution from Primitive Organisms to Homo sapiens" covers the area of genomic analysis and development of evolutionary equations based on genome structure. It represents an important approach to explain the origin and evolution of life, providing mathematical proofs on the genomic amino acid composition homogeneity. It illustrates the use of mathematics to explain biological organisms' evolution and reduces complex structural genetic information to simple linear regression relationships. This chapter allows inexperienced readers to understand the basic concepts and theory, but also invites them to go forward, offering deep biological and chemical molecular insights.
The chapter titled "Semantic similarity in cheminformatics" presents a great overview of chemical ontologies, explaining how it works, how the relationships between different chemical or biological entities are constructed in order to bind chemical information given by structures with other aspects as chemical classifications, reaction mechanisms, metabolites, toxicity, biological pathways and so on. The authors describe the fundamental concepts of ontology-based semantic similarity, pointing to the applications in cheminformatics and discussing the efforts in ontology development to link chemical databases with related fields such as medical chemistry, genomics, or proteomics.
Computational tools of chemometrics and pattern recognition techniques are used for the design of various compounds. Such examples are illustrated in the chapter titled "Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design of Bioactive Compounds", where authors use *ab initio* calculation of properties based on charge density and topological indices for the design of nitrofurans derivatives. The key features and descriptors, acting in the recognition process with the biological target, are elucidated and can be further used to design new biologically active molecules.
**II**
**Section 2**
*by Azhar Rasul*
Identification
Models
Drug Design and Develpment by Chemical Tools **107**
**Chapter 7 109**
**Chapter 8 113**
**Chapter 9 127**
**Chapter 10 147**
**Chapter 11 165**
Accelerating Chemical Tool Discovery by Academic Collaborative
Chemical Biology Toolsets for Drug Discovery and Target
*by Ammara Riaz, Azhar Rasul, Iqra Sarfraz, Javaria Nawaz,*
Artificial Intelligence-Based Drug Design and Discovery *by Yu-Chen Lo, Gui Ren, Hiroshi Honda and Kara L. Davis*
Cell-Penetrating Peptides: A Challenge for Drug Delivery
*Ayesha Sadiqa, Rabia Zara, Samreen Gul Khan and Zeliha Selamoglu*
Prologue: Cheminformatics and Its Applications
*by Bahne Stechmann and Wolfgang Fecke*
*by Sonia Aroui and Abderraouf Kenani*
The next chapter ("Chemical reactivity properties and bioactivity scores of the Angiotensin II vasoconstrictor octapeptide") emphasizes the reactivity descriptors, drug-likeness assessment, and prediction of oral bioavailability scores as preliminary steps for the development of new drugs based on specific peptide analogues, achieving a comparison of prediction realized with different quantum mechanical modelling methods.
Molecular complexity, flexibility, and other structural features and properties are used in a cheminformatic analysis of natural and synthetic compounds, based on similarity, in a case study of products originating from Panama, in an attempt to find and optimize lead compounds with antimalarial activity, in the chapter "Cheminformatic Approach: The Case of Natural Products of Panama".
In the chapter titled "Accelerating chemical tool discovery by academic collaborative models", the authors highlight the international efforts of academia and industrial pharmacists to generate consortia in the interdisciplinary field of chemical biology, to connect their knowledge, compound libraries and facilities, having the important goal to create open access information. The principal aim remains the development of new therapeutic compounds using the knowledge from multidisciplinary fields in academic and public and private media, thus helping researchers to solve mechanistical issues in life sciences.
The chapter "Chemical Biology Toolsets for Drug Discovery and Target Identification" is an overview of chemical techniques and methodologies implemented in the study of biological systems, metabolic pathways, drug-target complex interactions, and other biochemical process, all with the common goal to understand the action and all biochemical implications of the introduction in therapeutics of a new drug. Different complementary instrumental techniques and methodologies aiming to provide deep insights into the chemical structure are discussed alongside validation methods and techniques of selection of a new drug candidate.
Machine learning and deep learning are aspects covered in the chapter titled "Machine-learning based drug discovery and design", presenting a detailed view of their theoretical aspects and applications related to *de novo* drug design, QSAR analysis, and chemical space visualization
The chapter titled "Cell Penetrating Peptides", as its title suggests, emphasizes their biomedical applications as transport vectors for different therapeutic agents across cell membranes. The authors describe the origin and the classifications of CPPs, their uptake mechanisms, and their promising clinical efficacity in various cancer therapies.
With all information and conclusive examples presented above, this book is a valuable learning resource for readers from the scientific community, students, researchers both beginners and experienced in the field of chemistry/bioinformatics and related domains. By taking note of these chapters, I hope readers will feel encouraged, inspired, and motivated to continue new research and discoveries.
**V**
I thank all authors for their substantial contributions to this book, for sharing their knowledge, and for opening new opportunities and perspectives in such an
National Institute for Chemical - Pharmaceutical Research and
Laboratory of Molecular Design and Molecular Docking,
Government College University Faisalabad (GCUF),
Government College University Faisalabad (GCUF),
Development – ICCF Bucharest (Romania), Department of Pharmaceutical Biotechnologies,
**Amalia Stefaniu**
Bucharest, Romania
Faisalabad, Pakistan
Faisalabad, Pakistan
**Ghulam Hussain**
Department of Zoology, Faculty of Life Sciences,
Department of Physiology, Faculty of Life Sciences,
**Dr. Azhar Rasul**
evolving field as cheminformatics is.
I thank all authors for their substantial contributions to this book, for sharing their knowledge, and for opening new opportunities and perspectives in such an evolving field as cheminformatics is.
## **Amalia Stefaniu**
National Institute for Chemical - Pharmaceutical Research and Development – ICCF Bucharest (Romania), Department of Pharmaceutical Biotechnologies, Laboratory of Molecular Design and Molecular Docking, Bucharest, Romania
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ffe82432-4883-4adc-b03b-937c1baf5090.9 | **Dr. Azhar Rasul**
Department of Zoology, Faculty of Life Sciences, Government College University Faisalabad (GCUF), Faisalabad, Pakistan
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ffe82432-4883-4adc-b03b-937c1baf5090.10 | **Ghulam Hussain**
Department of Physiology, Faculty of Life Sciences, Government College University Faisalabad (GCUF), Faisalabad, Pakistan
**IV**
The next chapter ("Chemical reactivity properties and bioactivity scores of the Angiotensin II vasoconstrictor octapeptide") emphasizes the reactivity descriptors, drug-likeness assessment, and prediction of oral bioavailability scores as preliminary steps for the development of new drugs based on specific peptide analogues, achieving a comparison of prediction realized with different quantum
Molecular complexity, flexibility, and other structural features and properties are used in a cheminformatic analysis of natural and synthetic compounds, based on similarity, in a case study of products originating from Panama, in an attempt to find and optimize lead compounds with antimalarial activity, in the chapter
In the chapter titled "Accelerating chemical tool discovery by academic collaborative models", the authors highlight the international efforts of academia and industrial pharmacists to generate consortia in the interdisciplinary field of chemical biology, to connect their knowledge, compound libraries and facilities, having the important goal to create open access information. The principal aim remains the development of new therapeutic compounds using the knowledge from multidisciplinary fields in academic and public and private media, thus helping researchers to solve mechanistical issues in
"Cheminformatic Approach: The Case of Natural Products of Panama".
The chapter "Chemical Biology Toolsets for Drug Discovery and Target
Machine learning and deep learning are aspects covered in the chapter titled "Machine-learning based drug discovery and design", presenting a detailed view of their theoretical aspects and applications related to *de novo* drug design, QSAR
The chapter titled "Cell Penetrating Peptides", as its title suggests, emphasizes their biomedical applications as transport vectors for different therapeutic agents across cell membranes. The authors describe the origin and the classifications of CPPs, their uptake mechanisms, and their promising clinical efficacity in various cancer
With all information and conclusive examples presented above, this book is a valuable learning resource for readers from the scientific community, students, researchers both beginners and experienced in the field of chemistry/bioinformatics and related domains. By taking note of these chapters, I hope readers will feel encouraged, inspired, and motivated to continue new research and
Identification" is an overview of chemical techniques and methodologies implemented in the study of biological systems, metabolic pathways, drug-target complex interactions, and other biochemical process, all with the common goal to understand the action and all biochemical implications of the introduction in therapeutics of a new drug. Different complementary instrumental techniques and methodologies aiming to provide deep insights into the chemical structure are discussed alongside validation methods and techniques of selection of a new
mechanical modelling methods.
life sciences.
drug candidate.
therapies.
discoveries.
analysis, and chemical space visualization
**1**
Section 1
Insights of Chemical
Structures by
Chemoinformatics
Approaches
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ffe82432-4883-4adc-b03b-937c1baf5090.11 | Section 1
Insights of Chemical Structures by Chemoinformatics Approaches
**3**
ties and behaviour.
preponderant in medicinal chemistry.
**Chapter 1**
Prologue: Deep Insights
Think Forward!
maceutical and food science industries.
**1. Introduction - Multidisciplinary context**
*Amalia Stefaniu*
of Chemical Structures by
Chemoinformatics Tools, Let's
The constant need of chemical scientists to understand complex phenomena and process and to achieve a rational structural design by controlling the synthesis to obtain compounds with improved properties or materials with enhanced quality, together with advances in information technology, has led to development of a new branch of chemistry—chemoinformatics—with strong implications in life sciences such as molecular biology or biochemistry, with major interest in medicine, phar-
Mainly, these interdisciplinary efforts are focused on the medical and pharmaceutical area, aiming to improve the quality and standard of life, and have applications in drug design and development of new therapeutic strategies. Chemoinformatics, as new discipline, covers a broad spectrum of aspects including all applications of information technology to chemistry involving: constructing and archiving big compound libraries (small molecules and proteins) containing structural properties and molecular descriptors, spectra, X-ray crystallography data and so on; information processing; large-scale chemical data mining; computational tools for structure and interactions visualisation, computational models for predicting interactions, to calculate properties and bioactivity, molecular docking and dynamic simulations methodologies, virtual screening, pharmacophore modelling, fragments similarity analysis, estimation of ADME (absorption, distribution, metabolism and excretion) characteristics, toxicity alerting, etc. [1–4]. The integration of chemical information and its transformation involves mathematical models and statistical data analysis.
Due to web servers and open data initiatives, large amount of chemical data from screening libraries are now available [5] and facilitate the drug discovery process. There are numerous chemoinformatics databases which contain various experimental and/or predicted properties of small molecules (ligands), peptides, proteins and data about their interactions (drug-drug interactions, ligand-protein interactions, protein-protein interactions, RNA-ligand interactions), chemical toxicity, bioactivity, adverse drug reactions, drug pathways, toxicogenomics, secondary metabolites, pharmacokinetics, etc. The existing data could help to build new structures and new models and to make new in silico predictions about physico-chemical proper-
To raise awareness of the outstanding importance and impact of chemoinformatics research, exemplified below are some of its applications in life sciences,
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ffe82432-4883-4adc-b03b-937c1baf5090.13 | Prologue: Deep Insights of Chemical Structures by Chemoinformatics Tools, Let's Think Forward!
*Amalia Stefaniu*
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ffe82432-4883-4adc-b03b-937c1baf5090.14 | **1. Introduction - Multidisciplinary context**
The constant need of chemical scientists to understand complex phenomena and process and to achieve a rational structural design by controlling the synthesis to obtain compounds with improved properties or materials with enhanced quality, together with advances in information technology, has led to development of a new branch of chemistry—chemoinformatics—with strong implications in life sciences such as molecular biology or biochemistry, with major interest in medicine, pharmaceutical and food science industries.
Mainly, these interdisciplinary efforts are focused on the medical and pharmaceutical area, aiming to improve the quality and standard of life, and have applications in drug design and development of new therapeutic strategies. Chemoinformatics, as new discipline, covers a broad spectrum of aspects including all applications of information technology to chemistry involving: constructing and archiving big compound libraries (small molecules and proteins) containing structural properties and molecular descriptors, spectra, X-ray crystallography data and so on; information processing; large-scale chemical data mining; computational tools for structure and interactions visualisation, computational models for predicting interactions, to calculate properties and bioactivity, molecular docking and dynamic simulations methodologies, virtual screening, pharmacophore modelling, fragments similarity analysis, estimation of ADME (absorption, distribution, metabolism and excretion) characteristics, toxicity alerting, etc. [1–4]. The integration of chemical information and its transformation involves mathematical models and statistical data analysis.
Due to web servers and open data initiatives, large amount of chemical data from screening libraries are now available [5] and facilitate the drug discovery process. There are numerous chemoinformatics databases which contain various experimental and/or predicted properties of small molecules (ligands), peptides, proteins and data about their interactions (drug-drug interactions, ligand-protein interactions, protein-protein interactions, RNA-ligand interactions), chemical toxicity, bioactivity, adverse drug reactions, drug pathways, toxicogenomics, secondary metabolites, pharmacokinetics, etc. The existing data could help to build new structures and new models and to make new in silico predictions about physico-chemical properties and behaviour.
To raise awareness of the outstanding importance and impact of chemoinformatics research, exemplified below are some of its applications in life sciences, preponderant in medicinal chemistry.
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ffe82432-4883-4adc-b03b-937c1baf5090.15 | **2. Applications of chemoinformatics in medicinal chemistry**
Novel druggable protein targets are a subject of research in order to develop new therapeutic strategies against various diseases (scleroderma, Alzheimer's disease, infections, etc.). Investigations include methods such as quantitative structureactivity relationships (QSAR), similarity search, pharmacophore modelling, molecular docking and dynamic simulations and toxicity assessment.
#### **2.1 Anticancer therapy design**
To fight against malignancies, new screening methods aim to identify and develop novel chemical antiproliferative agents, with promising results. As example, biomolecular modelling techniques are used to identify potential kinase inhibitor targets. The mitogen-activated protein kinase (MAPK) plays a key role in tumorigenesis; that is why it is considered a priority druggable target candidate for anticancer therapy. The interactions of cancer-related MAPK kinases and potential inhibitors are investigated by in silico tools. Molecular docking calculations are employed to predict the inhibitor-bound active sites and the binding modes for actual and potential anticancer drugs [6].
#### **2.2 Parkinson's disease**
Researchers' efforts to improve medication for Parkinson's disease benefit from chemoinformatics and molecular docking tools to identify new potential neuroprotective compounds able to effectively treat the disease, by inhibition of oligomerization process of α-synuclein protein. By computational techniques, the protein in its dimer and oligomer forms can be studied, and multiple molecules are subject of computational simulations in order to identify potential inhibitors of α-synuclein aggregation [7].
#### **2.3 Alzheimer's disease**
Chemoinformatics approaches including molecular docking, dynamic simulations, lead optimization and quantum chemical characterisation are used to achieve the inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes, responsible for cholinergic dysfunctions associated with the cognitive and behavioural abnormalities in dementing illness, in order to design and develop new therapeutic agents against this disease [8–11]. Other approaches focus on the amyloid-beta aggregation process, trying to stop the formation of neurotoxic species, and the design of new inhibitors, the study being also facilitated by computational techniques such as QSAR modelling and assessment of inhibition efficiency by predicting stability and binding modes of potential inhibitors through combined computational techniques including structure-activity relationships analysis, docking and molecular dynamic simulations [12–15].
#### **2.4 Antimicrobial agents**
Researchers focus their studies to block the activity of DNA gyrase and topoisomerase IV, which are essential bacterial enzymes involved in replication and recombination processes. The design of novel antibacterial agents that act against these enzymes can be realised by molecular docking techniques and bioactivity evaluation. That is the case of quinolones, which act equally against DNA gyrase and topoisomerase IV [16–19].
**5**
**Author details**
molecules.
**of abuse**
Amalia Stefaniu
*Prologue: Deep Insights of Chemical Structures by Chemoinformatics Tools, Let's Think Forward!*
*Pharmacokinetics/ADMET properties* such as absorption, distribution, metabolism, excretion and toxicity of designed structures are assessed through computational approaches too, aiming to predict the therapeutic potential of the lead compound. Biochemical properties and drug-likeness according Lipinski's rule of five (RO5) [20] and the molecular flexibility, as key descriptors to describe the oral bioavailability of drugs, are also predicted using computational tools. Thus, computer-aided drug design, coupled with in silico ADMET studies, helps to select the drug candidate molecules with possible better efficacy and less side effects (poor hepatotoxic effects).
**3. Application in identification and quantification of substances**
Recent researches report the application of chemometric tools in correlation with spectrometric techniques (near-infrared spectroscopy) for onsite analysis of cannabinoids or amphetamine compounds (with portable and handheld NIR devices). The chemometric tools allow the user to compare collection of spectra, to develop prediction models and to achieve a real-time detection of sample contamination. Such method could become an alternative way of detection of illicit drugs, determined in oral fluids, being non-invasive, rapid and accurate test, completely automated [21, 22].
Food chemical data sets can be manipulated and analysed also by computational resources similar with those for drugs and nutraceuticals. The interest in this area is growing because of the food-related industrial challenges. Thus, an emerging field of research has arisen: foodinformatics [23]. In silico quantitative approaches are used to assess genotoxicity and carcinogenicity of food additives (flavours, colourants, contaminants, etc.) or cosmetic ingredients [24–26], in the attempts of safety evaluation for the human health. All these computational approaches must
This section is a collection of advanced studies focusing on topics of interest in the context of chemoinformatics applications in drug discovery and design of new
Department of Pharmaceutical Biotechnologies, National Institute of Chemical Pharmaceutical Research and Development (ICCF), Bucharest, Romania
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,
\*Address all correspondence to: [email protected]
provided the original work is properly cited.
*DOI: http://dx.doi.org/10.5772/intechopen.91858*
**4. Applications in food chemistry**
be verified by in vitro methods.
*Prologue: Deep Insights of Chemical Structures by Chemoinformatics Tools, Let's Think Forward! DOI: http://dx.doi.org/10.5772/intechopen.91858*
*Pharmacokinetics/ADMET properties* such as absorption, distribution, metabolism, excretion and toxicity of designed structures are assessed through computational approaches too, aiming to predict the therapeutic potential of the lead compound. Biochemical properties and drug-likeness according Lipinski's rule of five (RO5) [20] and the molecular flexibility, as key descriptors to describe the oral bioavailability of drugs, are also predicted using computational tools. Thus, computer-aided drug design, coupled with in silico ADMET studies, helps to select the drug candidate molecules with possible better efficacy and less side effects (poor hepatotoxic effects).
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ffe82432-4883-4adc-b03b-937c1baf5090.16 | **3. Application in identification and quantification of substances of abuse**
Recent researches report the application of chemometric tools in correlation with spectrometric techniques (near-infrared spectroscopy) for onsite analysis of cannabinoids or amphetamine compounds (with portable and handheld NIR devices). The chemometric tools allow the user to compare collection of spectra, to develop prediction models and to achieve a real-time detection of sample contamination. Such method could become an alternative way of detection of illicit drugs, determined in oral fluids, being non-invasive, rapid and accurate test, completely automated [21, 22].
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ffe82432-4883-4adc-b03b-937c1baf5090.17 | **4. Applications in food chemistry**
Food chemical data sets can be manipulated and analysed also by computational resources similar with those for drugs and nutraceuticals. The interest in this area is growing because of the food-related industrial challenges. Thus, an emerging field of research has arisen: foodinformatics [23]. In silico quantitative approaches are used to assess genotoxicity and carcinogenicity of food additives (flavours, colourants, contaminants, etc.) or cosmetic ingredients [24–26], in the attempts of safety evaluation for the human health. All these computational approaches must be verified by in vitro methods.
This section is a collection of advanced studies focusing on topics of interest in the context of chemoinformatics applications in drug discovery and design of new molecules.
| doab | 2025-04-07T04:13:04.414587 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.18 | **Author details**
*Cheminformatics and Its Applications*
**2.1 Anticancer therapy design**
actual and potential anticancer drugs [6].
ing and molecular dynamic simulations [12–15].
**2.2 Parkinson's disease**
aggregation [7].
**2.3 Alzheimer's disease**
**2.4 Antimicrobial agents**
and topoisomerase IV [16–19].
**2. Applications of chemoinformatics in medicinal chemistry**
Novel druggable protein targets are a subject of research in order to develop new therapeutic strategies against various diseases (scleroderma, Alzheimer's disease, infections, etc.). Investigations include methods such as quantitative structureactivity relationships (QSAR), similarity search, pharmacophore modelling, molecular docking and dynamic simulations and toxicity assessment.
To fight against malignancies, new screening methods aim to identify and develop novel chemical antiproliferative agents, with promising results. As example, biomolecular modelling techniques are used to identify potential kinase inhibitor targets. The mitogen-activated protein kinase (MAPK) plays a key role in tumorigenesis; that is why it is considered a priority druggable target candidate for anticancer therapy. The interactions of cancer-related MAPK kinases and potential inhibitors are investigated by in silico tools. Molecular docking calculations are employed to predict the inhibitor-bound active sites and the binding modes for
Researchers' efforts to improve medication for Parkinson's disease benefit from chemoinformatics and molecular docking tools to identify new potential neuroprotective compounds able to effectively treat the disease, by inhibition of oligomerization process of α-synuclein protein. By computational techniques, the protein in its dimer and oligomer forms can be studied, and multiple molecules are subject of computational simulations in order to identify potential inhibitors of α-synuclein
Chemoinformatics approaches including molecular docking, dynamic simulations, lead optimization and quantum chemical characterisation are used to achieve the inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes, responsible for cholinergic dysfunctions associated with the cognitive and behavioural abnormalities in dementing illness, in order to design and develop new therapeutic agents against this disease [8–11]. Other approaches focus on the amyloid-beta aggregation process, trying to stop the formation of neurotoxic species, and the design of new inhibitors, the study being also facilitated by computational techniques such as QSAR modelling and assessment of inhibition efficiency by predicting stability and binding modes of potential inhibitors through combined computational techniques including structure-activity relationships analysis, dock-
Researchers focus their studies to block the activity of DNA gyrase and topoisomerase IV, which are essential bacterial enzymes involved in replication and recombination processes. The design of novel antibacterial agents that act against these enzymes can be realised by molecular docking techniques and bioactivity evaluation. That is the case of quinolones, which act equally against DNA gyrase
**4**
Amalia Stefaniu
Department of Pharmaceutical Biotechnologies, National Institute of Chemical Pharmaceutical Research and Development (ICCF), Bucharest, Romania
\*Address all correspondence to: [email protected]
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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ffe82432-4883-4adc-b03b-937c1baf5090.21 | Visible Evolution from Primitive Organisms to *Homo sapiens*
*Kenji Sorimachi*
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ffe82432-4883-4adc-b03b-937c1baf5090.22 | **Abstract**
The ratios of amino acids to the total amino acids deduced from the complete genome and those of nucleotides to the total nucleotides in the genome are useful indexes to characterize various large genomes among different species from bacteria to *Homo sapiens*. These indexes are not only independent of species but also of genome size. Using these indexes, the following results were obtained: (1) primitive life forms appeared to have similar amino acid compositions to present day organisms; (2) cellular amino acid compositions that are similar among various species and between whole cells and complete genomes; (3) genome structure that is homogeneously constructed from putative small units encoding proteins of similar amino acid compositions, followed by synchronous mutations over the genome; (4) all organisms can be classified into two groups, "GC-rich" and "AT-rich," based on their nucleotide contents, or "terrestrial" and "aquatic vertebrates" based on natural selection by cluster analyses using amino acid contents as the traits; and (5) evolution based on nucleotide content alterations can be expressed by definitive equations. Thus, the ratios of amino acids or nucleotides to their total contents are useful indexes for characterizing genomes, regardless of species differences and genome sizes. The two normalized nucleotide contents are universally expressed regression line.
**Keywords:** genome, mitochondria, codons, Chargaff's parity rules, cluster analysis, normalization, phylogenetic trees, evolution
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ffe82432-4883-4adc-b03b-937c1baf5090.23 | **1. Introduction**
The origin of life has long been interested to human since old times. Indeed, Aristotle proposed "spontaneous generation" more than 2000 years ago, although this idea was disproved by Louis Pasteur in experiments using "swan neck flasks." Our great interest in the origin of life might be expressed by the following philosophical words: *Where do we come from? What are we? Where are we going?* These words were written by French artist Paul Gauguin on his canvas in Tahiti in 1897.
The development of nucleotide sequencing technology [1, 2] has contributed to progress in molecular biology, including the analysis of a complete bacterial genome first carried out in 1995 [3], and, subsequently, the draft human genome, which was reported in 2001 [4, 5]. At present (June 19, 2019), 498 eukaryote, 5159 bacterial, and 296 archaeal complete genomes were determined. However, the origin of life is still unclear. Assuming that the replacement rates of nucleotides or amino acids in genes are constant [6], phylogenetic trees were drawn [6–11]. However, we know that their exact replacement rates differ between genes and between species. Studies based on nucleotide or amino acid sequences are applicable to genes
whose nucleotide or amino acid numbers are much smaller than those of complete genomes, but not to genomes consisting of huge numbers of nucleotides and many genes. Of course, simple comparison of sequence differences between genes in the same species and the same genes in different species is useful.
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ffe82432-4883-4adc-b03b-937c1baf5090.24 | **2. Normalization**
Intraspecies nucleotide contents were first analyzed in 1950 by Chargaff, who reported that G = C, A = T, and [(G + A) = (C + T)] [12], which was named as Chargaff's first parity rule. This rule is understandable based on the double-stranded DNA structure [13]. Additionally, this rule is applicable to single-stranded DNA obtained from a single species nucleus, termed Chargaff's second parity rule [14]. As the rules are based on normalized values to 1 (G + C + A + T = 1), nucleotide contents are expressed by their ratios. However, the second parity rule is more difficult to understand because we could not image how G and C or T and A pairs are formed in the single DNA strand. Recently, this puzzle has been solved mathematically, using the similarity of the forward and reverse strands and homogeneity of the DNA strand over the genome structure [15]. Although Chargaff's parity rules represent original intraspecies phenomena, the rules can be expanded to inter-species phenomena using data from a large number of complete genomes [16]: the second parity rule is applicable only to a single DNA strand from a double-stranded DNA molecule.
Sueoka [17] was the first to analyze the cellular amino acid composition in bacteria, and our laboratory has independently analyzed the cellular amino acid compositions of bacteria, archaea, and eukaryotes [18]. Graphical representation or a diagrammatic approach to the study of complicated biological systems can provide an intuitive picture and provide useful insights [19, 20]. Using certain graphical presentations, huge data sets from genomes can be easily recognized as simple patterns representing complicated organisms. Indeed, using a radar chart to express cellular amino acid compositions, their patterns, a "star-shape," are similar among various organisms, and their differences seem to reflect biological evolution [18]. In addition, the amino acid compositions deduced from complete genomes resemble those obtained from amino acid analyses of cell lysates [21]. These results suggest that the ratios of amino acids to the total amino acids and those of nucleotides to the total nucleotide content are useful indices to characterize whole genome structures [21].
## **3. Patternalization of amino acid compositions**
In general, there are 20 amino acids that can form proteins, and the amino acid sequences are strictly controlled by 64 codons consisting of three nucleotides, a triplet. Thus, differences in amino acid sequences of the same kind of proteins reflect biological evolution among species, although differences among different kinds of proteins seem not to be significant. Furthermore, sequence comparisons of protein mixtures are theoretically too complex to consider given currently available tools. Conversely, the amino acid composition predicted from protein(s) can characterize protein(s) from a different point of view, not only among the same organisms, but also among different organisms. In fact, the cellular amino acid compositions of various bacteria have been analyzed [17]. Based on the 20 amino acids that comprise proteins, there were 20 traits that could be evaluated, which, at first glance, seemed too many to provide meaningful information for cells.
**11**
*Visible Evolution from Primitive Organisms to* Homo sapiens
However, using a radar chart to present the amino acid compositions, the data could be patternalized, and the amino acid composition was observed to represent certain cellular characteristics, as shown in **Figure 1**. The patterns of bacteria (*Escherichia coli*) and of humans (*Homo sapiens*) resemble each other, although there is a great evolutionary distance between these two organisms. Microorganisms' fossils were found in 550–2800-million-year-old rocks [22–24], and it is thought that bacteria are evolutionarily close to primitive life forms. Therefore, it seemed that the primitive life forms might have similar amino acid compositions [21]. This "star-shape" cellular amino acid composition pattern must have been conserved from primitive
*Radar charts of cellular amino acid compositions of* Escherichia coli *and* Homo sapiens*. Amino acid compositions are expressed as the percentage of total amino acids. Gln and Asn are combined with Glu and*
*Asp, respectively, because the former two are converted into the latter two during hydrolysis [18].*
**4. Chronological precedence of protein formation over codon formation**
To understand the establishment of primitive organisms, the chronological precedence of protein and codon formation is a very important subject in biological evolution. Unfortunately, this theory has not yet been proven, because primitive organisms were formed under so many unknown factors an extremely long time ago. However, a simulation analysis based on a random choice of amino acids or nucleotides was carried out, which assumed that their polymerization depended on their free monomer concentrations, according to the chemical reaction rule that governs natural phenomena. Amino acid polymerizations produced a protein which reflected original free amino acid concentrations without codons, while nucleotide polymerizations did not produce functional proteins, even after considering the codon table, as shown in **Figure 2** [25]. Therefore, it seems difficult to predict "the RNA world" which presumes that RNA polymers formed primitive life forms [26]. Additionally, the possibility of the accumulation of RNA, which has a UV absorbance at around 250 nm, might be very low under the strong UV irradiation present on the primitive Earth. These results suggest that protein formation might chronologically precede codon formation at the end of prebiotic evolution, although we have no explanation of how the nucleotide sequence information necessary for proteins might have been transmitted to the nucleotide polymerization that established the codons. The
*DOI: http://dx.doi.org/10.5772/intechopen.91170*
organisms to those current organisms.
**Figure 1.**
*Visible Evolution from Primitive Organisms to* Homo sapiens *DOI: http://dx.doi.org/10.5772/intechopen.91170*
**Figure 1.**
*Cheminformatics and Its Applications*
**2. Normalization**
whose nucleotide or amino acid numbers are much smaller than those of complete genomes, but not to genomes consisting of huge numbers of nucleotides and many genes. Of course, simple comparison of sequence differences between genes in the
Intraspecies nucleotide contents were first analyzed in 1950 by Chargaff, who reported that G = C, A = T, and [(G + A) = (C + T)] [12], which was named as Chargaff's first parity rule. This rule is understandable based on the double-stranded DNA structure [13]. Additionally, this rule is applicable to single-stranded DNA obtained from a single species nucleus, termed Chargaff's second parity rule [14]. As the rules are based on normalized values to 1 (G + C + A + T = 1), nucleotide contents are expressed by their ratios. However, the second parity rule is more difficult to understand because we could not image how G and C or T and A pairs are formed in the single DNA strand. Recently, this puzzle has been solved mathematically, using the similarity of the forward and reverse strands and homogeneity of the DNA strand over the genome structure [15]. Although Chargaff's parity rules represent original intraspecies phenomena, the rules can be expanded to inter-species phenomena using data from a large number of complete genomes [16]: the second parity rule is applicable
Sueoka [17] was the first to analyze the cellular amino acid composition in bacteria, and our laboratory has independently analyzed the cellular amino acid compositions of bacteria, archaea, and eukaryotes [18]. Graphical representation or a diagrammatic approach to the study of complicated biological systems can provide an intuitive picture and provide useful insights [19, 20]. Using certain graphical presentations, huge data sets from genomes can be easily recognized as simple patterns representing complicated organisms. Indeed, using a radar chart to express cellular amino acid compositions, their patterns, a "star-shape," are similar among various organisms, and their differences seem to reflect biological evolution [18]. In addition, the amino acid compositions deduced from complete genomes resemble those obtained from amino acid analyses of cell lysates [21]. These results suggest that the ratios of amino acids to the total amino acids and those of nucleotides to the total nucleotide content are useful indices to characterize whole genome
In general, there are 20 amino acids that can form proteins, and the amino acid sequences are strictly controlled by 64 codons consisting of three nucleotides, a triplet. Thus, differences in amino acid sequences of the same kind of proteins reflect biological evolution among species, although differences among different kinds of proteins seem not to be significant. Furthermore, sequence comparisons of protein mixtures are theoretically too complex to consider given currently available tools. Conversely, the amino acid composition predicted from protein(s) can characterize protein(s) from a different point of view, not only among the same organisms, but also among different organisms. In fact, the cellular amino acid compositions of various bacteria have been analyzed [17]. Based on the 20 amino acids that comprise proteins, there were 20 traits that could be evaluated, which, at first glance, seemed too many to provide meaningful information for cells.
same species and the same genes in different species is useful.
only to a single DNA strand from a double-stranded DNA molecule.
**3. Patternalization of amino acid compositions**
**10**
structures [21].
*Radar charts of cellular amino acid compositions of* Escherichia coli *and* Homo sapiens*. Amino acid compositions are expressed as the percentage of total amino acids. Gln and Asn are combined with Glu and Asp, respectively, because the former two are converted into the latter two during hydrolysis [18].*
However, using a radar chart to present the amino acid compositions, the data could be patternalized, and the amino acid composition was observed to represent certain cellular characteristics, as shown in **Figure 1**. The patterns of bacteria (*Escherichia coli*) and of humans (*Homo sapiens*) resemble each other, although there is a great evolutionary distance between these two organisms. Microorganisms' fossils were found in 550–2800-million-year-old rocks [22–24], and it is thought that bacteria are evolutionarily close to primitive life forms. Therefore, it seemed that the primitive life forms might have similar amino acid compositions [21]. This "star-shape" cellular amino acid composition pattern must have been conserved from primitive organisms to those current organisms.
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ffe82432-4883-4adc-b03b-937c1baf5090.25 | **4. Chronological precedence of protein formation over codon formation**
To understand the establishment of primitive organisms, the chronological precedence of protein and codon formation is a very important subject in biological evolution. Unfortunately, this theory has not yet been proven, because primitive organisms were formed under so many unknown factors an extremely long time ago. However, a simulation analysis based on a random choice of amino acids or nucleotides was carried out, which assumed that their polymerization depended on their free monomer concentrations, according to the chemical reaction rule that governs natural phenomena. Amino acid polymerizations produced a protein which reflected original free amino acid concentrations without codons, while nucleotide polymerizations did not produce functional proteins, even after considering the codon table, as shown in **Figure 2** [25]. Therefore, it seems difficult to predict "the RNA world" which presumes that RNA polymers formed primitive life forms [26]. Additionally, the possibility of the accumulation of RNA, which has a UV absorbance at around 250 nm, might be very low under the strong UV irradiation present on the primitive Earth. These results suggest that protein formation might chronologically precede codon formation at the end of prebiotic evolution, although we have no explanation of how the nucleotide sequence information necessary for proteins might have been transmitted to the nucleotide polymerization that established the codons. The
#### **Figure 2.**
*Computational amino acid compositions of an* Ureaplasma urealyticum *gene. Upper panel: random choice of amino acids was carried out in the original gene (5005 amino acid pool). Lower panel: random choice of nucleotides was carried out in the original gene (15,018 nucleotides). In the simulation using nucleotides, the stop codon and Trp were discarded from the calculation of amino acid compositions, and a triplet formed was immediately counted as an amino acid. This figure was adapted from Sorimachi and Okayasu [25].*
"amino acid world" [21] seems a better fit for primitive life forms rather than the "RNA world." There are several hypotheses for codon formation [27–29], but the process of codon formation has not yet been determined.
According to our simulation analyses [25], proteins that were components of primitive life forms might reflect the free amino acid concentrations on the primitive Earth. As shown in **Figure 1**, the cellular basic amino acid composition, the "star-shape," is characterized by comparatively high concentrations of hydrophobic amino acids, such as valine, leucine, and isoleucine. The glycine and alanine contents were also comparatively high. The former might contribute to self-aggregation of proteins via hydrophobicity to form primitive life forms under low protein concentrations, and the latter might reflect their easy formation on the primitive Earth. In fact, simple amino acids such as glycine and alanine have been identified in meteorites [30, 31] and can be formed by electrical discharge in an atmosphere presumed to reflect primitive Earth [32]. Conversely, the phenylalanine, tryptophan, and tyrosine content, which can absorb ultraviolet light, were quite low. Strong ultraviolet irradiation might induce photodegradation of these amino acids. The differences in amino acid contents in cellular amino acid compositions seem to reflect the presumed free amino acid concentrations on the primitive Earth and eventually resulted in the formation of the "star-shaped" cellular amino acid compositions (**Figure 1**).
### **5. Amino acid compositions deduced from complete genomes**
Initially, amino acid compositions were deduced from complete genomes by assuming that each gene is equally expressed in a whole cell [21]. This resulted in the amino acid composition deduced from the complete genome resembling the cellular amino acid composition obtained from the amino acid analyses of cell lysates [21], as shown in **Figure 3**. This coincidence is difficult to understand because of the different origins of both values, until the genome structure has been clarified, as shown in the next section.
**13**
**Figure 4.**
*Visible Evolution from Primitive Organisms to* Homo sapiens
*DOI: http://dx.doi.org/10.5772/intechopen.91170*
**6. Homogeneity of genome structure**
*respectively, to compare with data based on amino acid analysis.*
**Figure 3.**
Each gene has its characteristic amino acid or nucleotide sequence, and its amino acid or nucleotide composition differs not only in inter-species but also in intraspecies. Conversely, gene assemblies encoding 3000–7000 amino acid
*Radar charts of amino acid compositions calculated from various units of the complete genome of*
Methanobacterium thermoautotrophicum*. (A) The complete genome structure of* M. thermoautotrophicum *(B) radar charts of amino acid compositions calculated from the complete genome, and (C) from various units. The complete genome, comprising 1869 protein genes, was divided into 10 or 20 units. Ten units (1–10); based on 186 and 195 genes, half size units (1-H–9-H); based on 93 genes, single genes (1-F–9-F); based on the first single gene of each unit. Glutamine and asparagine were calculated as glutamic acid and aspartic acid, respectively, and tryptophan (<1%) was omitted in the radar charts [18]. This figure was adapted from Sorimachi [36].*
*Radar charts of cellular and genomic amino acid compositions. Values are expressed as the percentages of total amino acids.* Pyrococcus horikoshii *was examined. The cellular amino acid composition was obtained from three independent analyses. In genomic calculations, Gln and Asn were also incorporated into Glu and Asp,*
*Visible Evolution from Primitive Organisms to* Homo sapiens *DOI: http://dx.doi.org/10.5772/intechopen.91170*
**Figure 3.**
*Cheminformatics and Its Applications*
**Figure 2.**
"amino acid world" [21] seems a better fit for primitive life forms rather than the "RNA world." There are several hypotheses for codon formation [27–29], but
*Computational amino acid compositions of an* Ureaplasma urealyticum *gene. Upper panel: random choice of amino acids was carried out in the original gene (5005 amino acid pool). Lower panel: random choice of nucleotides was carried out in the original gene (15,018 nucleotides). In the simulation using nucleotides, the stop codon and Trp were discarded from the calculation of amino acid compositions, and a triplet formed was immediately counted as an amino acid. This figure was adapted from Sorimachi and Okayasu [25].*
According to our simulation analyses [25], proteins that were components of primitive life forms might reflect the free amino acid concentrations on the primitive Earth. As shown in **Figure 1**, the cellular basic amino acid composition, the "star-shape," is characterized by comparatively high concentrations of hydrophobic amino acids, such as valine, leucine, and isoleucine. The glycine and alanine contents were also comparatively high. The former might contribute to self-aggregation of proteins via hydrophobicity to form primitive life forms under low protein concentrations, and the latter might reflect their easy formation on the primitive Earth. In fact, simple amino acids such as glycine and alanine have been identified in meteorites [30, 31] and can be formed by electrical discharge in an atmosphere presumed to reflect primitive Earth [32]. Conversely, the phenylalanine, tryptophan, and tyrosine content, which can absorb ultraviolet light, were quite low. Strong ultraviolet irradiation might induce photodegradation of these amino acids. The differences in amino acid contents in cellular amino acid compositions seem to reflect the presumed free amino acid concentrations on the primitive Earth and eventually resulted in the formation of the "star-shaped" cellular amino
the process of codon formation has not yet been determined.
**5. Amino acid compositions deduced from complete genomes**
Initially, amino acid compositions were deduced from complete genomes by assuming that each gene is equally expressed in a whole cell [21]. This resulted in the amino acid composition deduced from the complete genome resembling the cellular amino acid composition obtained from the amino acid analyses of cell lysates [21], as shown in **Figure 3**. This coincidence is difficult to understand because of the different origins of both values, until the genome structure has been clarified, as
**12**
acid compositions (**Figure 1**).
shown in the next section.
*Radar charts of cellular and genomic amino acid compositions. Values are expressed as the percentages of total amino acids.* Pyrococcus horikoshii *was examined. The cellular amino acid composition was obtained from three independent analyses. In genomic calculations, Gln and Asn were also incorporated into Glu and Asp, respectively, to compare with data based on amino acid analysis.*
## **6. Homogeneity of genome structure**
Each gene has its characteristic amino acid or nucleotide sequence, and its amino acid or nucleotide composition differs not only in inter-species but also in intraspecies. Conversely, gene assemblies encoding 3000–7000 amino acid
#### **Figure 4.**
*Radar charts of amino acid compositions calculated from various units of the complete genome of* Methanobacterium thermoautotrophicum*. (A) The complete genome structure of* M. thermoautotrophicum *(B) radar charts of amino acid compositions calculated from the complete genome, and (C) from various units. The complete genome, comprising 1869 protein genes, was divided into 10 or 20 units. Ten units (1–10); based on 186 and 195 genes, half size units (1-H–9-H); based on 93 genes, single genes (1-F–9-F); based on the first single gene of each unit. Glutamine and asparagine were calculated as glutamic acid and aspartic acid, respectively, and tryptophan (<1%) was omitted in the radar charts [18]. This figure was adapted from Sorimachi [36].*
residues show very similar amino acid compositions [33] and nucleotide compositions [34] in intraspecies examinations. Consistent results were obtained from whole chromosomes consisting of putative small units of 3000–7000 amino acid residues [33]. Additionally, it has been shown mathematically that 3000–7000 amino acid residues represent the amino acid composition of a certain amino acid pool [35]. Thus, genome structure, which is constructed homogeneously from putative similar small units, can be represented by a "pearl-necklace," as shown in **Figure 4**. The fact that the structure of a genome is homogeneously constructed with putative similar small units indicates that micro-alterations of nucleotide sequences are canceled out within the small unit and that the small unit represents the whole genome characteristics. Macro-alterations represented by the small unit, and based on species differences, occur synchronously over the genome [33]. This conclusion has never been obtained from the analysis of nucleotide or amino acid sequences of actual genes. Based on these results, the ratios of amino acids to the total amino acids or those of nucleotides to the total nucleotides form useful indices for characterizing a genome whose nucleotide numbers differ among species.
| doab | 2025-04-07T04:13:04.415465 | 20-4-2021 18:19 | {
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"section_idx": 25
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ffe82432-4883-4adc-b03b-937c1baf5090.26 | **7. Nucleotide compositions**
As described above, the intraspecies rule of nucleotide composition was reported by Chargaff in 1950, as the first parity rule [12], and a similar parity rule regarding the single DNA strand was reported by the same group in 1968, as the second parity rule [14]. Using the normalized values to 1 (G + C + T + A = 1), the following relationships are obtained: G = C, T = A, and [(G + A) = (C + T)]. Recently, Mitchell and Bridge [16] reported that Chargaff 's second parity rule is applicable to a single DNA strand comprising a double-stranded DNA, based on many complete genome data among various species. Conversely, we showed that chloroplast and plant mitochondrial DNA and nuclear DNA obey Chargaff 's second parity rule as an inter-species rule [37], and that the second parity rule was applicable to the nucleotide relationships not only in the coding region, but also in non-coding regions compared with those of the complete single DNA strand [37, 38]. When invertebrate mitochondrial DNA is classified into two groups, high C/G and low C/G ratios, nucleotide content relationships may be expressed by linear formulae [37]. However, organellar DNA deviated from Chargaff 's second parity rule and nucleotide relationships were heteroskedastic [16, 39, 40]. The fact that all regression lines based on different kingdoms closed at the same single point suggests that all species descended from a single origin [41]. This is the first demonstration based on scientific evidence that all species were descended from a single origin of life. This concept has been presumed since Darwin's theory "Origin of Species" was published in 1859. Charles Darwin discussed evolution over the course of generations via the presence of "Natural Selection" in "On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life"; however, he discussed neither "a single origin" nor "a common ancestor" of species. The two regression lines of nucleotide relationships based on coding and non-coding regions closed to form a wedge-shape, because both fragments exist on the same DNA strand [37]. Similarly, the two regression lines based on chloroplast and plant mitochondrial DNA also closed to form a wedge-shape [37]. Thus, both organellar DNA independently descended from the same origin in biological evolution. Quite recently, it has been shown that vertebrates are descended from a certain
**15**
**Figure 5.**
*from Sorimachi [36].*
*Visible Evolution from Primitive Organisms to* Homo sapiens
phenotypic expressions over a 3.5-billion-year period.
invertebrate [42]. However, although the phylogenetic trees [7–11] have an apparent single origin, these "facts" are merely mathematical calculation results.
Chargaff's parity rules were originally based on intraspecies phenomena [12, 14], and the rules are applicable to inter-species evolutionary phenomena for nuclear, chloroplast, and plant mitochondria as mentioned above. The rules are represented by the following equations: G = C, T = A, [(G + A) = (C + T)]. As all values are normalized to 1, Chargaff's parity rule can also be represented as: 2G + 2A = 1, A = 0.5 – G, T = 0.5 – G, C = G, G = (G). The lines G and C overlap and the lines A and T overlap, and the former is line symmetrical to the latter against the line y = 0.25, as shown in **Figure 5**. These equations mean that four nucleotide contents can be expressed by just one nucleotide content using regression lines (**Figure 5**), and the two duplicate nucleotide contents (G or C and T or A) are symmetrical. Thus, the four nucleotide contents (two duplicate points) move strictly on the diagonal of 0.5 of a square in nuclear, chloroplast, and mitochondrial DNA, which obey Chargaff's second parity rule. Therefore, biological evolution caused by nucleotide alterations is expressed on the diagonal of a 0.5 square: the "diagonal genome universe" [36], although biological evolution shows a wide spectrum of
*The "Diagonal Genome Universe." Plotting four nucleotide contents normalized to 1 against certain nucleotide content (i.e., G or C content), G and C contents are expressed by (G = G) and (G = C), respectively, and T and A contents are expressed by (T = 0.5 − G) and (A = 0.5 − G), respectively. For example, if G = 0.1 (white dashed line), C = 0.1, T = 0.4, and A = 0.4. White open square, A or T; pink closed square, C or G. The white dotted line represents the line of symmetry (y = 0.25). Similarly, plotting nucleotide contents against T or A content, (T = T), (T = A), (C = 0.5 – T or A), and (G = 0.5 − T or A) are obtained. This figure was adapted*
*DOI: http://dx.doi.org/10.5772/intechopen.91170*
**8. Diagonal genome universe**
invertebrate [42]. However, although the phylogenetic trees [7–11] have an apparent single origin, these "facts" are merely mathematical calculation results.
| doab | 2025-04-07T04:13:04.415852 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.27 | **8. Diagonal genome universe**
*Cheminformatics and Its Applications*
numbers differ among species.
**7. Nucleotide compositions**
residues show very similar amino acid compositions [33] and nucleotide compositions [34] in intraspecies examinations. Consistent results were obtained from whole chromosomes consisting of putative small units of 3000–7000 amino acid residues [33]. Additionally, it has been shown mathematically that 3000–7000 amino acid residues represent the amino acid composition of a certain amino acid pool [35]. Thus, genome structure, which is constructed homogeneously from putative similar small units, can be represented by a "pearl-necklace," as shown in **Figure 4**. The fact that the structure of a genome is homogeneously constructed with putative similar small units indicates that micro-alterations of nucleotide sequences are canceled out within the small unit and that the small unit represents the whole genome characteristics. Macro-alterations represented by the small unit, and based on species differences, occur synchronously over the genome [33]. This conclusion has never been obtained from the analysis of nucleotide or amino acid sequences of actual genes. Based on these results, the ratios of amino acids to the total amino acids or those of nucleotides to the total nucleotides form useful indices for characterizing a genome whose nucleotide
As described above, the intraspecies rule of nucleotide composition was reported by Chargaff in 1950, as the first parity rule [12], and a similar parity rule regarding the single DNA strand was reported by the same group in 1968, as the second parity rule [14]. Using the normalized values to 1 (G + C + T + A = 1), the following relationships are obtained: G = C, T = A, and [(G + A) = (C + T)]. Recently, Mitchell and Bridge [16] reported that Chargaff 's second parity rule is applicable to a single DNA strand comprising a double-stranded DNA, based on many complete genome data among various species. Conversely, we showed that chloroplast and plant mitochondrial DNA and nuclear DNA obey Chargaff 's second parity rule as an inter-species rule [37], and that the second parity rule was applicable to the nucleotide relationships not only in the coding region, but also in non-coding regions compared with those of the complete single DNA strand [37, 38]. When invertebrate mitochondrial DNA is classified into two groups, high C/G and low C/G ratios, nucleotide content relationships may be expressed by linear formulae [37]. However, organellar DNA deviated from Chargaff 's second parity rule and nucleotide relationships were heteroskedastic [16, 39, 40]. The fact that all regression lines based on different kingdoms closed at the same single point suggests that all species descended from a single origin [41]. This is the first demonstration based on scientific evidence that all species were descended from a single origin of life. This concept has been presumed since Darwin's theory "Origin of Species" was published in 1859. Charles Darwin discussed evolution over the course of generations via the presence of "Natural Selection" in "On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life"; however, he discussed neither "a single origin" nor "a common ancestor" of species. The two regression lines of nucleotide relationships based on coding and non-coding regions closed to form a wedge-shape, because both fragments exist on the same DNA strand [37]. Similarly, the two regression lines based on chloroplast and plant mitochondrial DNA also closed to form a wedge-shape [37]. Thus, both organellar DNA independently descended from the same origin in biological evolution. Quite recently, it has been shown that vertebrates are descended from a certain
**14**
Chargaff's parity rules were originally based on intraspecies phenomena [12, 14], and the rules are applicable to inter-species evolutionary phenomena for nuclear, chloroplast, and plant mitochondria as mentioned above. The rules are represented by the following equations: G = C, T = A, [(G + A) = (C + T)]. As all values are normalized to 1, Chargaff's parity rule can also be represented as: 2G + 2A = 1, A = 0.5 – G, T = 0.5 – G, C = G, G = (G). The lines G and C overlap and the lines A and T overlap, and the former is line symmetrical to the latter against the line y = 0.25, as shown in **Figure 5**. These equations mean that four nucleotide contents can be expressed by just one nucleotide content using regression lines (**Figure 5**), and the two duplicate nucleotide contents (G or C and T or A) are symmetrical. Thus, the four nucleotide contents (two duplicate points) move strictly on the diagonal of 0.5 of a square in nuclear, chloroplast, and mitochondrial DNA, which obey Chargaff's second parity rule. Therefore, biological evolution caused by nucleotide alterations is expressed on the diagonal of a 0.5 square: the "diagonal genome universe" [36], although biological evolution shows a wide spectrum of phenotypic expressions over a 3.5-billion-year period.
#### **Figure 5.**
*The "Diagonal Genome Universe." Plotting four nucleotide contents normalized to 1 against certain nucleotide content (i.e., G or C content), G and C contents are expressed by (G = G) and (G = C), respectively, and T and A contents are expressed by (T = 0.5 − G) and (A = 0.5 − G), respectively. For example, if G = 0.1 (white dashed line), C = 0.1, T = 0.4, and A = 0.4. White open square, A or T; pink closed square, C or G. The white dotted line represents the line of symmetry (y = 0.25). Similarly, plotting nucleotide contents against T or A content, (T = T), (T = A), (C = 0.5 – T or A), and (G = 0.5 − T or A) are obtained. This figure was adapted from Sorimachi [36].*
| doab | 2025-04-07T04:13:04.416227 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.28 | **9. Codon evolution**
The 20 amino acids are encoded by genes using nucleotide triplets; therefore, these sequences are determined according to triplet sequences. Additionally, amino acid sequences differ not only inter-gene but also intraspecies. These facts indicate that a comparison of codon evolution based on the complete genome, which comprises large numbers of different genes, would not be significant. Indeed, no clear evaluation has been obtained, despite the attempted explanations of many scientists [27–29]. However, as described in the previous section, it has been clarified that a whole genome is constructed from putative small units that encode proteins of similar amino acid composition. This suggests that the total codon usage deduced from the complete genome is stable and represents the whole genome characteristic. According to this concept, correlationships of nucleotide contents in a complete genome can be expressed by the linear formula, y = ax + b; where "y" and "x" are nucleotide contents, and "a" and "b" are constant values. In addition, as each codon usage is expressed by a linear formula among various organisms, the determination of any one nucleotide content in certain organism can essentially estimate other three nucleotide contents and, therefore, the 64 codon usages (**Figure 6**). The estimated codon usage patterns and amino acid compositions are almost the same between the original experimental results and estimated results. The codon usage patterns clearly indicate that codon usages changed synchronously among the 64 codons during biological evolution.
#### **Figure 6.**
*Codon usage patterns and amino acid compositions of* Homo sapience*. Codon usage (bar) and amino acid composition (radar chart) are expressed as a percent of total codons and amino acids, respectively. Upper and lower panels represent genomic and estimated data, respectively. This figure was reproduced from Sorimachi and Okayasu [38].*
| doab | 2025-04-07T04:13:04.416310 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.29 | **10. Natural selection in biological evolution based on amino acid contents**
The above mentioned theories have been described in previous review articles [36, 43]; therefore, in this section, unique applications based on the amino acid compositions or nucleotide contents in the construction of phylogenetic trees to study evolution are presented using recent data.
The theory of natural selection was promoted by Charles Darwin and Alfred Wallace 150 years ago. This theory was derived from specific differences or similarities in the phenotypes of organisms that lived on geologically isolated islands.
**17**
**Figure 7.**
*Phylogenetic tree generated using Ward's cluster analysis method [48] from the predicted amino acid*
*vertebrates (red). This figure first appeared in Ref. [49] and is reproduced with permission.*
*composition of the complete mitochondrial genomes of 26 invertebrates (blue), 3 hemichordates (black), and 63*
*Visible Evolution from Primitive Organisms to* Homo sapiens
*DOI: http://dx.doi.org/10.5772/intechopen.91170*
*Cheminformatics and Its Applications*
codons during biological evolution.
**10. Natural selection in biological evolution based on amino**
study evolution are presented using recent data.
The above mentioned theories have been described in previous review articles [36, 43]; therefore, in this section, unique applications based on the amino acid compositions or nucleotide contents in the construction of phylogenetic trees to
*Codon usage patterns and amino acid compositions of* Homo sapience*. Codon usage (bar) and amino acid composition (radar chart) are expressed as a percent of total codons and amino acids, respectively. Upper and lower panels represent genomic and estimated data, respectively. This figure was reproduced from Sorimachi*
The theory of natural selection was promoted by Charles Darwin and Alfred Wallace 150 years ago. This theory was derived from specific differences or similarities in the phenotypes of organisms that lived on geologically isolated islands.
The 20 amino acids are encoded by genes using nucleotide triplets; therefore, these sequences are determined according to triplet sequences. Additionally, amino acid sequences differ not only inter-gene but also intraspecies. These facts indicate that a comparison of codon evolution based on the complete genome, which comprises large numbers of different genes, would not be significant. Indeed, no clear evaluation has been obtained, despite the attempted explanations of many scientists [27–29]. However, as described in the previous section, it has been clarified that a whole genome is constructed from putative small units that encode proteins of similar amino acid composition. This suggests that the total codon usage deduced from the complete genome is stable and represents the whole genome characteristic. According to this concept, correlationships of nucleotide contents in a complete genome can be expressed by the linear formula, y = ax + b; where "y" and "x" are nucleotide contents, and "a" and "b" are constant values. In addition, as each codon usage is expressed by a linear formula among various organisms, the determination of any one nucleotide content in certain organism can essentially estimate other three nucleotide contents and, therefore, the 64 codon usages (**Figure 6**). The estimated codon usage patterns and amino acid compositions are almost the same between the original experimental results and estimated results. The codon usage patterns clearly indicate that codon usages changed synchronously among the 64
**9. Codon evolution**
**16**
**acid contents**
**Figure 6.**
*and Okayasu [38].*
#### **Figure 7.**
*Phylogenetic tree generated using Ward's cluster analysis method [48] from the predicted amino acid composition of the complete mitochondrial genomes of 26 invertebrates (blue), 3 hemichordates (black), and 63 vertebrates (red). This figure first appeared in Ref. [49] and is reproduced with permission.*
The theory of biological evolution has been further developed by paleontology [44], using phenotypic changes in fossils, and by molecular biology [6], using genotypic modifications (nucleotides or amino acids) of genes in living organisms.
Generally, the nucleotide or amino acid sequences of a particular gene or genes have been the focus of biological evolution studies, and many phylogenetic trees have been constructed using nucleotide or amino acid sequences [7–11, 27, 29, 45]. Conversely, the amino acid compositions or nucleotide contents have been rarely used for whole genome research. However, these indices have been used to classify bacteria, archaea, and eukaryotes [46] and recently vertebrate evolution [47]. In those studies, all organisms could be classified into two types, "GC-rich" and "AT-rich," and the vertebrates examined were further classified into two groups: terrestrial and aquatic vertebrates, based on natural selection. A similar result was obtained from an analysis based on 16S rRNA sequences [45, 47].
When the normalized amino acid compositions of vertebrate and invertebrate complete mitochondrial genomes were used, the groups were separated cleanly into two large clusters, vertebrates and invertebrates (**Figure 7**). In invertebrates, starfish (Echinodermata) formed a small cluster, and squids and octopus (Mollusca) were grouped into the same cluster. Vertebrates were further classified into three major clusters, mammals, fish, and a mixture of reptiles and amphibians. For example, primates (human, chimpanzee, and gorilla) formed a small cluster. Thus,
#### **Figure 8.**
*Phylogenetic tree of complete vertebrate mitochondrial genomes based on cluster analysis [51] using amino acid compositions as the trait. Green and blue characters represent terrestrial and aquatic vertebrates, respectively. This figure was adapted from Sorimachi et al. [47].*
**19**
**Figure 9.**
*figure was adapted from Sorimachi et al. [47].*
*Visible Evolution from Primitive Organisms to* Homo sapiens
close species fell into the same cluster and did not split into different clusters. These results indicate that the normalized values of amino acid and nucleotide contents calculated from complete genomes could be used to characterize organisms and to construct phylogenetic trees. Our results based on complete mitochondrial genomes revealed that hemichordates (*Balanoglossus carnosus* and *Saccoglossus kowalevskii*) and *Xenoturbella bocki*, which were classified into the low G/C content invertebrates group, were closer to vertebrates than to invertebrates [49]. Protists (*Monosiga brevicollis*) and cephalochordate (*Branchiostoma belcheri*) were classified into the
In a previous study to classify vertebrates [49, 50], as organisms were chosen at random without any preposition, it was difficult to evaluate whether the classification results were reasonable in the phylogenetic trees. Using the amino acid composition as the trait, the vertebrates examined were separated into two major clusters (**Figure 8**), terrestrial and aquatic vertebrates. The exceptions were the hagfish (*Eptatretus burgeri*), which fell into the terrestrial vertebrate cluster, and the black spotted frog (*Rana nigromaculata*), which clustered with the aquatic vertebrates [47]. The clustering of the
*Phylogenetic tree of 16S rRNA. The phylogenetic tree was constructed by the neighbor-joining method [48] using nucleotide sequences. Green and blue characters represent terrestrial and aquatic vertebrates, respectively. This*
low G/C and high G/C content invertebrate groups, respectively [49].
*DOI: http://dx.doi.org/10.5772/intechopen.91170*
### *Visible Evolution from Primitive Organisms to* Homo sapiens *DOI: http://dx.doi.org/10.5772/intechopen.91170*
*Cheminformatics and Its Applications*
The theory of biological evolution has been further developed by paleontology [44], using phenotypic changes in fossils, and by molecular biology [6], using genotypic modifications (nucleotides or amino acids) of genes in living organisms. Generally, the nucleotide or amino acid sequences of a particular gene or genes have been the focus of biological evolution studies, and many phylogenetic trees have been constructed using nucleotide or amino acid sequences [7–11, 27, 29, 45]. Conversely, the amino acid compositions or nucleotide contents have been rarely used for whole genome research. However, these indices have been used to classify bacteria, archaea, and eukaryotes [46] and recently vertebrate evolution [47]. In those studies, all organisms could be classified into two types, "GC-rich" and "AT-rich," and the vertebrates examined were further classified into two groups: terrestrial and aquatic vertebrates, based on natural selection. A similar result was
When the normalized amino acid compositions of vertebrate and invertebrate complete mitochondrial genomes were used, the groups were separated cleanly into two large clusters, vertebrates and invertebrates (**Figure 7**). In invertebrates, starfish (Echinodermata) formed a small cluster, and squids and octopus (Mollusca) were grouped into the same cluster. Vertebrates were further classified into three major clusters, mammals, fish, and a mixture of reptiles and amphibians. For example, primates (human, chimpanzee, and gorilla) formed a small cluster. Thus,
*Phylogenetic tree of complete vertebrate mitochondrial genomes based on cluster analysis [51] using amino acid compositions as the trait. Green and blue characters represent terrestrial and aquatic vertebrates, respectively.*
obtained from an analysis based on 16S rRNA sequences [45, 47].
**18**
**Figure 8.**
*This figure was adapted from Sorimachi et al. [47].*
close species fell into the same cluster and did not split into different clusters. These results indicate that the normalized values of amino acid and nucleotide contents calculated from complete genomes could be used to characterize organisms and to construct phylogenetic trees. Our results based on complete mitochondrial genomes revealed that hemichordates (*Balanoglossus carnosus* and *Saccoglossus kowalevskii*) and *Xenoturbella bocki*, which were classified into the low G/C content invertebrates group, were closer to vertebrates than to invertebrates [49]. Protists (*Monosiga brevicollis*) and cephalochordate (*Branchiostoma belcheri*) were classified into the low G/C and high G/C content invertebrate groups, respectively [49].
In a previous study to classify vertebrates [49, 50], as organisms were chosen at random without any preposition, it was difficult to evaluate whether the classification results were reasonable in the phylogenetic trees. Using the amino acid composition as the trait, the vertebrates examined were separated into two major clusters (**Figure 8**), terrestrial and aquatic vertebrates. The exceptions were the hagfish (*Eptatretus burgeri*), which fell into the terrestrial vertebrate cluster, and the black spotted frog (*Rana nigromaculata*), which clustered with the aquatic vertebrates [47]. The clustering of the
#### **Figure 9.**
*Phylogenetic tree of 16S rRNA. The phylogenetic tree was constructed by the neighbor-joining method [48] using nucleotide sequences. Green and blue characters represent terrestrial and aquatic vertebrates, respectively. This figure was adapted from Sorimachi et al. [47].*
hagfish (*E. burgeri*) with the terrestrial vertebrates may reflect the controversy over the classification of this fish [52]. If the hagfish truly belongs to the terrestrial group, it suggests that hagfish still possesses some primitive mitochondrial characteristics that were present before its evolution. The frog (*R. nigromaculata*) was consistently grouped with the aquatic vertebrates which may reflect the conservation of tadpole characteristics after metamorphosis. The coelacanth (*Latimeria chalumnae*), the Queensland lungfish (*Neoceratodus forsteri*), which is a living fossil and one of the oldest living vertebrate genera, and the American paddlefish (*Polyodon spathula*), which is the oldest living animal species in North America, all belonged to an additional small cluster. Using the G, C, A, and T content of the coding regions, non-coding regions, and complete mitochondrial genomes as the traits in cluster analyses, similar results were obtained, but with some additional exceptions [50].
Single genes have been used to construct phylogenetic trees [7–11], and 16S rRNA has been frequently examined [27, 29]. The phylogenetic tree based on 16S rRNA sequences of various vertebrates is shown in **Figure 9**. The tree is consistent with that based on nucleotide contents. The hagfish (*E. burgeri*) fell into the terrestrial vertebrates, while the black spotted frog (*R. nigromaculata*) belonged to the terrestrial vertebrates. These results indicate that vertebrate evolution is controlled by natural selection under both an internal bias resulting nucleotide replacement rules and by an external bias caused by environmental biospheric conditions. In addition, based on amino acid composition or nucleotide content of complete mitochondrial genomes, Hemichordates (*Balanoglossus carnosus* and *Saccoglossus kowalevskii*) and Xenoturbella were classified into vertebrates not into invertebrates [49].
| doab | 2025-04-07T04:13:04.416457 | 20-4-2021 18:19 | {
"license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/3.0/",
"book_id": "ffe82432-4883-4adc-b03b-937c1baf5090",
"url": "https://mts.intechopen.com/storage/books/9236/authors_book/authors_book.pdf",
"author": "",
"title": "Cheminformatics and its Applications",
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ffe82432-4883-4adc-b03b-937c1baf5090.30 | **11. Organelle evolution**
In Chargaff's first parity rule [12], G = C and A = T in a double DNA strand, while in the second parity rule [14], G ≈ C and A ≈ T in a complete single DNA strand. Based on Chargaff's second parity rule, nucleotide content differences such as (G – C) and (A – T) reflect biological evolution. In addition, the other nucleotide content differences, (G – A, G – T, C – A, and C – T), also reflect biological evolution [34, 53].
Six nucleotide content differences among the complete mitochondria of the four species (*M. brevicollis, P. pallidum, D. discoideum*, and *R. Americana*) were examined (**Figure 10**, left panel). The GC and AT skew are expressed by the ratios of (G – C)/ (G + C) and (A – T)/(A + T), respectively [54]. The skew seems to be due to differences in replication processes between the leading and lagging strands [55]. In the replication of the lagging strand, the deamination of cytosine increases the probability of mutations, and the inversion of nucleotide content differences reflects biological divergence. Similarly, these phenomena are observed in mitochondria, consisting of heavy (H) and light (L) chains [56–58]. When the GC skew was plotted against G content, animal mitochondria were classified into two groups: high and low C/G [59].
To allow simple comparison of inter- and intraspecies genome structures, genomes were divided into three fragments throughout subsequent analyses, from which three separate patterns emerged. There is no inversion of nucleotide content differences that was observed in the mtDNA of *M. brevicollis* (G: 0.081, C: 0.059), the mycetozoan *Polysphondylium pallidum* (G: 0.143, C: 0.085), or *Dictyostelium discoideum* (G: 0.171, C: 0.104) (**Figure 10**), whereas differences in (G – C) and (T – A) values for *M. brevicollis* mtDNA were the lowest among these species. Choanoflagellates are most closely related to animals based on genome sequencing [60]. The fact that the nucleotide content difference patterns of the three fragments were almost identical for these three species indicates that their nucleotide distributions were homogeneous, and that the nucleotide content was symmetrical.
**21**
**Figure 10.**
*Visible Evolution from Primitive Organisms to* Homo sapiens
Based on these results, these mitochondria are likely to be primitive. Consistent results were obtained from Ward's clustering analysis using amino acid compositions predicted from complete mitochondrial genomes as traits [59]. Thus, the *M. brevicollis* mitochondrion is the most primitive among the three. Although the *Reclinomonas americana* mtDNA (G: 0.148, C: 0.114) has previously been proposed as a mitochondrial ancestor [61], AT inversion was observed in the third fragment. In addition, differences in (G – C) and (T – A) values in *R. americana* mtDNA were smaller than those in the mtDNA of the previous three organisms. The unsymmetrical nucleotide content causes significant differences in nucleotide content
*Nucleotide content differences in complete mitochondrial genomes (left side) and the three fragments of each mitochondrial genome (right side). Left to right: (G – C), (G – T), (G – A), (C – T), (C – A), and (T – A).*
*DOI: http://dx.doi.org/10.5772/intechopen.91170*
*Visible Evolution from Primitive Organisms to* Homo sapiens *DOI: http://dx.doi.org/10.5772/intechopen.91170*
*Cheminformatics and Its Applications*
obtained, but with some additional exceptions [50].
were classified into vertebrates not into invertebrates [49].
**11. Organelle evolution**
hagfish (*E. burgeri*) with the terrestrial vertebrates may reflect the controversy over the classification of this fish [52]. If the hagfish truly belongs to the terrestrial group, it suggests that hagfish still possesses some primitive mitochondrial characteristics that were present before its evolution. The frog (*R. nigromaculata*) was consistently grouped with the aquatic vertebrates which may reflect the conservation of tadpole characteristics after metamorphosis. The coelacanth (*Latimeria chalumnae*), the Queensland lungfish (*Neoceratodus forsteri*), which is a living fossil and one of the oldest living vertebrate genera, and the American paddlefish (*Polyodon spathula*), which is the oldest living animal species in North America, all belonged to an additional small cluster. Using the G, C, A, and T content of the coding regions, non-coding regions, and complete mitochondrial genomes as the traits in cluster analyses, similar results were
Single genes have been used to construct phylogenetic trees [7–11], and 16S rRNA has been frequently examined [27, 29]. The phylogenetic tree based on 16S rRNA sequences of various vertebrates is shown in **Figure 9**. The tree is consistent with that based on nucleotide contents. The hagfish (*E. burgeri*) fell into the terrestrial vertebrates, while the black spotted frog (*R. nigromaculata*) belonged to the terrestrial vertebrates. These results indicate that vertebrate evolution is controlled by natural selection under both an internal bias resulting nucleotide replacement rules and by an external bias caused by environmental biospheric conditions. In addition, based on amino acid composition or nucleotide content of complete mitochondrial genomes, Hemichordates (*Balanoglossus carnosus* and *Saccoglossus kowalevskii*) and Xenoturbella
In Chargaff's first parity rule [12], G = C and A = T in a double DNA strand, while
in the second parity rule [14], G ≈ C and A ≈ T in a complete single DNA strand. Based on Chargaff's second parity rule, nucleotide content differences such as (G – C) and (A – T) reflect biological evolution. In addition, the other nucleotide content differences, (G – A, G – T, C – A, and C – T), also reflect biological evolution [34, 53]. Six nucleotide content differences among the complete mitochondria of the four species (*M. brevicollis, P. pallidum, D. discoideum*, and *R. Americana*) were examined (**Figure 10**, left panel). The GC and AT skew are expressed by the ratios of (G – C)/ (G + C) and (A – T)/(A + T), respectively [54]. The skew seems to be due to differences in replication processes between the leading and lagging strands [55]. In the replication of the lagging strand, the deamination of cytosine increases the probability of mutations, and the inversion of nucleotide content differences reflects biological divergence. Similarly, these phenomena are observed in mitochondria, consisting of heavy (H) and light (L) chains [56–58]. When the GC skew was plotted against G content, animal mitochondria were classified into two groups: high and low C/G [59]. To allow simple comparison of inter- and intraspecies genome structures, genomes were divided into three fragments throughout subsequent analyses, from which three separate patterns emerged. There is no inversion of nucleotide content differences that was observed in the mtDNA of *M. brevicollis* (G: 0.081, C: 0.059), the mycetozoan *Polysphondylium pallidum* (G: 0.143, C: 0.085), or *Dictyostelium discoideum* (G: 0.171, C: 0.104) (**Figure 10**), whereas differences in (G – C) and (T – A) values for *M. brevicollis* mtDNA were the lowest among these species. Choanoflagellates are most closely related to animals based on genome sequencing [60]. The fact that the nucleotide content difference patterns of the three fragments were almost identical for these three species indicates that their nucleotide distributions were homogeneous, and that the nucleotide content was symmetrical.
**20**
| doab | 2025-04-07T04:13:04.416939 | 20-4-2021 18:19 | {
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"author": "",
"title": "Cheminformatics and its Applications",
"publisher": "IntechOpen",
"isbn": "9781838800680",
"section_idx": 30
} |
ffe82432-4883-4adc-b03b-937c1baf5090.31 | **Figure 10.**
*Nucleotide content differences in complete mitochondrial genomes (left side) and the three fragments of each mitochondrial genome (right side). Left to right: (G – C), (G – T), (G – A), (C – T), (C – A), and (T – A).*
Based on these results, these mitochondria are likely to be primitive. Consistent results were obtained from Ward's clustering analysis using amino acid compositions predicted from complete mitochondrial genomes as traits [59]. Thus, the *M. brevicollis* mitochondrion is the most primitive among the three. Although the *Reclinomonas americana* mtDNA (G: 0.148, C: 0.114) has previously been proposed as a mitochondrial ancestor [61], AT inversion was observed in the third fragment. In addition, differences in (G – C) and (T – A) values in *R. americana* mtDNA were smaller than those in the mtDNA of the previous three organisms. The unsymmetrical nucleotide content causes significant differences in nucleotide content
### *Cheminformatics and Its Applications*
patterns as a result of nucleotide content inversion. Judging from these results, the *R. americana* mitochondrion is probably more evolved than the former three mitochondria. In addition, AT inversion occurred in the following more highly evolved organisms: Mollusca species, squid (*Todarodes pacificus*), octopus (*Octopus vulgaris*), Echinodermata species, sea urchin (*Paracentrotus lividus*), water flea (*Daphnia pulex*), hermit crab (*Pagurus longicarpus*), and Humboldt squid (*Dosidicus gigas*) [53, 62]. In addition, large positive (G – A) values in the three fragments were observed in *Paragonimus westermani*, while large positive (G – C) and (A – T) values in the three fragments were observed for the mtDNA of representatives of the following phyla: Cnidaria (*Pavona clavus*), Platyhelminthes (*Schistosoma mansoni*), Porifera (*Geodia neptuni*), Arthropoda (*Tigriopus californicus*), and Chordata (*Branchiostoma belcheri*) [53]. Furthermore, the following invertebrate
**23**
*Visible Evolution from Primitive Organisms to* Homo sapiens
*carnosus*, and *Xenoturbella bocki* was examined [53].
mitochondria were also examined: *Acanthaster planci*, *Haliotis rubra*, *Lampsilis ornate,* and the mtDNA of hemichordates, *Saccoglossus kowalevskii, Balanoglossus*
In the mtDNA of primate species *H. sapiens*, *P. troglodytes*, *G. gorilla*, *Macaca mulatta*, *Daubentonia madagascariensis*, *Nycticebus coucang*, and *Tupaia belangeri*, nucleotide content difference patterns were quite similar in the first four species, and large positive increases in (C – T) differences in the three fragments clearly indicated evolutionary divergence (**Figure 11**). The positive (C – T) differences in all three fragments were characteristic of these four primate mitochondria, while positive increases in (C – T) values were only observed in the third fragment of *N. coucang* and *T. belangeri* mtDNA. In contrast, nucleotide content difference patterns of the prosimian *Lemur catta* completely differed from those of the primates, although TA inversion was observed in the second fragment. The primate mtDNA nucleotide content patterns were also completely different from that of hemichordate *B. carnosus*, although their C contents were the highest among all organisms examined [59]. This finding indicates that mitochondrial structures respect epig-
In the normalization of nucleotide contents (G + C + A + T = 1), as (G = C) and (A = T) based on Chargaff's parity rules, (2G + 2A = 1) is obtained. This equation is altered to (A = 0.5 – G) and then (A – G = 0.5 – 2G). Finally, G – A = 2G – 0.5. The relationship between (G – A) and (G) is linear when both (G) and (A) are expressed by linear functions. In animal mitochondria, only the correlations between the two purines (A versus G) or the two pyrimidines (C versus T) are linear, while the correlations between purines and pyrimidines (A or G versus T or C) are weak or not correlated at all [62]. For example, when plotting (G – C), (G – T), (G – A), (C – T), (C – A), and (T – C) against G content, only (G – A) versus G content was linear in vertebrate mitochondria [59]. In invertebrate mitochondria, plotting nucleotide
Plotting (X – Y)/(X + Y) against (X – Y), the following linear relationship was obtained in mitochondria, chloroplasts, and chromosomes (**Figure 12**): (X – Y)/ (X + Y) = a (X – Y) + b, where X and Y are nucleotide contents, and (a) and (b) are constants. As (b) was almost null and (a) was ~2.0, (X – Y)/(X + Y) ≈ 2.0 (X – Y). In these genome analyses, which are independent of Chargaff's parity rules, the values of (a) for (G, C), (G, A), (G, T), (C, T), (C, A), and (A, T) were 2.5858, 1.85558, 1.9908, 1.9771, 1.9968, and 1.5689, respectively, in our previous results [53, 54]. Based on these results, (G + C), (G + A), (G + T), (C + A), (C + T), and (A + T) were 0.39, 0.54, 0.50, 0.51, 0.50, and 0.64, respectively. In virus genome analyses [53, 54], the constant values for (a) were 1.9–2.1, and the values for (X + Y) were 0.47–0.53. In contrast, in the normalization of nucleotide contents (G + C + A + T = 1), as (G = C) and (A = T) based on Chargaff's parity rules, (2G + 2A = 1) is obtained. This equation is altered to (G + A = 0.5). This value is consistent with the value obtained above from genome analyses. Similarly, (G + T = 0.5), (C + A = 0.5), and (C + T = 0.5), although (G + C) and (A + T) cannot be determined. Therefore, the four nucleotide contents are expressed by the following regression lines, plotted against G content: A = 0.5 – G, T = 0.5 – G, C = G, and G = G. Lines G and C overlap, as do lines A and T, and the former line is symmetrical to the latter against line (y = 0.25). The intercepts of lines G and C are close to the origin, while those of lines A and T are close to 0.5 at the vertical and horizontal axes. All organisms from bacteria to *H. sapiens* are located on the
*DOI: http://dx.doi.org/10.5772/intechopen.91170*
enomic evolutionary functions.
**12. Definitive universal equations**
content differences against G content was weakly linear.
#### **Figure 11.**
*Nucleotide differences in the three fragments of each primate mitochondrial genome. Left to right: (G – C), (G – T), (G – A), (C – T), (C – A), and (T – A).*
*Visible Evolution from Primitive Organisms to* Homo sapiens *DOI: http://dx.doi.org/10.5772/intechopen.91170*
*Cheminformatics and Its Applications*
patterns as a result of nucleotide content inversion. Judging from these results, the *R. americana* mitochondrion is probably more evolved than the former three mitochondria. In addition, AT inversion occurred in the following more highly evolved organisms: Mollusca species, squid (*Todarodes pacificus*), octopus (*Octopus vulgaris*), Echinodermata species, sea urchin (*Paracentrotus lividus*), water flea (*Daphnia pulex*), hermit crab (*Pagurus longicarpus*), and Humboldt squid (*Dosidicus gigas*) [53, 62]. In addition, large positive (G – A) values in the three fragments were observed in *Paragonimus westermani*, while large positive (G – C) and (A – T) values in the three fragments were observed for the mtDNA of representatives of the following phyla: Cnidaria (*Pavona clavus*), Platyhelminthes (*Schistosoma mansoni*), Porifera (*Geodia neptuni*), Arthropoda (*Tigriopus californicus*), and Chordata (*Branchiostoma belcheri*) [53]. Furthermore, the following invertebrate
*Nucleotide differences in the three fragments of each primate mitochondrial genome. Left to right: (G – C),*
**22**
**Figure 11.**
*(G – T), (G – A), (C – T), (C – A), and (T – A).*
mitochondria were also examined: *Acanthaster planci*, *Haliotis rubra*, *Lampsilis ornate,* and the mtDNA of hemichordates, *Saccoglossus kowalevskii, Balanoglossus carnosus*, and *Xenoturbella bocki* was examined [53].
In the mtDNA of primate species *H. sapiens*, *P. troglodytes*, *G. gorilla*, *Macaca mulatta*, *Daubentonia madagascariensis*, *Nycticebus coucang*, and *Tupaia belangeri*, nucleotide content difference patterns were quite similar in the first four species, and large positive increases in (C – T) differences in the three fragments clearly indicated evolutionary divergence (**Figure 11**). The positive (C – T) differences in all three fragments were characteristic of these four primate mitochondria, while positive increases in (C – T) values were only observed in the third fragment of *N. coucang* and *T. belangeri* mtDNA. In contrast, nucleotide content difference patterns of the prosimian *Lemur catta* completely differed from those of the primates, although TA inversion was observed in the second fragment. The primate mtDNA nucleotide content patterns were also completely different from that of hemichordate *B. carnosus*, although their C contents were the highest among all organisms examined [59]. This finding indicates that mitochondrial structures respect epigenomic evolutionary functions.
| doab | 2025-04-07T04:13:04.417284 | 20-4-2021 18:19 | {
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"author": "",
"title": "Cheminformatics and its Applications",
"publisher": "IntechOpen",
"isbn": "9781838800680",
"section_idx": 31
} |
ffe82432-4883-4adc-b03b-937c1baf5090.32 | **12. Definitive universal equations**
In the normalization of nucleotide contents (G + C + A + T = 1), as (G = C) and (A = T) based on Chargaff's parity rules, (2G + 2A = 1) is obtained. This equation is altered to (A = 0.5 – G) and then (A – G = 0.5 – 2G). Finally, G – A = 2G – 0.5. The relationship between (G – A) and (G) is linear when both (G) and (A) are expressed by linear functions. In animal mitochondria, only the correlations between the two purines (A versus G) or the two pyrimidines (C versus T) are linear, while the correlations between purines and pyrimidines (A or G versus T or C) are weak or not correlated at all [62]. For example, when plotting (G – C), (G – T), (G – A), (C – T), (C – A), and (T – C) against G content, only (G – A) versus G content was linear in vertebrate mitochondria [59]. In invertebrate mitochondria, plotting nucleotide content differences against G content was weakly linear.
Plotting (X – Y)/(X + Y) against (X – Y), the following linear relationship was obtained in mitochondria, chloroplasts, and chromosomes (**Figure 12**): (X – Y)/ (X + Y) = a (X – Y) + b, where X and Y are nucleotide contents, and (a) and (b) are constants. As (b) was almost null and (a) was ~2.0, (X – Y)/(X + Y) ≈ 2.0 (X – Y). In these genome analyses, which are independent of Chargaff's parity rules, the values of (a) for (G, C), (G, A), (G, T), (C, T), (C, A), and (A, T) were 2.5858, 1.85558, 1.9908, 1.9771, 1.9968, and 1.5689, respectively, in our previous results [53, 54]. Based on these results, (G + C), (G + A), (G + T), (C + A), (C + T), and (A + T) were 0.39, 0.54, 0.50, 0.51, 0.50, and 0.64, respectively. In virus genome analyses [53, 54], the constant values for (a) were 1.9–2.1, and the values for (X + Y) were 0.47–0.53. In contrast, in the normalization of nucleotide contents (G + C + A + T = 1), as (G = C) and (A = T) based on Chargaff's parity rules, (2G + 2A = 1) is obtained. This equation is altered to (G + A = 0.5). This value is consistent with the value obtained above from genome analyses. Similarly, (G + T = 0.5), (C + A = 0.5), and (C + T = 0.5), although (G + C) and (A + T) cannot be determined. Therefore, the four nucleotide contents are expressed by the following regression lines, plotted against G content: A = 0.5 – G, T = 0.5 – G, C = G, and G = G. Lines G and C overlap, as do lines A and T, and the former line is symmetrical to the latter against line (y = 0.25). The intercepts of lines G and C are close to the origin, while those of lines A and T are close to 0.5 at the vertical and horizontal axes. All organisms from bacteria to *H. sapiens* are located on the
#### *Cheminformatics and Its Applications*
diagonal lines of a 0.5 square, termed the "Diagonal Genome Universe," using the normalized values that obey Chargaff's first parity rule [12]. These relationships lead to (G or C) + (A or T) = 0.5. The present results indicate that a linear regression line equation, (X – Y)/(X + Y) = a (X – Y) + b, universally represents all normalized values, including the values deviating from Chargaff's parity rules. This newly discovered equation clearly reflects not only Chargaff's first parity rules, based on hydrogen bonding between two nucleotides, but also natural rule.
#### **Figure 12.**
*Universal rules. The following genome samples were examined: mitochondria of vertebrates (65), invertebrates (54), and non-animals (42), chloroplasts (28), prokaryote chromosomes (21), and eukaryote chromosomes (15). Left side: relationship between (X – Y) and (X – Y)/(X + Y) and right side: relationship between (X/Y) and (X – Y)/(X + Y).*
**25**
**Acknowledgements**
computer analyses.
*Visible Evolution from Primitive Organisms to* Homo sapiens
A linear regression line was not obtained when using randomly chosen value (**Figure 12A**). Furthermore, plotting (X – Y)/(X + Y) against (X/Y), the following logarithmic function was obtained for all tested genomes as well as when using randomly chosen values (**Figure 12B**): (X – Y)/(X + Y) = a ln (X/Y) + b. As (b) was almost null and (a) was ~0.5, (X – Y)/(X + Y) ≈ 0.5 ln (X/Y). The ratio between two values, (X/Y), can be expressed by a logarithmic function, ~0.5 ln (X/Y) ≈ (X – Y)/(X + Y). Plotting the GC skew vs. G content, animal mitochondria were classified into two groups: high and low C/G [59]. This fact indicates that the ratio C/G and the GC skew are evolutionarily related to each other. Any change can be expressed universally by a definitive logarithmic function, (X – Y)/(X + Y) = a ln (X/Y) + b. The present results indicate that cellular organelle evolution is strictly controlled under these characteristic rules, although nonanimal mitochondria, chloroplasts, and chromosomes are controlled under Chargaff 's parity rules [12, 14]. The present study clearly shows that biological evolution, which seems to be based on complicated processes, is governed by
The ratios of amino acids to the total amino acids or of nucleotides to total nucleotides predicted from complete genomes consisting of huge number of nucleotides can characterize a whole organism. In addition, as these values are independent of species and genome size, these indexes are very useful for genome research, as well as single gene research. The validity of these indexes is clearly based on the homogeneity of genomic structures. In addition, patternalization of values after simple calculations based on large data sets can provide an intuitive picture and provide useful insights, revealing the homogeneity of genomic structures followed by synchronous alterations over the genome. In addition, any change between two values, X and Y, including biological evolution can be expressed definitively by a linear regression line equation, (X – Y)/ (X + Y) = a (X – Y) + b, where X and Y are nucleotide contents, and (a) and (b) are constants, and by a logarithmic function, (X – Y)/(X + Y) = a′ ln (X/Y) + b′, where (a′) and (b′) are constants. As the present review is based on the endeavors and data of numerous scientists from all over the world, the author would like to express finally his following feeling as one of scientists. (Human being is an organism of huge numbers of organisms on the Earth, and we are not ranked as a special species above all organisms as a result of long evolution.) However, we have made the present modern civilization based on fossil energy usage which seems to induce climate changes. Thus, we must be responsible to establish sustainable development not only for Human being but also for other organisms.
The author greatly acknowledges President Hiroyuki Okada of Shinko Sangyo, Co. Ltd., Takasaki, Gunma, Japan for his financial support and Dr. Teiji Okayasu who was one of collaborators in Dokkyo Medical University for his excellent
The Earth is for all organisms, not only for Human being.
*DOI: http://dx.doi.org/10.5772/intechopen.91170*
simple universal equations.
**13. Conclusions**
*Visible Evolution from Primitive Organisms to* Homo sapiens *DOI: http://dx.doi.org/10.5772/intechopen.91170*
A linear regression line was not obtained when using randomly chosen value (**Figure 12A**). Furthermore, plotting (X – Y)/(X + Y) against (X/Y), the following logarithmic function was obtained for all tested genomes as well as when using randomly chosen values (**Figure 12B**): (X – Y)/(X + Y) = a ln (X/Y) + b. As (b) was almost null and (a) was ~0.5, (X – Y)/(X + Y) ≈ 0.5 ln (X/Y). The ratio between two values, (X/Y), can be expressed by a logarithmic function, ~0.5 ln (X/Y) ≈ (X – Y)/(X + Y). Plotting the GC skew vs. G content, animal mitochondria were classified into two groups: high and low C/G [59]. This fact indicates that the ratio C/G and the GC skew are evolutionarily related to each other. Any change can be expressed universally by a definitive logarithmic function, (X – Y)/(X + Y) = a ln (X/Y) + b. The present results indicate that cellular organelle evolution is strictly controlled under these characteristic rules, although nonanimal mitochondria, chloroplasts, and chromosomes are controlled under Chargaff 's parity rules [12, 14]. The present study clearly shows that biological evolution, which seems to be based on complicated processes, is governed by simple universal equations.
| doab | 2025-04-07T04:13:04.417629 | 20-4-2021 18:19 | {
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"section_idx": 32
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ffe82432-4883-4adc-b03b-937c1baf5090.33 | **13. Conclusions**
*Cheminformatics and Its Applications*
diagonal lines of a 0.5 square, termed the "Diagonal Genome Universe," using the normalized values that obey Chargaff's first parity rule [12]. These relationships lead to (G or C) + (A or T) = 0.5. The present results indicate that a linear regression line equation, (X – Y)/(X + Y) = a (X – Y) + b, universally represents all normalized values, including the values deviating from Chargaff's parity rules. This newly discovered equation clearly reflects not only Chargaff's first parity rules, based on
*Universal rules. The following genome samples were examined: mitochondria of vertebrates (65), invertebrates (54), and non-animals (42), chloroplasts (28), prokaryote chromosomes (21), and eukaryote chromosomes (15). Left side: relationship between (X – Y) and (X – Y)/(X + Y) and right side: relationship between (X/Y) and*
hydrogen bonding between two nucleotides, but also natural rule.
**24**
**Figure 12.**
*(X – Y)/(X + Y).*
The ratios of amino acids to the total amino acids or of nucleotides to total nucleotides predicted from complete genomes consisting of huge number of nucleotides can characterize a whole organism. In addition, as these values are independent of species and genome size, these indexes are very useful for genome research, as well as single gene research. The validity of these indexes is clearly based on the homogeneity of genomic structures. In addition, patternalization of values after simple calculations based on large data sets can provide an intuitive picture and provide useful insights, revealing the homogeneity of genomic structures followed by synchronous alterations over the genome. In addition, any change between two values, X and Y, including biological evolution can be expressed definitively by a linear regression line equation, (X – Y)/ (X + Y) = a (X – Y) + b, where X and Y are nucleotide contents, and (a) and (b) are constants, and by a logarithmic function, (X – Y)/(X + Y) = a′ ln (X/Y) + b′, where (a′) and (b′) are constants. As the present review is based on the endeavors and data of numerous scientists from all over the world, the author would like to express finally his following feeling as one of scientists. (Human being is an organism of huge numbers of organisms on the Earth, and we are not ranked as a special species above all organisms as a result of long evolution.) However, we have made the present modern civilization based on fossil energy usage which seems to induce climate changes. Thus, we must be responsible to establish sustainable development not only for Human being but also for other organisms. The Earth is for all organisms, not only for Human being.
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ffe82432-4883-4adc-b03b-937c1baf5090.34 | **Acknowledgements**
The author greatly acknowledges President Hiroyuki Okada of Shinko Sangyo, Co. Ltd., Takasaki, Gunma, Japan for his financial support and Dr. Teiji Okayasu who was one of collaborators in Dokkyo Medical University for his excellent computer analyses.
*Cheminformatics and Its Applications*
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ffe82432-4883-4adc-b03b-937c1baf5090.35 | **Author details**
Kenji Sorimachi1,2
1 Educational Support Center, Dokkyo Medical University, Tochigi, Japan
2 Research Laboratories, Gunma Agriculture and Forest Development Com., Ltd., Takasaki, Gunma, Japan
\*Address all correspondence to: [email protected]
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
**27**
pp. 7-16
1986;**83**:1383-1387
*Visible Evolution from Primitive Organisms to* Homo sapiens
[9] Doolittle WF, Brown JR. Tempo, mode, the progenote, and the universal root. Proceedings of the National Academy of Sciences of the United States of America. 1994;**91**:6721-6728
[10] Maizels N, Weiner AM. Phylogeny
[11] DePouplana L, Turner RJ, Steer BA, Schimmel P. Genetic code origins: tRNAs older than their synthetases? Proceedings of the National Academy of Sciences of the United States of America. 1998;**95**:11295-11300
[12] Chargaff E. Chemical specificity of nucleic acids and mechanism of their enzymatic degradation. Experientia.
[13] Watson JD, Crick FHC. Genetical implications of the structure of deoxyribonucleic acid. Nature.
Chargaff E. Separation of *B. subtilis* DNA into complementary strands. 3. Direct analysis. Proceedings of the National Academy of Sciences of the United States of America.
[15] Sorimachi K. A proposed solution to the historic puzzle of Chargaff's second parity rule. Open Genomics Journal.
[17] Sueoka N. Correlation between base composition of deoxyribonucleic acid
[16] Mitchell D, Bridge R. A test of Chargaff's second rule. Biochemical and Biophysical Research Communications.
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from function: Evidence from the molecular fossil record that tRNA originated in replication, not translation. Proceedings of the National Academy of Sciences of the United States of America. 1994;**91**:6729-6734
*DOI: http://dx.doi.org/10.5772/intechopen.91170*
[1] Sanger F, Coulson AR. A rapid method for determining sequences in DNA by printed synthesis with DNA polymerase. Journal of Molecular
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[3] Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, et al. Whole-genome random sequencing and assembly of *Haemophilus influenzae* Rd. Science.
[4] Lander ES, Linton ML, Birren B, Nusbaum C, Zody MC, Baldwin J, et al.
[5] Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, et al. The sequence of the human genome.
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McLaughlin PJ. Building a phylogenetic trees: Cytochrome C. In: Dayhoff MO, editor. Atlas of Protein Sequence and Structure. Vol. 5. Washington, D.C.: National Biomedical Foundation; 1977.
[8] Sogin ML, Elwood HJ, Gunderson JH. Evolutionary diversity of eukaryotic small subunit rRNA genes. Proceedings of the National Academy of Sciences of the United States of America.
[6] Zuckerkandl E, Pauling LB. Molecular disease, evolution, and genetic heterogeneity. In: Kasha M, Pullman B, editors. Horizons in Biochemistry. New York: Academic
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*Visible Evolution from Primitive Organisms to* Homo sapiens *DOI: http://dx.doi.org/10.5772/intechopen.91170*
| doab | 2025-04-07T04:13:04.418005 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.37 | **Chapter 3**
*Cheminformatics and Its Applications*
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of the National Academy of Sciences of the United States of America.
[61] Andersson SG et al. The genome sequence of *Rickettsia prowazekii* and the origin of mitochondria. Nature.
[62] Sorimachi K. Codon evolution in double-stranded organelle DNA: Strong regulation of homonucleotides and their analog alternations. Natural Science.
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[53] Sorimachi K. The most primitive extant ancestor of organisms and discovery of definitive evolutionary equations based on complete genome structures. Natural Science.
[54] Lobry JR. Asymmetric substitution patterns in the two DNA strands of bacteria. Molecular Biology and Evolution. 1996;**13**:660-665
[55] Tillier ER, Collins RA. The contributions of replication orientation, gene direction, and signal sequences to base-composition asymmetries in bacterial genomes. Journal of Molecular Evolution.
[56] Anderson S et al. Sequence and organization of the human mitochondrial genome. Nature.
[57] Fonceca MM, Harris DJ,
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Posada D. The inversion of the control region in three mitogenomes pro vides further evidence for an asymmetric model of vertebrate mtDNA replication.
[58] Seligmann H. Coding constraints modulate chemically spontaneous mutational replication gradients in mitochondrial genomes. Current
[59] Sorimachi K. Origine of life in the ocean: Direct derivation of mitochondria from primitive organisms based on complete
genomes. Current Chemical Biology.
[60] King N et al. The genome of the choanoflagellates Monosigarevicollis and the origin of metazoans. Nature.
**30**
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ffe82432-4883-4adc-b03b-937c1baf5090.38 | Semantic Similarity in Cheminformatics
*João D. Ferreira and Francisco M. Couto*
| doab | 2025-04-07T04:13:04.418369 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.39 | **Abstract**
Similarity in chemistry has been applied to a variety of problems: to predict biochemical properties of molecules, to disambiguate chemical compound references in natural language, to understand the evolution of metabolic pathways, to predict drug-drug interactions, to predict therapeutic substitution of antibiotics, to estimate whether a compound is harmful, etc. While measures of similarity have been created that make use of the structural properties of the molecules, some ontologies (the Chemical Entities of Biological Interest (ChEBI) being one of the most relevant) capture chemistry knowledge in machine-readable formats and can be used to improve our notions of molecular similarity. Ontologies in the biomedical domain have been extensively used to compare entities of biological interest, a technique known as ontology-based semantic similarity. This has been applied to various biologically relevant entities, such as genes, proteins, diseases, and anatomical structures, as well as in the chemical domain. This chapter introduces the fundamental concepts of ontology-based semantic similarity, its application in cheminformatics, its relevance in previous studies, and future potential. It also discusses the existing challenges in this area, tracing a parallel with other domains, particularly genomics, where this technique has been used more often and for longer.
**Keywords:** semantic similarity, ontologies, ChEBI, prediction of molecule properties, databases
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ffe82432-4883-4adc-b03b-937c1baf5090.40 | **1. Introduction**
With the unprecedented amount of data being generated today, we must start (and in some cases have already started) to rely on automatic systems to process, analyse, and understand all the scientific information that we produce. For some examples in chemistry, consider the number of drugs represented in DrugBank, which grew from 3909 in 2006 to 9688 [1], about 13% each year; the number of metabolites in the Human Metabolite Database grew from 2180 in 2007 to 114,100 in 2017 [2], approximately 39% per year (although at some point this database imported a large number of metabolites at once, artificially increasing this statistic); ChemSpider had 25 million compounds in 2010 [3] and now has 63 million (10% a year); and PubChem grew from 19 million compound structures in 2008 [4] to 96.5 million in August 2018 [5] (16% a year). These numbers usually grow exponentially [6], reflecting the fact that the amount of knowledge the scientific community produces is proportional to the amount of knowledge we discover.
With such high volumes of data, it is imperative that we categorise this information in ways that assist us in the tasks of consuming that information, specifically through categorisation schemas that abstract away the less useful details of reality and increase the manageability of this information. As we will see later in this chapter, ontologies can perform that goal: they are computational artefacts (files, tables in a database, etc.) whose goal is to encode real-world knowledge in machinereadable logical axioms that can be used by automatic systems to manipulate the knowledge inferred and potentially derivable from the data we have.
Furthermore, like most other scientific knowledge, chemistry ideas and notions are inferred from comparing entities and finding their similarities and differences. For instance, compound similarity has been used to (i) develop pharmacophores [7, 8], (ii) estimate whether a compound is harmful without in vivo experimentation [9], (iii) understand the evolution of metabolic pathways [10], (iv) predict adverse side effects of drugs [11], and (v) perform pharmacological profiling of compounds in drug design [12].
As we explore in this chapter, ontologies provide one way to measure similarity of chemistry entities (compounds, substances, mixtures, reactions, etc.), a technique known as ontology-based semantic similarity (shortened to semantic similarity in this chapter). This idea is already widely used in genomics and proteomics, but its full potential still needs to be brought over to other domains. While some research has successfully used this methodology in the cheminformatics domain (which we discuss below), there is still space for improvement and further methodological development.
In this chapter, we explore the ideas and concepts behind semantic similarity and chemistry ontologies, explore some past applications that use those concepts to further our knowledge of the chemical domain, and expose some limitations and challenges that this technique still needs to overcome for its whole potential to be released.
### **2. Measures of similarity in chemistry**
Similarity, in its nature, is a notion that produces a number. In that sense, it is mathematical. However, chemical knowledge cannot be trivially reduced to mathematical form. For example, given two molecules, how should one compare them and assign a number to represent their similarity? And even if specific cases can be handled by humans, we still need an automatic way to perform comparison. However, to a certain extent, computers can only manipulate objects that can be represented mathematically (e.g., vectors) or as strings of characters (e.g., gene sequences, SMILES). But the algorithms that are used with these structures are context-free: they usually transform the structures without any knowledge of what they represent.
Many mechanisms exist to deal with this issue. For example, graph similarity can be used to find common substructures in two molecules as a basis for similarity calculations (see, e.g., [13, 14]), but these methods tend to be slow and computationally expensive. There is also the possibility to reduce a molecular structure into a *fingerprint*, which is a binary vector where each position represents the presence (with a 1) or absence (with a 0) of a certain feature in the structure. For example, the presence of a carboxyl group could be indicated with a 1 in some position of the vector. Similarity can then be computed by measuring the overlap in those vectors [15, 16].
These methods provide a high similarity value when the structures of the two molecules are high. Under the quantitative structure-activity relationship (QSAR)
**33**
**3. Ontologies**
*Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
**Figure 1.**
premise, this means that, in general, two molecules with a high similarity score (as defined by these methods) tend to have similar biological role, similar chemical properties (such as melting point, optical parameters, and mass spectroscopy spectra), similar safety warnings, similar appearance, etc. But this is not always true. For instance, while L-amino acids are used to synthesise proteins, D-amino acids are much less frequent in nature, and their role is quite different [17]. From a biological point of view, they are distinct; however, to capture their structural differences, one needs to use three-dimensional methods, and even with that consideration, the structural similarity will be high, because both molecules have the same atoms and bonds. Another possibility includes simulation of docking with target proteins, but these methods are quite expensive computationally. Furthermore, not only can similar molecules perform different biological roles, different molecules can perform similar roles. For example, both clavulanic acid and salsalate are *β*-lactamase
*Chemical structure of two semantically related compounds. The two molecular structures in the figure are quite different structures, and yet both present the same biological activity, namely, they inhibit β-lactamase enzymes.*
Another way to measure similarity is by means of the semantics attached to the chemical compounds. Here, we use the term *semantics* to mean the knowledge that exists about a compound. This includes not only the structure of the molecule itself (e.g., the atomic connectivity, the number of oxygen atoms, the presence of triple bonds) but also other types of contextual knowledge, such as its chemical role (e.g., whether it is an electron donor, a solvent, or an explosive), biological role (e.g., whether it is a poison, a cofactor, or a vitamin), its applications (as a drug, fertiliser, fuel, etc.), its relationship to other molecules (such as being enantiomers,
The difficulty with this is that knowledge is not directly machine-readable. Indeed, established facts have been traditionally published in plain text, which enables some humans to understand them; however, natural language processing techniques are not yet fully capable of converting scientific text into actionable formats (e.g., formats that allow automatic reasoning). Therefore, to enable the application of computerised processing power to knowledge manipulation, it is essential to find ways to represent knowledge in machine-readable formats.
Ontologies are the solution to this problem. An ontology is a representation of concepts from a domain of knowledge and the relationship between them and is usually visualised as a directed acyclic graph (DAG), where nodes are the concepts, edges are the relationships, and there are no cycles in the graph. See, for example,
inhibitors, despite their different structures (see **Figure 1**).
parent hydrides, etc.), and so on.
*Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
**Figure 1.**
*Cheminformatics and Its Applications*
compounds in drug design [12].
**2. Measures of similarity in chemistry**
ological development.
released.
they represent.
vectors [15, 16].
With such high volumes of data, it is imperative that we categorise this information in ways that assist us in the tasks of consuming that information, specifically through categorisation schemas that abstract away the less useful details of reality and increase the manageability of this information. As we will see later in this chapter, ontologies can perform that goal: they are computational artefacts (files, tables in a database, etc.) whose goal is to encode real-world knowledge in machinereadable logical axioms that can be used by automatic systems to manipulate the
Furthermore, like most other scientific knowledge, chemistry ideas and notions are inferred from comparing entities and finding their similarities and differences. For instance, compound similarity has been used to (i) develop pharmacophores [7, 8], (ii) estimate whether a compound is harmful without in vivo experimentation [9], (iii) understand the evolution of metabolic pathways [10], (iv) predict adverse side effects of drugs [11], and (v) perform pharmacological profiling of
As we explore in this chapter, ontologies provide one way to measure similarity of chemistry entities (compounds, substances, mixtures, reactions, etc.), a technique known as ontology-based semantic similarity (shortened to semantic similarity in this chapter). This idea is already widely used in genomics and proteomics, but its full potential still needs to be brought over to other domains. While some research has successfully used this methodology in the cheminformatics domain (which we discuss below), there is still space for improvement and further method-
In this chapter, we explore the ideas and concepts behind semantic similarity and chemistry ontologies, explore some past applications that use those concepts to further our knowledge of the chemical domain, and expose some limitations and challenges that this technique still needs to overcome for its whole potential to be
Similarity, in its nature, is a notion that produces a number. In that sense, it is mathematical. However, chemical knowledge cannot be trivially reduced to mathematical form. For example, given two molecules, how should one compare them and assign a number to represent their similarity? And even if specific cases can be handled by humans, we still need an automatic way to perform comparison. However, to a certain extent, computers can only manipulate objects that can be represented mathematically (e.g., vectors) or as strings of characters (e.g., gene sequences, SMILES). But the algorithms that are used with these structures are context-free: they usually transform the structures without any knowledge of what
Many mechanisms exist to deal with this issue. For example, graph similarity can be used to find common substructures in two molecules as a basis for similarity calculations (see, e.g., [13, 14]), but these methods tend to be slow and computationally expensive. There is also the possibility to reduce a molecular structure into a *fingerprint*, which is a binary vector where each position represents the presence (with a 1) or absence (with a 0) of a certain feature in the structure. For example, the presence of a carboxyl group could be indicated with a 1 in some position of the vector. Similarity can then be computed by measuring the overlap in those
These methods provide a high similarity value when the structures of the two molecules are high. Under the quantitative structure-activity relationship (QSAR)
knowledge inferred and potentially derivable from the data we have.
**32**
*Chemical structure of two semantically related compounds. The two molecular structures in the figure are quite different structures, and yet both present the same biological activity, namely, they inhibit β-lactamase enzymes.*
premise, this means that, in general, two molecules with a high similarity score (as defined by these methods) tend to have similar biological role, similar chemical properties (such as melting point, optical parameters, and mass spectroscopy spectra), similar safety warnings, similar appearance, etc. But this is not always true. For instance, while L-amino acids are used to synthesise proteins, D-amino acids are much less frequent in nature, and their role is quite different [17]. From a biological point of view, they are distinct; however, to capture their structural differences, one needs to use three-dimensional methods, and even with that consideration, the structural similarity will be high, because both molecules have the same atoms and bonds. Another possibility includes simulation of docking with target proteins, but these methods are quite expensive computationally. Furthermore, not only can similar molecules perform different biological roles, different molecules can perform similar roles. For example, both clavulanic acid and salsalate are *β*-lactamase inhibitors, despite their different structures (see **Figure 1**).
Another way to measure similarity is by means of the semantics attached to the chemical compounds. Here, we use the term *semantics* to mean the knowledge that exists about a compound. This includes not only the structure of the molecule itself (e.g., the atomic connectivity, the number of oxygen atoms, the presence of triple bonds) but also other types of contextual knowledge, such as its chemical role (e.g., whether it is an electron donor, a solvent, or an explosive), biological role (e.g., whether it is a poison, a cofactor, or a vitamin), its applications (as a drug, fertiliser, fuel, etc.), its relationship to other molecules (such as being enantiomers, parent hydrides, etc.), and so on.
The difficulty with this is that knowledge is not directly machine-readable. Indeed, established facts have been traditionally published in plain text, which enables some humans to understand them; however, natural language processing techniques are not yet fully capable of converting scientific text into actionable formats (e.g., formats that allow automatic reasoning). Therefore, to enable the application of computerised processing power to knowledge manipulation, it is essential to find ways to represent knowledge in machine-readable formats.
## **3. Ontologies**
Ontologies are the solution to this problem. An ontology is a representation of concepts from a domain of knowledge and the relationship between them and is usually visualised as a directed acyclic graph (DAG), where nodes are the concepts, edges are the relationships, and there are no cycles in the graph. See, for example,
**Figure 2**, a toy exampled based on a real-world ontology that encodes the fact that "acetate" is the conjugate base of "acetic acid" and that "acetic acid" is the conjugate acid of "acetate" and then organises these concepts in a hierarchy that contains concepts like "ion", "molecule", "organic acid", and "organic molecular entity", and ends up in the most generic "molecular entity" concept.
There are many ontologies whose purpose is to encode the chemical knowledge, but one of the most comprehensive and used is the ontology for Chemical Entities of Biological Interest (ChEBI) [18]. This ontology represents in a machine-readable format about 114 thousand concepts, including not only the chemical compounds but also their biological and chemical roles. Other ontologies that encode this or related domains include (*i*) Interlinking Ontology for Biological Concepts, (*ii*) Current Procedural Terminology, (*iii*) SNOMED CT, (*iv*) Chemical Information Ontology, and (*v*) Chemical Methods Ontology.
It is important to notice that, even though the notion of ontologies usually requires some logic concepts (such as axioms, predicates, etc.), some classification hierarchies are also sometimes named "ontologies". MeSH, the system used
#### **Figure 2.**
*A toy example of an ontology for chemical compounds, based on ChEBI. The ontology shows "is-a" relationships with solid lines, and a relationship between acid/base conjugates with a dotted line. The green shaded concepts are those that subsume both the yellow and the blue ones.*
**35**
*Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
events, with 3 thousand concepts).
**4. Semantic similarity**
extracted from the ontology.
System.
by PubMed to classify publications, is a hierarchy of concepts that possesses many of the same properties that ontologies do, namely, that it can be represented as a directed acyclic graph. However, one of the differences is that the relationship between two concepts does not always carry the same meaning. For example, "Head" is categorised under "Body Regions", and "Ear" is categorised under "Head", but while heads *are* body regions, ears *are not* heads; they are instead *parts* of the head. This illustrates the informality of MeSH: only one relationship type exists and it is used to express different notions. Another system in this category is the Anatomical Therapeutic Chemical (ATC) Classification
BioPortal [19], a repository of ontologies for the biomedical domain, contains a collection of 948 ontologies at the time of this writing. As an illustration of its magnitude, consider that 19 ontologies represent the concept "lidocaine". This reflects the effort being currently spent to represent human knowledge in machinereadable ontologies. In fact, while ontologies such as ChEBI are massive, BioPortal allows their users to submit new ontologies, even if small, focussed on a specific domain, and created with a specific application in mind other than pure knowledge representation (e.g., there is an ontology specific for cardiovascular drug adverse
Other efforts have been set into place to aggregate ontologies in a single source of knowledge. For example, the Open Biological and Biomedical Ontology (OBO) Foundry [20] developed the OBO file format to represent ontologies and currently defines principles of quality for ontologies in biomedical domain that prescribe good practices for ontology development, such as being open, being reusable, being developed with collaboration in mind, containing both textual and logical definitions (for the benefit of both humans and machines), etc. They contain more than 200 ontologies as of this writing, 10 of which fully adhere to those principles (ChEBI being one of them). The OBO Foundry is tightly coupled with Ontobee [21], a web service that uses the principles of linked data to serve as a linked data
Using a formal representation of knowledge, computers are given the ability to manipulate concepts that are difficult to represent, in a way that preserves their "semantics". Ontologies provide the appropriate support for automatic manipulation of information. In this context, semantic similarity is a technique that assigns a numeric value to a pair of concepts based on the similarity of their meaning,
For example, there is no directly obvious way to compare two roles. However, considering the illustration in **Figure 3**, it is possible to intuitively understand that, because both "hallucinogen" and "antifungal drug" are examples of "drugs", they are more similar than "hallucinogen" and "fossil fuel". This measure makes use of the meaning of the concepts, implicitly represented in the ontologies through the relations between the concepts. Ontologies function as a proxy for that meaning
Several formulas and ideas have been proposed, implemented and tested in the past to compute semantic similarity. A full exposition on such measures and algorithms is beyond the scope of this chapter. The reader is encouraged to expand on this topic by reading works such as [22–25]. As such, the following is an abridged version of how ontology-based semantic similarity has been computed. In this
server specifically targeted for ontologies and their concepts.
and enable its manipulation and ultimately comparison.
discussion, consider the ontology in **Figure 3**.
#### *Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
*Cheminformatics and Its Applications*
**Figure 2**, a toy exampled based on a real-world ontology that encodes the fact that "acetate" is the conjugate base of "acetic acid" and that "acetic acid" is the conjugate acid of "acetate" and then organises these concepts in a hierarchy that contains concepts like "ion", "molecule", "organic acid", and "organic molecular entity", and
There are many ontologies whose purpose is to encode the chemical knowledge, but one of the most comprehensive and used is the ontology for Chemical Entities of Biological Interest (ChEBI) [18]. This ontology represents in a machine-readable format about 114 thousand concepts, including not only the chemical compounds but also their biological and chemical roles. Other ontologies that encode this or related domains include (*i*) Interlinking Ontology for Biological Concepts, (*ii*) Current Procedural Terminology, (*iii*) SNOMED CT, (*iv*) Chemical Information
It is important to notice that, even though the notion of ontologies usually requires some logic concepts (such as axioms, predicates, etc.), some classification hierarchies are also sometimes named "ontologies". MeSH, the system used
*A toy example of an ontology for chemical compounds, based on ChEBI. The ontology shows "is-a" relationships with solid lines, and a relationship between acid/base conjugates with a dotted line. The green shaded concepts*
ends up in the most generic "molecular entity" concept.
Ontology, and (*v*) Chemical Methods Ontology.
**34**
**Figure 2.**
*are those that subsume both the yellow and the blue ones.*
by PubMed to classify publications, is a hierarchy of concepts that possesses many of the same properties that ontologies do, namely, that it can be represented as a directed acyclic graph. However, one of the differences is that the relationship between two concepts does not always carry the same meaning. For example, "Head" is categorised under "Body Regions", and "Ear" is categorised under "Head", but while heads *are* body regions, ears *are not* heads; they are instead *parts* of the head. This illustrates the informality of MeSH: only one relationship type exists and it is used to express different notions. Another system in this category is the Anatomical Therapeutic Chemical (ATC) Classification System.
BioPortal [19], a repository of ontologies for the biomedical domain, contains a collection of 948 ontologies at the time of this writing. As an illustration of its magnitude, consider that 19 ontologies represent the concept "lidocaine". This reflects the effort being currently spent to represent human knowledge in machinereadable ontologies. In fact, while ontologies such as ChEBI are massive, BioPortal allows their users to submit new ontologies, even if small, focussed on a specific domain, and created with a specific application in mind other than pure knowledge representation (e.g., there is an ontology specific for cardiovascular drug adverse events, with 3 thousand concepts).
Other efforts have been set into place to aggregate ontologies in a single source of knowledge. For example, the Open Biological and Biomedical Ontology (OBO) Foundry [20] developed the OBO file format to represent ontologies and currently defines principles of quality for ontologies in biomedical domain that prescribe good practices for ontology development, such as being open, being reusable, being developed with collaboration in mind, containing both textual and logical definitions (for the benefit of both humans and machines), etc. They contain more than 200 ontologies as of this writing, 10 of which fully adhere to those principles (ChEBI being one of them). The OBO Foundry is tightly coupled with Ontobee [21], a web service that uses the principles of linked data to serve as a linked data server specifically targeted for ontologies and their concepts.
| doab | 2025-04-07T04:13:04.418614 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.41 | **4. Semantic similarity**
Using a formal representation of knowledge, computers are given the ability to manipulate concepts that are difficult to represent, in a way that preserves their "semantics". Ontologies provide the appropriate support for automatic manipulation of information. In this context, semantic similarity is a technique that assigns a numeric value to a pair of concepts based on the similarity of their meaning, extracted from the ontology.
For example, there is no directly obvious way to compare two roles. However, considering the illustration in **Figure 3**, it is possible to intuitively understand that, because both "hallucinogen" and "antifungal drug" are examples of "drugs", they are more similar than "hallucinogen" and "fossil fuel". This measure makes use of the meaning of the concepts, implicitly represented in the ontologies through the relations between the concepts. Ontologies function as a proxy for that meaning and enable its manipulation and ultimately comparison.
Several formulas and ideas have been proposed, implemented and tested in the past to compute semantic similarity. A full exposition on such measures and algorithms is beyond the scope of this chapter. The reader is encouraged to expand on this topic by reading works such as [22–25]. As such, the following is an abridged version of how ontology-based semantic similarity has been computed. In this discussion, consider the ontology in **Figure 3**.
**Figure 3.** *A second toy example of an ontology representing chemical roles, also based on ChEBI.*
Measures of similarity based on ontologies can roughly be divided into edgebased and node-based. An example of an edge-based measure is counting how many relations must be traversed to connect the two concepts being compared. Rada et al. [26] define distance as the number of edges in the smallest path between two nodes composed only of "is-a" relations. In this case, the distance between "hallucinogen" and "antimicrobial agent" would be three ("hallucino gen"→"drug"→"antifungal drug"→"antimicrobial agent"). While this type of approach is intuitive, it assumes that all nodes and all edges are equally important in terms of their semantics (e.g., that all edges weigh the same), which is generally not true in ontologies in life sciences. For instance, the "is-a" relation between "hallucinogen" and "drug" does not necessarily convey the same *amount of information* as the inverse "is-a" relation between "drug" and "antifungal drug".
One way to solve this is to introduce node-based methods, a technique that weighs nodes based on their *information content* (IC) [27]. The IC of a node is a numeric value based that reflects how informative its presence is and is calculated based on its frequency of use, since concepts that appear more frequently are generally less informative. The first formula proposed to measure IC was
$$\text{IC}(\mathcal{c}) = -\log f(\mathcal{c}) \tag{1}$$
where *f*(*c*) is the relative frequency with which the concept *c* and all its descendants appear in a corpus (in the example ontology, we can use the fraction of chemical concepts in ChEBI annotated as performing each of those roles). The intuition behind this idea is the following: consider a document (e.g., a scientific article) that uses the sentence "rodents have fur". The term "rodent" is used in such a way that every other concept that can be categorised under it also possesses the declared property. In fact, whenever a concept is used (in text, in logical axioms, etc.), it must be interpreted as including the set of all concepts recursively categorised under it.
The similarity between two concepts can be computed as the IC of the *most informative common ancestor* (usually abbreviated as MICA) between them
$$\text{sim}\_{\text{Resnik}}(c\_1, c\_2) = \text{IC}(\text{MICA}(c\_1, c\_2)).\tag{2}$$
**37**
*Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
the measure is unbounded above);
This idea has been iterated upon with some additions and adaptations.
properties of the graph representation of the ontology [31].
• The IC measure can be normalised so that it ranges from 0.0 to 1.0 (originally,
• The semantic similarity measure itself can be normalised. Notice that the original measure gives the same similarity to the pair "application"/"biological role" (both generic concepts) and "fossil fuel"/"antiviral agent", which goes against the intuition that the first pair should be more similar. Lin [32] uses this idea to define
simLin(*c*1, *c*2) = <sup>2</sup> <sup>⋅</sup> IC(MICA(*c*1, *c*2)) \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ IC(*<sup>c</sup>*1) <sup>+</sup> IC(*c*2)
• The notion of shared information content (originally measured as the information content of the MICA of the two concepts) has also been tuned to take into account the fact that concepts can have multiple parents [33], which is necessary in many life science fields since it is in the nature of biomedical ontologies that some concepts are categorised under multiple parents, (see https://github.com/lasige-BioTM/DiShIn for an example of software that computes this type of measure) or the fact that ontologies have disjointness axioms that encode the fact that two concepts cannot share any descendants [34], also important because life science ontologies, and especially chemistry ones, make use of those types of axioms [35].
• The way to measure shared information content has also been completely reimplemented to use not the IC of the most informative common ancestor but a
These measures are able to compare one concept with another. It is also possible to compare sets of concepts. For this, one takes the matrix of pairwise similarities between concepts in the first set and concepts in the second set and mathematically manipulates it to produce a single number, taking, for example, the average, the maximum, or the "best match average", an approach that averages the highest values in each row and column [22]. There are other approaches that convert a set of concepts into the set of all their ancestors and take the intersection of those sets as a
Finally, there is a difference in measuring the *similarity* or the *relatedness* between concepts. Similarity is a term that is generally applied to the notion that two concepts are "alike" and is usually computed based on "is-a" hierarchies; relatedness is more general: two related concepts can be related based on their categorisation on a hierarchy or on any number of other non-hierarchical relations. This distinction is important in chemistry, and ChEBI in particular, since many chemistry concepts are related via relations such as "has-role", "has-part", "is-enantiomer-of", etc. Notice that when nothing is known about a chemical compound other than its structure, semantic methods can still be used, because one of the ways ontologies
metric based on the set of all ancestors of the concepts [36].
measure of similarity (two examples are simUI and simGIC [22]).
; (3)
• The IC measure has been computed from multiple sources, such as (*i*) text corpora (as in the original), (*ii*) frequency of usage of the ontology concepts in external sources [28], or (*iii*) the ontology itself, where frequency can be computed based on the number of descendants (direct or indirect) of a concept [29], the number of leaf descendants of a concept [30], or other topological
*Cheminformatics and Its Applications*
Measures of similarity based on ontologies can roughly be divided into edgebased and node-based. An example of an edge-based measure is counting how many relations must be traversed to connect the two concepts being compared. Rada et al. [26] define distance as the number of edges in the smallest path between two nodes composed only of "is-a" relations. In this case, the distance between "hallucinogen" and "antimicrobial agent" would be three ("hallucino gen"→"drug"→"antifungal drug"→"antimicrobial agent"). While this type of approach is intuitive, it assumes that all nodes and all edges are equally important in terms of their semantics (e.g., that all edges weigh the same), which is generally not true in ontologies in life sciences. For instance, the "is-a" relation between
*A second toy example of an ontology representing chemical roles, also based on ChEBI.*
"hallucinogen" and "drug" does not necessarily convey the same *amount of information* as the inverse "is-a" relation between "drug" and "antifungal drug". One way to solve this is to introduce node-based methods, a technique that weighs nodes based on their *information content* (IC) [27]. The IC of a node is a numeric value based that reflects how informative its presence is and is calculated based on its frequency of use, since concepts that appear more frequently are generally less informative. The first formula proposed to measure
where *f*(*c*) is the relative frequency with which the concept *c* and all its descen-
The similarity between two concepts can be computed as the IC of the *most*
*informative common ancestor* (usually abbreviated as MICA) between them
dants appear in a corpus (in the example ontology, we can use the fraction of chemical concepts in ChEBI annotated as performing each of those roles). The intuition behind this idea is the following: consider a document (e.g., a scientific article) that uses the sentence "rodents have fur". The term "rodent" is used in such a way that every other concept that can be categorised under it also possesses the declared property. In fact, whenever a concept is used (in text, in logical axioms, etc.), it must be interpreted as including the set of all concepts recursively catego-
IC(*c*) = −log *f*(*c*) (1)
simResnik(*c*1, *c*2) = IC(MICA(*c*1, *c*2)). (2)
**36**
IC was
**Figure 3.**
rised under it.
This idea has been iterated upon with some additions and adaptations.
$$
\hat{\mathbf{s}} \cdot \mathbf{m}\_{\text{Lin}}(\mathbf{c}\_1, \mathbf{c}\_2) = \frac{2 \cdot \text{IC}(\text{MICA}(\mathbf{c}\_1, \mathbf{c}\_2))}{\text{IC}(\mathbf{c}\_1) + \text{IC}(\mathbf{c}\_2)} \; ; \tag{3}
$$
$$
\text{(3)}
$$
These measures are able to compare one concept with another. It is also possible to compare sets of concepts. For this, one takes the matrix of pairwise similarities between concepts in the first set and concepts in the second set and mathematically manipulates it to produce a single number, taking, for example, the average, the maximum, or the "best match average", an approach that averages the highest values in each row and column [22]. There are other approaches that convert a set of concepts into the set of all their ancestors and take the intersection of those sets as a measure of similarity (two examples are simUI and simGIC [22]).
Finally, there is a difference in measuring the *similarity* or the *relatedness* between concepts. Similarity is a term that is generally applied to the notion that two concepts are "alike" and is usually computed based on "is-a" hierarchies; relatedness is more general: two related concepts can be related based on their categorisation on a hierarchy or on any number of other non-hierarchical relations. This distinction is important in chemistry, and ChEBI in particular, since many chemistry concepts are related via relations such as "has-role", "has-part", "is-enantiomer-of", etc.
Notice that when nothing is known about a chemical compound other than its structure, semantic methods can still be used, because one of the ways ontologies (especially ChEBI) classify molecules is based on their structure. For example, ChEBI has a concept "carboxylic acid" which is an ancestor of all molecules that have one or more carboxylic acid groups (e.g., benzoic acid, all amino acids, all penicillins, etc.). This, however, is not conceptually different from measuring structural similarity, and such a setting would lack the enrichment provided by other types of knowledge (e.g., the knowledge of the chemical and biological roles of the molecule).
| doab | 2025-04-07T04:13:04.419617 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.42 | **5. Applications**
Since 2003, when Lord et al. [28] introduced the idea of ontology-based semantic similarity in the gene ontology (GO), several results have been achieved using this technique, proving beyond doubt that it is sound and useful and has real-life applications. In genomics and proteomics, semantic similarity based on GO has been used to (i) cluster proteins [37], (ii) find protein-protein interactions [38], (iii) interpret microarray data [39], (iv) predict protein functions [40], (v) prioritise candidate disease genes [41], etc. Other uses outside GO include predicting disease-related phenotypes [42] and predicting clinical diagnosis from a set of phenotype abnormalities [43].
The uses in chemistry-related areas have been scarce, but nonetheless existing and with real-world applications. We collected three research studies of semantic similarity in cheminformatics, which show its use in this area.
### **5.1 Predict biochemical properties of molecules**
In 2010, ontology-based semantic similarity was applied to ChEBI [44] using a methodology named Chym. Chym shows for the first time that semantic similarity is useful in biomedical chemistry, by applying these ideas to predict whether a molecule (i) is capable of crossing the blood brain barrier, (ii) is a substrate of the P-glycoprotein, and (iii) binds to an oestrogen receptor. These properties are at least partially intrinsically related to the three-dimensional structure of the molecules and also of the proteins that perform the biochemical role in the organism. However, the work shows that structural similarity alone can be improved if it is coupled with semantic similarity.
Chym used daylight fingerprints for structural similarity and simUI and simGIC for semantic similarity, using ChEBI as the ontology. For all the three properties mentioned above, Chym was able to clearly outperform what were then the state-ofthe-art prediction techniques for those properties.
Notice that this means that the two ideas presented here, structural similarity and semantic similarity, are not orthogonal and can be applied simultaneously with good results. This is not surprising, as ontologies can complement the knowledge that can be inferred form the structure alone, without needing to resort to wet-lab experiments.
#### **5.2 Disambiguate chemical compound references in natural language**
As the amount of textual chemistry information increases, particularly in the form of drug leaflets, articles, patents, and other types of communications, the need to develop mechanisms to automatically read these texts and extract tractable information from them increases as well. In this context, named entity recognition is a text mining task whose goal is to identify the entities mentioned in text.
**39**
repurposing.
*Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
correct entity.
one, but GO.
**5.3 Drug repurposing**
pharmaceutical industry.
"expression profile".
The main methodology of this work was:
1.Select a drug *d* and a potential target protein *p*.
have now a vector *X* sem = (*p* 1, *p* 2,…, *p m*).
between the "expression profiles" of the two drugs.
listic model that predicts whether drugs and proteins interact.
There have been many attempts to create such systems in the chemical domain (see, e.g., the review [45]). In one of those attempts [46], semantic similarity has been used to improve the precision of existing methodologies by successfully identifying some false positives and removing them from the final result set. The idea of that work is that, within a scope of text (e.g., a sentence or a paragraph), chemical entities mentioned in that scope share some degree of semantic similarity that is higher than average. When entity recognition algorithms offer more than one possible ChEBI identifier for an excerpt of text, similarity with other ChEBI concepts can be used to disambiguate which is the
Drug repurposing is the process by which drug that have therapeutic application are computationally tested to find other therapeutic applications. This reduces costs and improves the drug development pipeline and as such is important for the
The work presented in [47] couples similarity between the three-dimensional molecular structure with semantic similarity between the drug targets to find new indications for known drugs. The ontology used here is not a chemistry-specific
2. Find drugs similar to this one (up to a threshold) with a structural similarity measure. Store these structural similarity values in a vector *X* str = (*d* 1, *d* 2,…, *d m*).
3.For each similar drug *di*, find its interacting proteins, compare them with *p* using GO-based semantic similarity, and sum the results. Call this value *pi*. We
4.The drug-protein association is assigned a score that depends on the correlation between the vectors *X*str and *X*sem. For a set of *N* proteins, each drug was then assigned a vector of *N* drug-protein association values, called the drug's
5.The drug-drug similarity measure was computed based on the correlation
The similarity between drugs was then used to construct a network of similarities, where clusters of highly connected drugs were indicative of potential drug
A related work [48] also uses semantic similarity to predict drug-protein interaction. In this work, probabilistic similarity logic is used to construct models that are based on a notion of "similarity triads": triples of the form "drug-target-drug" with similar drugs or "target-drug-target" with similar targets. The whole work was based on the assumption that similar targets tend to interact with the same drug and similar drugs tend to interact with the same target. Here, several protein similarity methods (including semantic similarity based on GO) and drug similarity method (including semantic similarity based on ATC) were used to build a probabi*Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
There have been many attempts to create such systems in the chemical domain (see, e.g., the review [45]). In one of those attempts [46], semantic similarity has been used to improve the precision of existing methodologies by successfully identifying some false positives and removing them from the final result set. The idea of that work is that, within a scope of text (e.g., a sentence or a paragraph), chemical entities mentioned in that scope share some degree of semantic similarity that is higher than average. When entity recognition algorithms offer more than one possible ChEBI identifier for an excerpt of text, similarity with other ChEBI concepts can be used to disambiguate which is the correct entity.
#### **5.3 Drug repurposing**
*Cheminformatics and Its Applications*
molecule).
**5. Applications**
phenotype abnormalities [43].
coupled with semantic similarity.
(especially ChEBI) classify molecules is based on their structure. For example, ChEBI has a concept "carboxylic acid" which is an ancestor of all molecules that have one or more carboxylic acid groups (e.g., benzoic acid, all amino acids, all penicillins, etc.). This, however, is not conceptually different from measuring structural similarity, and such a setting would lack the enrichment provided by other types of knowledge (e.g., the knowledge of the chemical and biological roles of the
Since 2003, when Lord et al. [28] introduced the idea of ontology-based semantic similarity in the gene ontology (GO), several results have been achieved using this technique, proving beyond doubt that it is sound and useful and has real-life applications. In genomics and proteomics, semantic similarity based on GO has been used to (i) cluster proteins [37], (ii) find protein-protein interactions [38], (iii) interpret microarray data [39], (iv) predict protein functions [40], (v) prioritise candidate disease genes [41], etc. Other uses outside GO include predicting disease-related phenotypes [42] and predicting clinical diagnosis from a set of
The uses in chemistry-related areas have been scarce, but nonetheless existing and with real-world applications. We collected three research studies of semantic
In 2010, ontology-based semantic similarity was applied to ChEBI [44] using a methodology named Chym. Chym shows for the first time that semantic similarity is useful in biomedical chemistry, by applying these ideas to predict whether a molecule (i) is capable of crossing the blood brain barrier, (ii) is a substrate of the P-glycoprotein, and (iii) binds to an oestrogen receptor. These properties are at least partially intrinsically related to the three-dimensional structure of the molecules and also of the proteins that perform the biochemical role in the organism. However, the work shows that structural similarity alone can be improved if it is
Chym used daylight fingerprints for structural similarity and simUI and simGIC for semantic similarity, using ChEBI as the ontology. For all the three properties mentioned above, Chym was able to clearly outperform what were then the state-of-
Notice that this means that the two ideas presented here, structural similarity and semantic similarity, are not orthogonal and can be applied simultaneously with good results. This is not surprising, as ontologies can complement the knowledge that can be inferred form the structure alone, without needing to resort to wet-lab
As the amount of textual chemistry information increases, particularly in the form of drug leaflets, articles, patents, and other types of communications, the need to develop mechanisms to automatically read these texts and extract tractable information from them increases as well. In this context, named entity recognition
**5.2 Disambiguate chemical compound references in natural language**
is a text mining task whose goal is to identify the entities mentioned in text.
similarity in cheminformatics, which show its use in this area.
**5.1 Predict biochemical properties of molecules**
the-art prediction techniques for those properties.
**38**
experiments.
Drug repurposing is the process by which drug that have therapeutic application are computationally tested to find other therapeutic applications. This reduces costs and improves the drug development pipeline and as such is important for the pharmaceutical industry.
The work presented in [47] couples similarity between the three-dimensional molecular structure with semantic similarity between the drug targets to find new indications for known drugs. The ontology used here is not a chemistry-specific one, but GO.
The main methodology of this work was:
The similarity between drugs was then used to construct a network of similarities, where clusters of highly connected drugs were indicative of potential drug repurposing.
A related work [48] also uses semantic similarity to predict drug-protein interaction. In this work, probabilistic similarity logic is used to construct models that are based on a notion of "similarity triads": triples of the form "drug-target-drug" with similar drugs or "target-drug-target" with similar targets. The whole work was based on the assumption that similar targets tend to interact with the same drug and similar drugs tend to interact with the same target. Here, several protein similarity methods (including semantic similarity based on GO) and drug similarity method (including semantic similarity based on ATC) were used to build a probabilistic model that predicts whether drugs and proteins interact.
| doab | 2025-04-07T04:13:04.420266 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.43 | **6. Challenges and future work**
Semantic similarity in cheminformatics has been slow to keep with the pace of equivalent research in other life science fields, such as genomics and proteomics. We posit that this is in some ways related to general and specific challenges associated with the application of this methodology in chemistry.
First, the state of ontology development and the more general knowledge representation area is very active, specifically in the biomedical fields. This means that many people have the motivation to develop their own ontology, with specific views of the reality embedded in it. However, as many people create their own knowledge representation artefacts, many different ontologies start to appear that overlap in domain, which means that it is not always obvious which ontology (or ontologies) to choose for a specific goal. Furthermore, these ontologies are not easy to reconcile, because they encode different and disjoint points of view. While efforts have been made to attenuate this problem, such as ontology matching (the process by which ontologies of the same domain are automatically merged into a single ontology) and the establishment of community standards (in chemistry, e.g., it is standard practice to reuse ChEBI concepts rather than create new concepts in new ontologies), the problem still persists.
Second, metrics of semantic similarity have been mostly developed and tested in the fields of natural language processing and genomics/proteomics. While these seem to have good enough results when used with ChEBI, we still do not know if they are the most adequate measures in this domain. Ferreira et al. [34] developed and validated a measure on the chemical domain, but more work needs to be done in this area. In particular, what role should the non-hierarchical relationship types ("is-enantiomer-of", "is-conjugate-acid-of", etc.) have in semantic similarity?
The third challenge is one of similarity profiles. It is not always obvious which details or properties of a molecule should be used for comparing. Should a pair of chemical compounds that differ only in the presence of an oxygen atom (e.g., methane vs. methanol) be more similar than a pair of molecules that differ only in charge (e.g., NO2 vs. NO2 <sup>−</sup>) or only in their three-dimensional conformation (e.g., L-serine vs. D-serine)? This problem must be solved based on context: determining what the similarity measure will be used for and then deciding which features are important. This includes deciding, for example, which relationship types should be taken into account, how to weight them, etc. Maggiora et al. [49] touch on the fact that chemoinformaticians and medicinal chemists typically perceive similarity differently and we need to find ways to capture those differences in actionable measures of similarity.
The fourth challenge is the necessity of taking into account multiple domains of knowledge: drugs interact with proteins, treat and cause diseases, are produced by different methods (industrial or otherwise), have side effects, participate in metabolic reactions, etc. These concepts from other domains can also be compared semantically (many are even already represented in appropriate ontologies, including diseases, proteins, types of molecular interaction, manufacturing procedures, side effects, and pathways). The question now is how to take advantage of these other ontologies in order to implement a useful and accurate measure of chemical similarity. This issue is even related to the previous one, since by tuning the weight of these other domains, we can create new profiles of similarity more pertinent to some goals than others.
Another challenge is the absence of a standardised way to *validate* the measures that are proposed. In practice, for each new measure being proposed by some research group, that same group validates the new measure by comparing them with previous ones or by using it to show that the new measure can find
**41**
*Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
to this field.
**7. Conclusion**
**Acknowledgements**
**Abbreviations**
hidden knowledge in some dataset. However, the *ad hoc* way these validations are performed means that frequently the measures are neither comparable nor interchangeable and that they can only be used for the goal used to validate them. Thus, a general but useful validation strategy should also be developed to bring cohesion
This chapter introduces the ideas behind ontology-based semantic similarity measures, how they are applied in life sciences, and some of their uses in chemistryrelated research endeavours. The main idea that we exposed is that these methods, having been used in other biomedical fields, can help cheminformatics in several fronts. We described three applications of where this methodology has been applied directly in cheminformatics research efforts and expect that this number grows as
We also exposed some of the future challenges in this area, which can serve as a starting point to anyone wishing to improve on the work already published, and provided general guidelines that should be taken into account for the further improvement of cheminformatics as a scientific field. In particular, we emphasise the need to explore the multidomain potential in semantic similarity, as well as the
This work was supported by FCT through funding of DeST: Deep Semantic Tagger project, ref. PTDC/CCI-BIO/28685/2017 (http://dest.rd.ciencias.ulisboa.
need to standardise the ways to validate measures of semantic similarity.
ATC anatomical therapeutic chemical classification system
SNOMED CT systematised nomenclature of medicine—clinical terms
pt./) and LASIGE Research Unit, ref. UID/CEC/00408/2019.
ChEBI chemical entities of biological interest
MICA most informative common ancestor OBO Open Biological and Biomedical Ontology QSAR quantitative structure-activity relationship simGIC similarity of graphs with information content
simUI similarity with union and intersection SMILES simplified molecular-input line-entry system
DAG directed acyclic graph
GO gene ontology IC information content MeSH medical subject headings
more people are exposed to this idea and its use cases.
### *Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
hidden knowledge in some dataset. However, the *ad hoc* way these validations are performed means that frequently the measures are neither comparable nor interchangeable and that they can only be used for the goal used to validate them. Thus, a general but useful validation strategy should also be developed to bring cohesion to this field.
| doab | 2025-04-07T04:13:04.420803 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.44 | **7. Conclusion**
*Cheminformatics and Its Applications*
**6. Challenges and future work**
gies), the problem still persists.
charge (e.g., NO2 vs. NO2
measures of similarity.
some goals than others.
with the application of this methodology in chemistry.
Semantic similarity in cheminformatics has been slow to keep with the pace of equivalent research in other life science fields, such as genomics and proteomics. We posit that this is in some ways related to general and specific challenges associated
First, the state of ontology development and the more general knowledge representation area is very active, specifically in the biomedical fields. This means that many people have the motivation to develop their own ontology, with specific views of the reality embedded in it. However, as many people create their own knowledge representation artefacts, many different ontologies start to appear that overlap in domain, which means that it is not always obvious which ontology (or ontologies) to choose for a specific goal. Furthermore, these ontologies are not easy to reconcile, because they encode different and disjoint points of view. While efforts have been made to attenuate this problem, such as ontology matching (the process by which ontologies of the same domain are automatically merged into a single ontology) and the establishment of community standards (in chemistry, e.g., it is standard practice to reuse ChEBI concepts rather than create new concepts in new ontolo-
Second, metrics of semantic similarity have been mostly developed and tested in the fields of natural language processing and genomics/proteomics. While these seem to have good enough results when used with ChEBI, we still do not know if they are the most adequate measures in this domain. Ferreira et al. [34] developed and validated a measure on the chemical domain, but more work needs to be done in this area. In particular, what role should the non-hierarchical relationship types ("is-enantiomer-of", "is-conjugate-acid-of", etc.) have in semantic similarity? The third challenge is one of similarity profiles. It is not always obvious which details or properties of a molecule should be used for comparing. Should a pair of chemical compounds that differ only in the presence of an oxygen atom (e.g., methane vs. methanol) be more similar than a pair of molecules that differ only in
L-serine vs. D-serine)? This problem must be solved based on context: determining what the similarity measure will be used for and then deciding which features are important. This includes deciding, for example, which relationship types should be taken into account, how to weight them, etc. Maggiora et al. [49] touch on the fact that chemoinformaticians and medicinal chemists typically perceive similarity differently and we need to find ways to capture those differences in actionable
The fourth challenge is the necessity of taking into account multiple domains of knowledge: drugs interact with proteins, treat and cause diseases, are produced by different methods (industrial or otherwise), have side effects, participate in metabolic reactions, etc. These concepts from other domains can also be compared semantically (many are even already represented in appropriate ontologies, including diseases, proteins, types of molecular interaction, manufacturing procedures, side effects, and pathways). The question now is how to take advantage of these other ontologies in order to implement a useful and accurate measure of chemical similarity. This issue is even related to the previous one, since by tuning the weight of these other domains, we can create new profiles of similarity more pertinent to
Another challenge is the absence of a standardised way to *validate* the measures that are proposed. In practice, for each new measure being proposed by some research group, that same group validates the new measure by comparing them with previous ones or by using it to show that the new measure can find
<sup>−</sup>) or only in their three-dimensional conformation (e.g.,
**40**
This chapter introduces the ideas behind ontology-based semantic similarity measures, how they are applied in life sciences, and some of their uses in chemistryrelated research endeavours. The main idea that we exposed is that these methods, having been used in other biomedical fields, can help cheminformatics in several fronts. We described three applications of where this methodology has been applied directly in cheminformatics research efforts and expect that this number grows as more people are exposed to this idea and its use cases.
We also exposed some of the future challenges in this area, which can serve as a starting point to anyone wishing to improve on the work already published, and provided general guidelines that should be taken into account for the further improvement of cheminformatics as a scientific field. In particular, we emphasise the need to explore the multidomain potential in semantic similarity, as well as the need to standardise the ways to validate measures of semantic similarity.
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ffe82432-4883-4adc-b03b-937c1baf5090.45 | **Acknowledgements**
This work was supported by FCT through funding of DeST: Deep Semantic Tagger project, ref. PTDC/CCI-BIO/28685/2017 (http://dest.rd.ciencias.ulisboa. pt./) and LASIGE Research Unit, ref. UID/CEC/00408/2019.
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ffe82432-4883-4adc-b03b-937c1baf5090.46 | **Abbreviations**
*Cheminformatics and Its Applications*
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ffe82432-4883-4adc-b03b-937c1baf5090.47 | **Author details**
João D. Ferreira\* and Francisco M. Couto LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Portugal
\*Address all correspondence to: [email protected]
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
**43**
*Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
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[9] Richard AM, Gold LS, Nicklaus MC. Chemical structure indexing of toxicity data on the internet: Moving toward a flat world. Current Opinion in Drug Discovery & Development.
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*Semantic Similarity in Cheminformatics DOI: http://dx.doi.org/10.5772/intechopen.89032*
| doab | 2025-04-07T04:13:04.421709 | 20-4-2021 18:19 | {
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"url": "https://mts.intechopen.com/storage/books/9236/authors_book/authors_book.pdf",
"author": "",
"title": "Cheminformatics and its Applications",
"publisher": "IntechOpen",
"isbn": "9781838800680",
"section_idx": 47
} |
ffe82432-4883-4adc-b03b-937c1baf5090.49 | **Chapter 4**
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Stumpfe D, Bajorath J. Molecular similarity in medicinal chemistry. Journal of Medicinal Chemistry. 2014;**57**(8):3186-3204. PMID: 24151987. Available from: 10.1021/jm401411z
[41] Liu B, Jin M, Zeng P. Prioritization of candidate disease genes by combining topological similarity and semantic similarity. Journal of Biomedical
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[42] Xue H, Peng J, Shang X. Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO. BMC Systems Biology. 2019;**13**(2):34.
[43] Köhler S, Schulz MH, Krawitz P, Bauer S, Dlken S, Ott CE, et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. The American Journal of Human Genetics. 2009;**85**(4):457- 464. Available from: http: //www. sciencedirect.com/science/article/pii/
[44] Ferreira JD, Couto FM. Semantic similarity for automatic classification of chemical compounds. PLoS Computational Biology. 2010;**6**(9):1- 11. Available from: 10.1371/journal.
[45] Eltyeb S, Salim N. Chemical named entities recognition: A review on approaches and applications. Journal of Cheminformatics. 2014;**6**(1):17. Available from: 10.1186/1758-2946-6-17
[46] Lamurias A, Ferreira JD, Couto FM. Improving chemical entity recognition through h-index based semantic similarity. Journal of Cheminformatics.
2015;**7**(1):S13. Available from: 10.1186/1758-2946-7-S1-S13
[47] Tan F, Yang R, Xu X, Chen X, Wang Y, Ma H, et al. Drug repositioning by applying "expression profiles" generated by integrating chemical
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**46**
| doab | 2025-04-07T04:13:04.422056 | 20-4-2021 18:19 | {
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"author": "",
"title": "Cheminformatics and its Applications",
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ffe82432-4883-4adc-b03b-937c1baf5090.50 | Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive Compounds
*Marcos Antônio B. dos Santos, Luã Felipe S. de Oliveira, Antônio Florêncio de Figueiredo, Fábio dos Santos Gil, Márcio de Souza Farias, Heriberto Rodrigues Bitencourt, José Ribamar B. Lobato, Raimundo Dirceu de P. Farreira, Sady Salomão da S. Alves, Edilson Luiz C. de Aquino and José Ciríaco-Pinheiro*
| doab | 2025-04-07T04:13:04.422188 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.51 | **Abstract**
In this chapter, firstly, we briefly review aspects of the approximation of quantum chemistry, molecular electrostatic potential (MEP), and chemometrics techniques, which are accredited as important tools in the development of chemical science and are frequently used in the study and design of bioactive compounds. Ultimately, we use MEP and pattern recognition (PR) techniques as tools to design nitrofuran compounds with biological activity against *Trypanosoma cruzi* (*T. cruzi*). PR models (PCA, HCA, KNN, SDA, and SIMCA) were constructed and demonstrated that 23 nitrofurans can be classified into two classes or groups: more active and less active according to their degrees of activity against *T. cruzi*. Properties such as charge on the N atom of the nitro group (QN1); the difference between the highest occupied molecular orbital (HOMO) energy and the lowest unoccupied molecular orbital (LUMO) energy (GAP energy); molecular representation of structure based on electron diffraction code of signal 5, unweighted (Mor05u); and Moriguchi water–octanol partition coefficient (MlogP) are responsible for the classification into more active and less active studied nitrofurans. It is interesting to notice that these properties represent three distinct classes of interactions between the nitrofurans and the biological receptor: electronic (QN1 and GAP energy), steric (Mor05u), and hydrophobic (MlogP). The results of the application of PR models on the validation set evidenced two nitrofuran compounds (compounds **25** and **30**) as more promising for synthesis and biological assays, which in the future can be used to validate our PR models.
**Keywords:** molecular electrostatic potential, chemometric techniques, pattern recognition techniques, chemoinformatics, design of bioactive compounds
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ffe82432-4883-4adc-b03b-937c1baf5090.52 | **1. Introduction**
Reports of theoretical bases of MEP and the development of efficient computational methods state that MEP has become an important reactivity index in studies of a large variety of molecular interactions [1]. The usefulness of this theoretical approach in studies and interpretation of chemical, biochemical, and related phenomena is well documented [2–18].
Chemometrics is a discipline that collects mathematical, statistical, information theory, and computer science tools to deal with complex chemical data [19–22]. PR techniques were introduced in the chemistry, at the beginning of the 1970s, to analyze various types of spectroscopic data. Since then, PR became part of chemometrics and has been an excellent tool to aid in the interpretation of chemical data to obtain relevant information in different application sectors of chemical science [19, 20]. PR techniques are especially useful for the classification of objects into discrete classes on the basis of measured features. A set of characteristic features of an object is considered as an abstract pattern that contains information about a not directly measured property of the object [19].
The MEP and PR techniques have been used as independent strategies in the study of active compounds and lead to the proposal of new molecules for synthesis and biological testing. The joint applications of these powerful tools were described carefully, to unravel the structure-activity relationship of bioactive compounds, consequently proposing new molecules. Therefore, a more intense exploration of its potentials is needed in order to design biologically active compounds.
The design of molecules with a desired property is one of the objectives of chemoinformatics. In this chapter, we present a study of the application of MEP and PR techniques to design nitrofuran compounds with potential activity against *T. cruzi.* In the first step of our study, MEP maps will be used in an attempt to identify the key structural features of nitrofuran compounds that are necessary for their activities and investigate their probable interactions with a molecular receptor through recognition in a biological process. Subsequently, PR techniques are used to construct models that will be applied later to a forecast set constructed with the accumulated perceptions in the MEP studies.
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ffe82432-4883-4adc-b03b-937c1baf5090.53 | **2. MEP and chemometrics techniques as tools for the design of bioactive compounds: a brief review**
According to the literature, MEP [1, 3] has been a tool of quantum chemistry used by researchers for several decades to study and understand the relationships between structure and activity of molecules. Among the papers that point out the importance of this tool in the matter, and consequently in the planning of bioactive compounds, we can mention those reported by Bernardinelli et al. [23] and by Jefford et al. [24].
Another tool, in the form of a set of techniques has been used emphatically over the years in the understanding of the structure-activity relationship of molecules is Chemometrics [25–27]. This set of techniques has also enables the planning of new biologically active compounds, and most of the developed research is focused on the construction of QSAR (quantitative structure-activity relationship) models.
The combination of MEP and chemometrics as tools for designing new bioactive compounds has almost always been focused on the elaboration of quantitative models, for example, the CoMFA methodology [28]. This methodology was developed in the late 1980s by Cramer et al. [29]. Its application is richly extensive and recently it has been used in several studies of structure–activity relationships of bioactive
**49**
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
compounds. Chatbar et al. conducted a study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer [30]. Pourbasheer et al. performed 3D-QSAR and 2D-QSAR analyses on the series of compounds hepatitis C virus NS5B polymerase inhibitors [31]. Cramer applied the CoMFA methodology for a large majority of 116 biological targets and obtained acceptable 3D-QSAR models [32]. Cramer et al. introduced in the literature a novel alignment methodology for training or test set structures in 3D-QSAR [33]. Dong et al. performed QSAR analyses of aromatic heterocycle thiosemicarbazone analogues for finding novel tyrosinase inhibitors [34]. Dong et al. built 3D-QSAR models of dabigatran analogues as thrombin inhibitors [35]. Ding et al. performed 3D-QSAR models of 6-aryl-5-cyanopyrimidine derivatives to explore the structure requirements of LSD1 inhibitors [36]. Applications of MEP to investigate the key features of compounds that are necessary for their biological activities and thus proposing new derivatives as well as the construction of chemometric models as indicative of the most promising among the new derivatives for syntheses and biological assays were reported by us in literature [37–43]. Pinheiro et al. stated the use of MEP and partial least squares regression (PLS) method in the design of new artemisinin derivatives with activities against *Plasmodium falciparum* [37]. Cardoso et al., using MEP maps and multivariate QSAR, designed new artemisinin derivatives with antimalarial activity [38]. Ferreira et al., through MEP maps and multivariate analysis, designed antimalarial artemisinins [39]. Figueiredo et al. designed new derivatives of dispiro-1,2,4 trioxolones with activity against falciparum malaria [40]. Carvalho et al., through maps of MEP and pattern recognition methods, proposed new artemisinin
derivatives with activity against *Leishmania donovani* [41]. Barbosa et al. used MEP maps and pattern recognition techniques to plan new derivatives of artemisinin anticancer HepG2 [42]. Cristino et al. proposed new derivatives of 10-substituted Deoartemisinis with activity against *P. falciparum* [43] through the use of MEP
**3. MEP and PR techniques as tools to design nitrofuran compounds**
The MEP is also suitable for analyzing processes based on the "recognition" of one molecule by another as in drug-receptor and enzyme-substrate interactions, because it is through their potentials that the two species first "see" each other
MEP for the electronic density is a very useful property for understanding the site of electrophilic attack and nucleophilic reactions as well as the hydrogen bonding interactions [46]. The MEP at a given point (x, y, z) in the vicinity of a molecule is defined in terms of the interaction energy between the electrical charge generated from the molecule's electrons and nuclei and a positive charge test (a proton)
by diffraction or by computational tools [3]. For the studied nitrofuran molecules,
*r*) = ∑ *j*=1 *K* \_ *Zj* | → *Rj* − →*r*| − ∫
*r*. Being a real physical property, MEP can be determined experimentally
ρ(→*r*′) *d*→ \_*r*′
(1)
*3.1.1 Biological recognition process ligand/receptor through the molecular*
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
maps and pattern recognition techniques.
**3.1 Computational**
[2, 3, 44–46].
located at <sup>→</sup>
*electrostatic potential*
**with biological activity against** *T. cruzi*
the MEP values were computed through Eq. (1) [45]
*V*(<sup>→</sup>
#### *Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
compounds. Chatbar et al. conducted a study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer [30]. Pourbasheer et al. performed 3D-QSAR and 2D-QSAR analyses on the series of compounds hepatitis C virus NS5B polymerase inhibitors [31]. Cramer applied the CoMFA methodology for a large majority of 116 biological targets and obtained acceptable 3D-QSAR models [32]. Cramer et al. introduced in the literature a novel alignment methodology for training or test set structures in 3D-QSAR [33]. Dong et al. performed QSAR analyses of aromatic heterocycle thiosemicarbazone analogues for finding novel tyrosinase inhibitors [34]. Dong et al. built 3D-QSAR models of dabigatran analogues as thrombin inhibitors [35]. Ding et al. performed 3D-QSAR models of 6-aryl-5-cyanopyrimidine derivatives to explore the structure requirements of LSD1 inhibitors [36].
Applications of MEP to investigate the key features of compounds that are necessary for their biological activities and thus proposing new derivatives as well as the construction of chemometric models as indicative of the most promising among the new derivatives for syntheses and biological assays were reported by us in literature [37–43]. Pinheiro et al. stated the use of MEP and partial least squares regression (PLS) method in the design of new artemisinin derivatives with activities against *Plasmodium falciparum* [37]. Cardoso et al., using MEP maps and multivariate QSAR, designed new artemisinin derivatives with antimalarial activity [38]. Ferreira et al., through MEP maps and multivariate analysis, designed antimalarial artemisinins [39]. Figueiredo et al. designed new derivatives of dispiro-1,2,4 trioxolones with activity against falciparum malaria [40]. Carvalho et al., through maps of MEP and pattern recognition methods, proposed new artemisinin derivatives with activity against *Leishmania donovani* [41]. Barbosa et al. used MEP maps and pattern recognition techniques to plan new derivatives of artemisinin anticancer HepG2 [42]. Cristino et al. proposed new derivatives of 10-substituted Deoartemisinis with activity against *P. falciparum* [43] through the use of MEP maps and pattern recognition techniques.
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ffe82432-4883-4adc-b03b-937c1baf5090.54 | **3. MEP and PR techniques as tools to design nitrofuran compounds with biological activity against** *T. cruzi*
#### **3.1 Computational**
*Cheminformatics and Its Applications*
phenomena is well documented [2–18].
directly measured property of the object [19].
accumulated perceptions in the MEP studies.
**of bioactive compounds: a brief review**
Reports of theoretical bases of MEP and the development of efficient computational methods state that MEP has become an important reactivity index in studies of a large variety of molecular interactions [1]. The usefulness of this theoretical approach in studies and interpretation of chemical, biochemical, and related
Chemometrics is a discipline that collects mathematical, statistical, information theory, and computer science tools to deal with complex chemical data [19–22]. PR techniques were introduced in the chemistry, at the beginning of the 1970s, to analyze various types of spectroscopic data. Since then, PR became part of chemometrics and has been an excellent tool to aid in the interpretation of chemical data to obtain relevant information in different application sectors of chemical science [19, 20]. PR techniques are especially useful for the classification of objects into discrete classes on the basis of measured features. A set of characteristic features of an object is considered as an abstract pattern that contains information about a not
The MEP and PR techniques have been used as independent strategies in the study of active compounds and lead to the proposal of new molecules for synthesis and biological testing. The joint applications of these powerful tools were described carefully, to unravel the structure-activity relationship of bioactive compounds, consequently proposing new molecules. Therefore, a more intense exploration of its
The design of molecules with a desired property is one of the objectives of chemoinformatics. In this chapter, we present a study of the application of MEP and PR techniques to design nitrofuran compounds with potential activity against *T. cruzi.* In the first step of our study, MEP maps will be used in an attempt to identify the key structural features of nitrofuran compounds that are necessary for their activities and investigate their probable interactions with a molecular receptor through recognition in a biological process. Subsequently, PR techniques are used to construct models that will be applied later to a forecast set constructed with the
potentials is needed in order to design biologically active compounds.
**2. MEP and chemometrics techniques as tools for the design**
According to the literature, MEP [1, 3] has been a tool of quantum chemistry used by researchers for several decades to study and understand the relationships between structure and activity of molecules. Among the papers that point out the importance of this tool in the matter, and consequently in the planning of bioactive compounds, we can mention those reported by Bernardinelli et al. [23] and by
Another tool, in the form of a set of techniques has been used emphatically over the years in the understanding of the structure-activity relationship of molecules is Chemometrics [25–27]. This set of techniques has also enables the planning of new biologically active compounds, and most of the developed research is focused on the construction of QSAR (quantitative structure-activity relationship) models. The combination of MEP and chemometrics as tools for designing new bioactive compounds has almost always been focused on the elaboration of quantitative models, for example, the CoMFA methodology [28]. This methodology was developed in the late 1980s by Cramer et al. [29]. Its application is richly extensive and recently it has been used in several studies of structure–activity relationships of bioactive
**1. Introduction**
**48**
Jefford et al. [24].
## *3.1.1 Biological recognition process ligand/receptor through the molecular electrostatic potential*
The MEP is also suitable for analyzing processes based on the "recognition" of one molecule by another as in drug-receptor and enzyme-substrate interactions, because it is through their potentials that the two species first "see" each other [2, 3, 44–46].
MEP for the electronic density is a very useful property for understanding the site of electrophilic attack and nucleophilic reactions as well as the hydrogen bonding interactions [46]. The MEP at a given point (x, y, z) in the vicinity of a molecule is defined in terms of the interaction energy between the electrical charge generated from the molecule's electrons and nuclei and a positive charge test (a proton) located at <sup>→</sup> *r*. Being a real physical property, MEP can be determined experimentally by diffraction or by computational tools [3]. For the studied nitrofuran molecules, the MEP values were computed through Eq. (1) [45] ρ(→*r*′) *d*→ \_*r*′
## different or by computational tools [ $\beta$ ]. For the studied attractor and becomes $\mathbf{MEP}$ values were computed through Eq. (1) [45]
$$\mathbf{V}(\vec{r}) = \sum\_{j=1}^{K} \frac{Z\_j}{|\vec{R}\_j - \vec{r}|} - \int \frac{\rho(\vec{r}')d\vec{r}'}{|\vec{r}' - \vec{r}|}\tag{1}$$
where K is the number of nuclei with charges *Zj*, located at position *Rj* and *ρ (*<sup>→</sup> *r)* is the electronic charge density. The first term on the right side of Eq. (1) represents the contribution of the nuclei, which is positive; the second term brings in the effect of the electrons, which is negative. In the investigation of the reactive sites of nitrofuran compounds, the MEP was evaluated through of the HF/6-31G method.
## *3.1.2 RP techniques*
In this section, we will make a brief presentation of the PR techniques used in this chapter. A deeper and detailed description of these matters can be found elsewhere [47–66].
## *3.1.2.1 Principal component analysis (PCA) technique*
When computing large multivariate data, it is mandatory to find and reduce unknown data trends using exploratory tools. The main idea of the PCA technique is to reduce the dimensionality of a data set consisting of large numbers of interrelated variables while retaining the variation present in the data set as much as possible. This can be achieved by transforming them into a new set of variables, the PCs, which are uncorrelated and ordered so that the first few retain most of the variation present in all of the original variables. As the final result, the PCA technique performs the selection of a small number of variables (molecular properties) considered better related to the dependent property or feature [67], in this study, the biological activity against *T. cruzi*.
| doab | 2025-04-07T04:13:04.422891 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.55 | *3.1.2.2 Hierarchical cluster analysis (HCA) technique*
This technique has become, together with PCA, another important tool in pattern recognition [67]. The purpose of using it is to display the data in such a way as to emphasize its natural clusters and patterns in a two-dimensional space. The results are presented as dendrograms. In HCA technique, the distances between objects or variables are calculated and computed through the similarity index which ranges from zero, that is, no similarity and large distance among objects, to one, for identical objects.
#### *3.1.2.3 K-nearest neighbor (KNN) technique*
The KNN technique [67] classifies the objects based on distance comparison among them. The multivariate Euclidean distances between every pair of objects with known class membership are calculated. The closest K objects are used to build the model. The optimal K is determined by cross-validation applied to the training set objects. The classification of a test object is determined based on the multivariate distance of this object with respect to the K objects in the training set. In this technique no assumption is made about the size and shape of the training set classes.
#### *3.1.2.4 Stepwise discriminant analysis (SDA) technique*
This technique separates objects from distinct populations and allocates new objects into populations previously defined. It uses a stepwise procedure in which, at each step, the most powerful variable is entered into the discriminant function. The SDA technique is anchored in the F-test for the significance of variables and at each step selects a variable based on its significance, and, after several steps, the most significant variables are extracted from the set in question [20, 68].
**51**
**Figure 1.**
*2D molecular structure for 5-nitrofuran-2-aldoxime.*
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
This SIMCA technique develops principal component models for each training set category. Its main objective is the reliable classification of new samples. When a prediction is made with the SIMCA technique, new samples insufficiently close to the PC space of a class are considered nonmembers. Furthermore, the technique requires that each training sample be pre-assigned to one of *Q* different categories, where *Q* is typically greater than one. It provides three possible outcome predictions: the sample fits only one pre-defined category, the sample does not fit any of the pre-defined categories, and the sample fits into more than one pre-defined
For the present chapter, we performed molecular calculations on an AMD PHENOM 955 X4 2.2 GHz processor with 4 Gb of RAM with the Gaussian 98 program package [69]. The MEP was computed from the electronic density, and the maps were displayed using the MOLEKEL software [70], while the PR models were
**Figure 1** shows the 2D structure of the 5-nitrofuran-2-aldoxim molecule [72] used in the selection of method/basis set (see Section 3.1.3.1). In **Figures 2** and **3** the 2D structures of the nitrofuran compounds from the training [73–75] and prediction sets are displayed, respectively. In this work, the nitrofuran molecules were defined as more active against *T. cruzi*, when in vitro *growth rate inhibition (GR) T. cruzi* ≥ 75, and as less active when in vitro *growth rate inhibition T.*
In general, the structure–activity relationship shows that for the compounds **1–6**, the increase in the carbon chain improves the activity against *T. cruzi*. The comparison between compounds **3** and **2** evidences increased activity by the substitution of the N atom by O. We can also notice that increasing the number of unsaturations and returning the nitrogen to the chain will lead to a decrease in biological activity (**7**, **8**). Still in relation to compound **1**, increasing the unsaturations, returning the atom of O, and increasing the carbon chain length (**9–12**) substantially increase the activity against *T. cruzi*. On the other hand, in compounds **13** and **14**, returning to an unsaturation in the main chain and introducing electron-withdrawing groups and more electronegative atoms, there is a decrease in chagasic activity. This evidence can also be verified for compounds
The molecular descriptors were obtained for the most stable conformation of each compound. These descriptors were computed to give information about the influence of electronic, steric, hydrophilic, and hydrophobic features on the antitrypanosomal activity of the studied nitrofurans. The atomic charges in this work were derived from the electrostatic potential obtained with HF/6-31G method/basis
carried out on a PC Pentium machine with the Pirouette program [71].
*3.1.2.5 Soft independent modeling of class analogy (SIMCA) technique*
*3.1.3 Computers, software, compounds, and molecular descriptors*
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
category [67].
*cruzi* < 75.
**16**, **17, 19–22**.
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
### *3.1.2.5 Soft independent modeling of class analogy (SIMCA) technique*
This SIMCA technique develops principal component models for each training set category. Its main objective is the reliable classification of new samples. When a prediction is made with the SIMCA technique, new samples insufficiently close to the PC space of a class are considered nonmembers. Furthermore, the technique requires that each training sample be pre-assigned to one of *Q* different categories, where *Q* is typically greater than one. It provides three possible outcome predictions: the sample fits only one pre-defined category, the sample does not fit any of the pre-defined categories, and the sample fits into more than one pre-defined category [67].
#### *3.1.3 Computers, software, compounds, and molecular descriptors*
For the present chapter, we performed molecular calculations on an AMD PHENOM 955 X4 2.2 GHz processor with 4 Gb of RAM with the Gaussian 98 program package [69]. The MEP was computed from the electronic density, and the maps were displayed using the MOLEKEL software [70], while the PR models were carried out on a PC Pentium machine with the Pirouette program [71].
**Figure 1** shows the 2D structure of the 5-nitrofuran-2-aldoxim molecule [72] used in the selection of method/basis set (see Section 3.1.3.1). In **Figures 2** and **3** the 2D structures of the nitrofuran compounds from the training [73–75] and prediction sets are displayed, respectively. In this work, the nitrofuran molecules were defined as more active against *T. cruzi*, when in vitro *growth rate inhibition (GR) T. cruzi* ≥ 75, and as less active when in vitro *growth rate inhibition T. cruzi* < 75.
In general, the structure–activity relationship shows that for the compounds **1–6**, the increase in the carbon chain improves the activity against *T. cruzi*. The comparison between compounds **3** and **2** evidences increased activity by the substitution of the N atom by O. We can also notice that increasing the number of unsaturations and returning the nitrogen to the chain will lead to a decrease in biological activity (**7**, **8**). Still in relation to compound **1**, increasing the unsaturations, returning the atom of O, and increasing the carbon chain length (**9–12**) substantially increase the activity against *T. cruzi*. On the other hand, in compounds **13** and **14**, returning to an unsaturation in the main chain and introducing electron-withdrawing groups and more electronegative atoms, there is a decrease in chagasic activity. This evidence can also be verified for compounds **16**, **17, 19–22**.
The molecular descriptors were obtained for the most stable conformation of each compound. These descriptors were computed to give information about the influence of electronic, steric, hydrophilic, and hydrophobic features on the antitrypanosomal activity of the studied nitrofurans. The atomic charges in this work were derived from the electrostatic potential obtained with HF/6-31G method/basis
**Figure 1.** *2D molecular structure for 5-nitrofuran-2-aldoxime.*
*Cheminformatics and Its Applications*
*3.1.2 RP techniques*
elsewhere [47–66].
identical objects.
*3.1.2.1 Principal component analysis (PCA) technique*
the biological activity against *T. cruzi*.
*3.1.2.3 K-nearest neighbor (KNN) technique*
*3.1.2.4 Stepwise discriminant analysis (SDA) technique*
*3.1.2.2 Hierarchical cluster analysis (HCA) technique*
where K is the number of nuclei with charges *Zj*, located at position *Rj* and *ρ (*<sup>→</sup>
is the electronic charge density. The first term on the right side of Eq. (1) represents the contribution of the nuclei, which is positive; the second term brings in the effect of the electrons, which is negative. In the investigation of the reactive sites of nitrofuran compounds, the MEP was evaluated through of the HF/6-31G method.
In this section, we will make a brief presentation of the PR techniques used in this chapter. A deeper and detailed description of these matters can be found
When computing large multivariate data, it is mandatory to find and reduce unknown data trends using exploratory tools. The main idea of the PCA technique is to reduce the dimensionality of a data set consisting of large numbers of interrelated variables while retaining the variation present in the data set as much as possible. This can be achieved by transforming them into a new set of variables, the PCs, which are uncorrelated and ordered so that the first few retain most of the variation present in all of the original variables. As the final result, the PCA technique performs the selection of a small number of variables (molecular properties) considered better related to the dependent property or feature [67], in this study,
This technique has become, together with PCA, another important tool in pattern recognition [67]. The purpose of using it is to display the data in such a way as to emphasize its natural clusters and patterns in a two-dimensional space. The results are presented as dendrograms. In HCA technique, the distances between objects or variables are calculated and computed through the similarity index which ranges from zero, that is, no similarity and large distance among objects, to one, for
The KNN technique [67] classifies the objects based on distance comparison among them. The multivariate Euclidean distances between every pair of objects with known class membership are calculated. The closest K objects are used to build the model. The optimal K is determined by cross-validation applied to the training set objects. The classification of a test object is determined based on the multivariate distance of this object with respect to the K objects in the training set. In this technique no assumption is made about the size and shape of the training set classes.
This technique separates objects from distinct populations and allocates new objects into populations previously defined. It uses a stepwise procedure in which, at each step, the most powerful variable is entered into the discriminant function. The SDA technique is anchored in the F-test for the significance of variables and at each step selects a variable based on its significance, and, after several steps, the
most significant variables are extracted from the set in question [20, 68].
*r)*
**50**
#### **Figure 2.** *2D molecular structure for nitrofurans (training set).*
set as implemented in the Gaussian program package. The electrostatic potential is obtained through the calculation of a set of punctual atomic charges so that it represents the possible best quantum molecular electrostatic potential for a set of points defined around the molecule [76, 77]. The charges derived from electrostatic potential present the advantage of being, in general, physically more satisfactory than the charges of Mülliken [78], especially with regard to biological activity.
The quantum–chemical descriptors employed and obtained with the Gaussian 98 program package [69] were total energy of molecules (TE), highest occupied molecular orbital (HOMO) energy, one level below to highest occupied molecular orbital (HOMO–1) energy; lowest unoccupied molecular orbital (LUMO) energy, one level about lowest unoccupied molecular orbital (LUMO+1) energy, HOMO energy–LUMO energy (gap energy), total dipole moment (μ), Mulliken's electronegativity (χ), atomic charges on the Nth atom (QN), molecular hardness (HD), and molecular softness (MS).
The physicochemical descriptors obtained with ChemPlus module [79] were total surface area (TSA), molecular volume (VOL), molecular refractivity (MR), and molecule hydration energy (MHE).
Molecular holistic (MH) descriptors were included with the purpose of representing different sources of chemical information in terms of molecular size, symmetry, and distribution of atoms in molecules. Also, we include topologic indices, connectivity indices, geometric descriptors, 3D-MoRSE descriptors, and Moriguchi octanol–water partition coefficient (MlogP). These descriptors were calculated with the Dragon software [80].
**53**
**Figure 3.**
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
*3.1.3.1 Theoretical approach and basis set used in the molecular calculations*
*2D molecular structures for nitrofurans for the prediction set.*
In the calculations with the nitrofuran compounds (**Figure 1**), quantum–chemical approaches were used [81–87]. We use Becke's three-parameter hybrid methods [81], the Lee-Yang-Parr (LYP) correlation functional [82], B3LYP and Becke's 1988 functional (BLYP) [83], Hartree-Fock (HF) method [84], Austin model 1 (AM1) method [85], Parametric Method Number 3 (PM3) [86], and standard basis sets [87] available in the Gaussian program package. In 5-nitrofuran-2-aldoxim, geometry optimization was carried out by B3LYP/6-21G, B3LYP/6-21G\*, B3LYP/6-31G, B3LYP/6-31-G\*, BLYP/6-21G, BLYP/6-21G\*, BLYP/6-31G, BLYP/6-31G\*, HF/6- 21G, HF/6-21G\*, HF/6-31G, and HF/6-31G\* approaches [81–84] and basis sets [87] and AM1 and PM3 approaches [85, 86] . The calculations were performed to find the approach and basis set that would present the best compromise between
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
#### **Figure 3.** *2D molecular structures for nitrofurans for the prediction set.*
| doab | 2025-04-07T04:13:04.423101 | 20-4-2021 18:19 | {
"license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/3.0/",
"book_id": "ffe82432-4883-4adc-b03b-937c1baf5090",
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"author": "",
"title": "Cheminformatics and its Applications",
"publisher": "IntechOpen",
"isbn": "9781838800680",
"section_idx": 55
} |
ffe82432-4883-4adc-b03b-937c1baf5090.56 | *3.1.3.1 Theoretical approach and basis set used in the molecular calculations*
In the calculations with the nitrofuran compounds (**Figure 1**), quantum–chemical approaches were used [81–87]. We use Becke's three-parameter hybrid methods [81], the Lee-Yang-Parr (LYP) correlation functional [82], B3LYP and Becke's 1988 functional (BLYP) [83], Hartree-Fock (HF) method [84], Austin model 1 (AM1) method [85], Parametric Method Number 3 (PM3) [86], and standard basis sets [87] available in the Gaussian program package. In 5-nitrofuran-2-aldoxim, geometry optimization was carried out by B3LYP/6-21G, B3LYP/6-21G\*, B3LYP/6-31G, B3LYP/6-31-G\*, BLYP/6-21G, BLYP/6-21G\*, BLYP/6-31G, BLYP/6-31G\*, HF/6- 21G, HF/6-21G\*, HF/6-31G, and HF/6-31G\* approaches [81–84] and basis sets [87] and AM1 and PM3 approaches [85, 86] . The calculations were performed to find the approach and basis set that would present the best compromise between
*Cheminformatics and Its Applications*
set as implemented in the Gaussian program package. The electrostatic potential is obtained through the calculation of a set of punctual atomic charges so that it represents the possible best quantum molecular electrostatic potential for a set of points defined around the molecule [76, 77]. The charges derived from electrostatic potential present the advantage of being, in general, physically more satisfactory than the charges of Mülliken [78], especially with regard to biological activity. The quantum–chemical descriptors employed and obtained with the Gaussian 98 program package [69] were total energy of molecules (TE), highest occupied molecular orbital (HOMO) energy, one level below to highest occupied molecular orbital (HOMO–1) energy; lowest unoccupied molecular orbital (LUMO) energy, one level about lowest unoccupied molecular orbital (LUMO+1) energy, HOMO energy–LUMO energy (gap energy), total dipole moment (μ), Mulliken's electronegativity (χ), atomic charges on the Nth atom (QN), molecular hardness (HD),
The physicochemical descriptors obtained with ChemPlus module [79] were total surface area (TSA), molecular volume (VOL), molecular refractivity (MR),
Molecular holistic (MH) descriptors were included with the purpose of representing different sources of chemical information in terms of molecular size, symmetry, and distribution of atoms in molecules. Also, we include topologic indices, connectivity indices, geometric descriptors, 3D-MoRSE descriptors, and Moriguchi octanol–water partition coefficient (MlogP). These descriptors were calculated with
**52**
**Figure 2.**
and molecular softness (MS).
the Dragon software [80].
and molecule hydration energy (MHE).
*2D molecular structure for nitrofurans (training set).*
computational time and accuracy of the information relative to the experimental data. The experimental structure of 5-nitrofuran-2-aldoxim molecule was retrieved from the Cambridge Structural Database CSD [72]. PCA and HCA techniques were used to compare the computed structures with different methods/basis sets of quantum chemistry with the experimental structure of 5-nitrofuran-2-aldoxim molecule to identify the appropriate method and the basis set for further calculations. The analyzes were carried out on an auto-scaled data matrix with dimension 26 × 5, where each row was associate 26 computed and 1 experimental geometry, and each column represented one of 5 geometrical parameters of the 5-nitrofuran-2-aldoxim molecule (bond lengths and bond angles). In order to compute all structures and perform calculations to obtain the molecular properties, the HF/6-31G method has selected (see Results and discussion section); the initial geometries of the nitrofurans (**Figures 2** and **3**) were built with the optimized geometry of the 5-nitrofuran-2-aldoxim molecule selected by PCA and HCA techniques. A conformational analysis for each compound was carried out with the MM+ algorithm [79], and the lowest energy conformation was submitted to a conformational search with the Gaussian program.
## **3.2 Results and discussion**
| doab | 2025-04-07T04:13:04.423844 | 20-4-2021 18:19 | {
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"author": "",
"title": "Cheminformatics and its Applications",
"publisher": "IntechOpen",
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"section_idx": 56
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ffe82432-4883-4adc-b03b-937c1baf5090.57 | *3.2.1 Quantum–chemical approach and basis set selection for the description of the geometries of nitrofurans*
The advantage in using the PCA and HCA techniques in this step was that all structural information are considered simultaneously and it takes into account the correlations among them. **Table 1** shows the theoretical and experimental structural information (bond lengths and bond angles) of the geometry of the 5-nitrofuran-2-aldoxim molecule. It was used with the aim to select using PCA and HCA techniques, which quantum–chemical approach and basis set give results closest to the experimental data [72].
The first two principal components explain 86.02% of the original information as follows: PC1 = 58.01% and PC2 = 28.02%. The PC1 versus PC2 scores plot is shown in **Figure 4**, from which it can be seen that the methods are discriminated into two classes according to PC2. The semiempirical approaches (AM1 and PM3) are at the top of the graph, while the other theoretical (HF, BLYP, and B3LYP) approaches and experimental data are at the bottom. Moreover, it can be seen that the HF/6-31G approach/basis set is the closest to the experimental data, indicating that they should be used in the development of this work.
Also, to investigate the most appropriate approach and basis set for further calculations, we used HCA. **Figure 5** shows the dendrogram obtained with complete linkage method; from this figure, we conclude that the theoretical approaches are distributed in a similar way as in PCA, i.e., HCA confirmed the PCA results. Moreover, we can observe that the HF/6-31G approach/basis set is closer to the experimental data therefore being the most suitable to carry out this work.
#### *3.2.2 MEP maps for compounds of the training set*
**Figure 6** shows the MEP maps for the nitrofurans in the training set. The analysis of these maps reveals that the most active compounds, in general, have the following characteristics:
(i) Compounds with an unsaturation and presenting O atom neighboring the carbonyl in the carbonic chain present greater electron density in the proximities of the furan ring with the decrease of the chain size. In these compounds (**4, 5,** and **6**), MEP
**55**
**Approaches/basis set**
**Geometric parameters** Bond length (Å)
C C2 3 C4C5 C C1 2 C O1 1 C O4 1 C N4 1
N
N
C C1 5 C N5 2
N
O
H4 1 Bond angle (°)
C O1 C1 4 O C1 C1 2 O C1 C1 5 C C5 C1 2 C N5 O2 2 C O1 C1 4
N
O2 H4 1
100.7
100.8
103.6
102.7
99.3
99.8
102.0
101.6
102.4
103.7
102.1
106.9
115.2
116.7
112.2
107.9
110.0
109.5
110.6
107.1
109.2
108.5
109.8
108.8
109.6
111.2
111.4
122.8
120.4
127.8
121.2
120.6
121.8
121.3
121.2
120.7
121.9
121.4
122.0
120.9
120.1
121.7
129.7
128.8
135.6
131.2
131.0
130.9
130.4
131.3
131.1
131.1
130.5
131.3
130.9
130.6
130.9
119.5
120.4
114.1
119.3
118.7
119.8
119.5
119.2
118.8
119.7
119.6
119.5
119.4
118.5
119.8
110.6
110.7
110.2
109.5
110.2
109.2
110.1
109.5
110.0
109.2
109.9
109.1
109.6
110.7
109.4
105.2
106.0
104.8
105.3
105.6
106.0
106.1
104.7
105.3
105.5
105.8
105.4
106.3
105.8
106.9
105.3
106.3
104.5
1.47
1.40
1.44
1.39
1.50
1.42
1.47
1.41
1.44
1.37
1.40
1.36
1.31
1.39
1.38
O2 4
1.29
1.28
1.29
1.28
1.32
1.31
1.31
1.30
1.26
1.25
1.26
1.25
1.31
1.29
1.27
1.43
1.44
1.43
1.44
1.44
1.45
1.44
1.45
1.45
1.46
1.45
1.46
1.45
1.45
1.45
1;29
1.23
1.27
1.23
1.32
1.26
1.30
1.26
1.26
1.20
1.23
1.20
1.20
1.22
1.22
O1 3
1.28
1.23
1.26
1.23
1.31
1.25
1.29
1.25
1.24
1.19
1.22
1.19
1.19
1.21
1.22
O1 2
1.41
1.43
1.41
1.43
1.43
1.44
1.43
1.48
1.40
1.43
1.41
1.42
1.46
1.47
1.42
1.41
1.43
1.41
1.43
1.43
1.44
1.43
1.49
1.40
1.43
1.40
1.42
1.45
1.48
1.42
1.38
1.35
1.38
1.35
1.41
1.37
1.40
1.37
1.36
1.37
1.35
1.33
1.40
1.38
1.35
1.40
1.37
1.39
1.36
1.42
1.39
1.42
1.38
1.37
1.39
1.37
1.33
1.34
1.37
1.37
1.38
1.38
1.38
1.38
1.39
1.39
1.40
1.39
1.35
1.35
1.35
1.35
1.33
1.38
1.36
1.36
1.36
1.37
1.37
1.38
1.38
1.39
1.38
1.34
1.39
1.34
1.34
1.40
1.39
1.34
1.42
1.42
1.42
1.42
1.43
1.47
1.43
1.42
1.43
1.43
1.43
1.43
1.43
1.43
1.41
**B3LYP/6-**
**B3LYP/6-**
**B3LYP/6-**
**B3LYP/6-**
**BLYP/6- 21G**
**BLYP/6- 21G\***
**BLYP/6- 31G**
**BLYP/6- 31G\***
**HF/6- 21G**
**HF/6- 21G\***
**HF/6- 31G**
**HF/6- 31G\***
**AM1**
**PM3**
**Exp [72]**
> **21G**
**21G\***
**31G**
**31G\***
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
#### *Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
*Cheminformatics and Its Applications*
each compound was carried out with the MM+
*of the geometries of nitrofurans*
closest to the experimental data [72].
that they should be used in the development of this work.
*3.2.2 MEP maps for compounds of the training set*
following characteristics:
**3.2 Results and discussion**
computational time and accuracy of the information relative to the experimental data. The experimental structure of 5-nitrofuran-2-aldoxim molecule was retrieved from the Cambridge Structural Database CSD [72]. PCA and HCA techniques were used to compare the computed structures with different methods/basis sets of quantum chemistry with the experimental structure of 5-nitrofuran-2-aldoxim molecule to identify the appropriate method and the basis set for further calculations. The analyzes were carried out on an auto-scaled data matrix with dimension 26 × 5, where each row was associate 26 computed and 1 experimental geometry, and each column represented one of 5 geometrical parameters of the 5-nitrofuran-2-aldoxim molecule (bond lengths and bond angles). In order to compute all structures and perform calculations to obtain the molecular properties, the HF/6-31G method has selected (see Results and discussion section); the initial geometries of the nitrofurans (**Figures 2** and **3**) were built with the optimized geometry of the 5-nitrofuran-2-aldoxim molecule selected by PCA and HCA techniques. A conformational analysis for
conformation was submitted to a conformational search with the Gaussian program.
The advantage in using the PCA and HCA techniques in this step was that all structural information are considered simultaneously and it takes into account the correlations among them. **Table 1** shows the theoretical and experimental structural information (bond lengths and bond angles) of the geometry of the 5-nitrofuran-2-aldoxim molecule. It was used with the aim to select using PCA and HCA techniques, which quantum–chemical approach and basis set give results
The first two principal components explain 86.02% of the original information as follows: PC1 = 58.01% and PC2 = 28.02%. The PC1 versus PC2 scores plot is shown in **Figure 4**, from which it can be seen that the methods are discriminated into two classes according to PC2. The semiempirical approaches (AM1 and PM3) are at the top of the graph, while the other theoretical (HF, BLYP, and B3LYP) approaches and experimental data are at the bottom. Moreover, it can be seen that the HF/6-31G approach/basis set is the closest to the experimental data, indicating
Also, to investigate the most appropriate approach and basis set for further calculations, we used HCA. **Figure 5** shows the dendrogram obtained with complete linkage method; from this figure, we conclude that the theoretical approaches are distributed in a similar way as in PCA, i.e., HCA confirmed the PCA results. Moreover, we can observe that the HF/6-31G approach/basis set is closer to the experimental data therefore being the most suitable to carry out this work.
**Figure 6** shows the MEP maps for the nitrofurans in the training set. The analysis of these maps reveals that the most active compounds, in general, have the
(i) Compounds with an unsaturation and presenting O atom neighboring the carbonyl in the carbonic chain present greater electron density in the proximities of the furan ring with the decrease of the chain size. In these compounds (**4, 5,** and **6**), MEP
*3.2.1 Quantum–chemical approach and basis set selection for the description*
algorithm [79], and the lowest energy
**54**
**Table 1.**
**57**
**Figure 4.**
**Figure 5.**
*semiempirical and semiempirical not.*
*semiempirical and semiempirical not.*
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
*Score plots of the two first PCs, PC1 and PC2, for the separation of the approaches basis sets into classes:*
maps show negative regions ranging from −82.99 to −4.87 kcal/mol. In the most active compound (**6**), as can be seen, the most negative values are in the nitro group, the O atom of the furan ring and the O atoms of the ester group (red and yellow). Also, the MEP maps of these compounds exhibit positive regions between the +4.54 and + 76.96 kcal/ mol values (green and blue). Compounds with double unsaturation, containing N atom next to the carbonyl, raise the electronic density with the increase of the carbonic chain. In the most active compound (**7**), the MEP map shows a region of negative values between −77.74 and − 1.31 kcal/mol, with the electron density concentrating mainly on the atoms of the nitro group, on the O atom of the furanic ring and on the N and O atoms of the amide group (red and yellow). According to the MEP map, these compounds pres-
*Dendrogram obtained with HCA technique for the separation of the approach basis set into two classes:*
(ii) Compounds with double unsaturation, containing O atom neighboring the carbonyl, raising the carbon chain, increase the electron density in the atoms of the
ent positive MEP between +5.64 and 61.21 kcal/mol (green and blue).
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
*Experimental and theoretical structural parameter of the 5-nirofuran-2-aldoxime.*
| doab | 2025-04-07T04:13:04.424006 | 20-4-2021 18:19 | {
"license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/3.0/",
"book_id": "ffe82432-4883-4adc-b03b-937c1baf5090",
"url": "https://mts.intechopen.com/storage/books/9236/authors_book/authors_book.pdf",
"author": "",
"title": "Cheminformatics and its Applications",
"publisher": "IntechOpen",
"isbn": "9781838800680",
"section_idx": 57
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ffe82432-4883-4adc-b03b-937c1baf5090.58 | *Cheminformatics and Its Applications*
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
#### **Figure 4.**
*Cheminformatics and Its Applications*
**56**
C C1 C2 3 O C1 4C3 C C3 N4 1 O C1 N4 1 C N4 O1 2 C N4 O1 3
O
**Table 1.**
*Experimental and theoretical structural parameter of the 5-nirofuran-2-aldoxime.*
N2 O1 3
127.3 *\*Refers to the base sets cited in the corresponding references.*
127.9
125.3
126.2
127.7
128.1
118.8
126.2
125.2
126.4
127.6
126.4
119.6
120.1
118.8
117.7
117.2
118.9
118.1
117.6
117.2
115.6
118.2
118.9
118.1
117.2
119.2
117.3
117.5
116.3
115.0
114.9
115.8
115.6
114.6
114.6
115.9
115.5
115.7
115.4
115.0
116.0
117.4
117.8
116.9
118.4
117.8
118.1
117.6
118.3
117.7
117.9
117.5
117.8
118.2
117.5
118.4
131.4
131.4
129.8
130.2
129.9
130.6
130.2
130.1
130.0
130.9
130.5
130.9
130.3
129.5
130.4
111.1
110.6
113.2
111.5
112.3
111.2
112.0
111.5
112.2
111.2
111.9
111.1
111.4
112.8
111.1
105.9
106.0
105.1
107.5
106.6
107.5
106.6
107.7
106.8
107.7
106.9
107.8
106.9
106.0
106.9
104.2
101.6
106
*Score plots of the two first PCs, PC1 and PC2, for the separation of the approaches basis sets into classes: semiempirical and semiempirical not.*
#### **Figure 5.**
*Dendrogram obtained with HCA technique for the separation of the approach basis set into two classes: semiempirical and semiempirical not.*
maps show negative regions ranging from −82.99 to −4.87 kcal/mol. In the most active compound (**6**), as can be seen, the most negative values are in the nitro group, the O atom of the furan ring and the O atoms of the ester group (red and yellow). Also, the MEP maps of these compounds exhibit positive regions between the +4.54 and + 76.96 kcal/ mol values (green and blue). Compounds with double unsaturation, containing N atom next to the carbonyl, raise the electronic density with the increase of the carbonic chain. In the most active compound (**7**), the MEP map shows a region of negative values between −77.74 and − 1.31 kcal/mol, with the electron density concentrating mainly on the atoms of the nitro group, on the O atom of the furanic ring and on the N and O atoms of the amide group (red and yellow). According to the MEP map, these compounds present positive MEP between +5.64 and 61.21 kcal/mol (green and blue).
(ii) Compounds with double unsaturation, containing O atom neighboring the carbonyl, raising the carbon chain, increase the electron density in the atoms of the
**Figure 6.** *MEP maps (kcal/mol) for nitrofurans (training set).*
nitro group, extending through the O atom of the furan ring to the O atoms of the ester group following the unsaturated chain. In these compounds (**10–12**), the MEP maps exhibit more negative values between −76.18 and − 6.36 kcal/mol (red and yellow). They exhibit positive MEP in the range of +0.63 to 67.42 kcal/mol (green and blue)
(iii) Compound with an unsaturation, N atom neighboring the carbonyl in the carbonic chain and bulky substituents, has higher electron density in the vicinity of the furan ring and in the N and O atoms of the amide group. In this compound
**59**
figure.
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
(**23**), the MEP map shows a negative region (red and yellow) between −73.10 and − 1.59 kcal/mol on the mentioned atoms and positive region between +5.56 and 69.91 kcal/mol (green and blue). The electron density around the nitro group, the O atom of the furan ring, and other atoms may induce the nitrofurans to show antitrypanosomal activity, suggesting the complexation in those regions with the active
From the above discussion, as a rule, to plan more active nitrofurans, we can assume we resort to one of the basic structures of the most active compounds and introduce groups of atoms or substituents electron donors enhancing the key
To perform the chemometric modeling, all variables were auto-scaled as preprocessing so that they could be standardized and so they could have the same importance regarding the scale. Furthermore, given a large quantity of multivariate data available, it was necessary to reduce the number of variables. Thus if any two descriptors had a high Pearson correlation coefficient (r ˃ 0.8), one of the two was excluded from the matrix at random, since theoretically they describe the same property [88]; they also have a high correlation with antitrypanosomal activity, and only one of them is enough to be used as independent variable in a predictive model.
Four molecular descriptors were selected for PCA model. The molecular descrip-
tors (QN1, gap energy, Mor05u, and MlogP), in vitro *T. cruzi* growth inhibition (experimental data), and activity and correlation matrix including all data for 23 nitrofurans can be seen in **Table 2**. The correlation between descriptors is less than 0.786. The first three principal components (PCs) describing 96.48 of the original information for the 23 are as follows: 45.70, 30.91, and 19.87%. PC1-PC2 scores for the samples are shown in **Figure 7**. From this figure, we can see that the nitrofurans are distributed into two distinct regions in PC1. The more active compounds are on the left side (**4–7, 10–12, 18,** and **23**) and the less active on the right side (**1–3, 8, 9, 13–17,** and **19–22**). According to **Figure 8**, the MlogP descriptor is responsible for displaying more active compounds on the left side, while the gap energy, QN1, and Mor05u descriptors displayed fewer active compounds for the right side from this
**Table 3** shows the loading vectors for PC1, PC2, and PC3. According to this
PC1 = 0.20 (QN1) + 0.06 (Gap energy) + 0.71 (Mor05u) − 68 (MlogP). (2)
From this equation, more active nitrofurans, in general, can be obtained when we have lower values for the QN1 combined with lower values for Gap energy and
The results of the HCA model are displayed in the dendrogram in **Figure 9** and are similar to those of PCA model. The nitrofurans are fairly well grouped according to their activity. From this figure, the two clusters (+ and −) mirror the same two
table, PC1 can be expressed through the following equation:
Mor05u and higher values for MlogP.
classes displayed by PCA model (**Figure 7**).
*3.2.3.2 HCA model*
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
*3.2.3 Chemometric modeling*
*3.2.3.1 PCA model*
site of the receptor in a biological recognition process.
structural features that are necessary for their activities.
#### *Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
(**23**), the MEP map shows a negative region (red and yellow) between −73.10 and − 1.59 kcal/mol on the mentioned atoms and positive region between +5.56 and 69.91 kcal/mol (green and blue). The electron density around the nitro group, the O atom of the furan ring, and other atoms may induce the nitrofurans to show antitrypanosomal activity, suggesting the complexation in those regions with the active site of the receptor in a biological recognition process.
From the above discussion, as a rule, to plan more active nitrofurans, we can assume we resort to one of the basic structures of the most active compounds and introduce groups of atoms or substituents electron donors enhancing the key structural features that are necessary for their activities.
#### *3.2.3 Chemometric modeling*
*Cheminformatics and Its Applications*
**58**
(green and blue)
*MEP maps (kcal/mol) for nitrofurans (training set).*
**Figure 6.**
nitro group, extending through the O atom of the furan ring to the O atoms of the ester group following the unsaturated chain. In these compounds (**10–12**), the MEP maps exhibit more negative values between −76.18 and − 6.36 kcal/mol (red and yellow). They exhibit positive MEP in the range of +0.63 to 67.42 kcal/mol
(iii) Compound with an unsaturation, N atom neighboring the carbonyl in the carbonic chain and bulky substituents, has higher electron density in the vicinity of the furan ring and in the N and O atoms of the amide group. In this compound
To perform the chemometric modeling, all variables were auto-scaled as preprocessing so that they could be standardized and so they could have the same importance regarding the scale. Furthermore, given a large quantity of multivariate data available, it was necessary to reduce the number of variables. Thus if any two descriptors had a high Pearson correlation coefficient (r ˃ 0.8), one of the two was excluded from the matrix at random, since theoretically they describe the same property [88]; they also have a high correlation with antitrypanosomal activity, and only one of them is enough to be used as independent variable in a predictive model.
#### *3.2.3.1 PCA model*
Four molecular descriptors were selected for PCA model. The molecular descriptors (QN1, gap energy, Mor05u, and MlogP), in vitro *T. cruzi* growth inhibition (experimental data), and activity and correlation matrix including all data for 23 nitrofurans can be seen in **Table 2**. The correlation between descriptors is less than 0.786. The first three principal components (PCs) describing 96.48 of the original information for the 23 are as follows: 45.70, 30.91, and 19.87%. PC1-PC2 scores for the samples are shown in **Figure 7**. From this figure, we can see that the nitrofurans are distributed into two distinct regions in PC1. The more active compounds are on the left side (**4–7, 10–12, 18,** and **23**) and the less active on the right side (**1–3, 8, 9, 13–17,** and **19–22**). According to **Figure 8**, the MlogP descriptor is responsible for displaying more active compounds on the left side, while the gap energy, QN1, and Mor05u descriptors displayed fewer active compounds for the right side from this figure.
**Table 3** shows the loading vectors for PC1, PC2, and PC3. According to this table, PC1 can be expressed through the following equation:
| doab | 2025-04-07T04:13:04.424521 | 20-4-2021 18:19 | {
"license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/3.0/",
"book_id": "ffe82432-4883-4adc-b03b-937c1baf5090",
"url": "https://mts.intechopen.com/storage/books/9236/authors_book/authors_book.pdf",
"author": "",
"title": "Cheminformatics and its Applications",
"publisher": "IntechOpen",
"isbn": "9781838800680",
"section_idx": 58
} |
ffe82432-4883-4adc-b03b-937c1baf5090.59 | PC1 = 0.20 (QN1) + 0.06 (Gap energy) + 0.71 (Mor05u) − 68 (MlogP). (2)
From this equation, more active nitrofurans, in general, can be obtained when we have lower values for the QN1 combined with lower values for Gap energy and Mor05u and higher values for MlogP.
#### *3.2.3.2 HCA model*
The results of the HCA model are displayed in the dendrogram in **Figure 9** and are similar to those of PCA model. The nitrofurans are fairly well grouped according to their activity. From this figure, the two clusters (+ and −) mirror the same two classes displayed by PCA model (**Figure 7**).
*Inhibitor concentration of 5 μM. b Growth inhibition ≥ 75, more active (MA)<sup>c</sup> , and growth inhibition < 75, less active (LA)<sup>c</sup> .*
#### **Table 2.**
*Values for the four most important descriptors which classify the studied nitrofuran compounds, in vitro T. cruzi growth inhibition (experimental data), activity, and correlation matrix.*
#### *3.2.3.3 KNN model*
**Table 4** shows the results for the KNN models obtained with the KNN technique and constructed with one (1NN) to four (4NN) nearest neighbors. To all models the percentage of correct information was 100%. We used the model 4NN because the greater the number of the nearest neighbors, the better the reliability of the KNN technique, and the same was used for validation of the training set from **Figure 2**.
#### *3.2.3.4 SDA model*
In the construction of the SDA model, the discrimination functions for groups more active and less active, respectively, are given below:
**61**
**Figure 7.**
**Figure 8.**
Group MA (more active):
Group LA (less active):
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
0.51(QN1) + 0.43Gap energy + 3.05Mor05u − 1.5MlogP–0.62 (3)
*Loading vector plots of the first PCs, PC1 and PC2, for four variables responsible for the separation of the 23*
*nitrofurans (training set) into two classes: (+) more active and (−) less active against T. cruzi.*
*Score plots of the two first PCs, PC1 and PC2, responsible for the separation of the 23 nitrofurans (training set)*
−0.80QN1 − 0.67Gap energy − 4.75Mor05u + 2.34MlogP − 3.92 (4)
Also, through the discrimination functions, Eqs. (3) and (4), and of the value of each descriptor for the nitrofurans, we obtain the classification matrix by using all compounds from the training set (**Table 5**). The classification error was 0.00% resulting in a satisfactory separation of more active and less active compounds. From SDA model, the allocation rule was derived when the activity against *T. cruzi* of new nitrofurans is investigated: (a) initially calculate, for the new compound, the value of the most important descriptors obtained in the construction of the SDA model, (b) put these auto-scaled values in the two discrimination functions
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
*into two classes: (+) more active and (−) less active against T. cruzi.*
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
#### **Figure 7.**
*Cheminformatics and Its Applications*
**Nitrofurans QN1 Gap**
**energy (kcal/mol)**
− 0.201 220.9 −3.966 1.135 30 LA − 0.201 220.9 −2.938 1.708 20 LA − 0.165 220.9 −2.723 0.181 32 LA 4+ 0.165 226.5 −6.869 1.980 92.7 MA 5+ 0.165 225.3 −7.439 3.155 83.7 MA 6+ 0.169 229.7 −0.016 1.708 96.2 MA 7+ 0.164 208.3 −7.439 1.889 81.9 MA − 0.164 205.2 −4.854 0.334 26.7 LA − 0.166 215.9 −3.292 0.478 58 LA 10+ 0.166 215.9 −7.470 2.146 90 MA 11+ 0.164 208.3 −5.674 1.354 87.4 MA 12+ 0.164 208.3 −8.435 3.307 92.3 MA − 0.167 195.2 −4.338 0.751 12 LA − 0.161 203.3 −2.872 0.501 3 LA − 0.167 208.3 −4.217 0.411 30 LA − 0.167 225.3 −2.373 0.609 20 LA − 0.167 225.9 −4.054 1.063 6 LA 18+ 0.167 225.3 −6.339 2.001 75 MA − 0.166 225.3 −4.145 0.398 31 LA − 0.167 226.5 −4.786 0.667 35 LA − 0.167 225.3 −3.398 1.157 23 LA − 0.166 218.4 −3.876 0.802 14 LA 23+ 0.166 224.6 −6.314 3.014 90.5 MA
**Mor05u MlogP % in vitro**
*T. cruzi* **growth inhibitiona,b**
*, and growth inhibition < 75, less active*
**Activityc**
**60**
*a*
*(LA)<sup>c</sup> .*
**Table 2.**
*3.2.3.3 KNN model*
Gap energy −0.171
*Inhibitor concentration of 5 μM. b*
Mor05u 0.27 −0.006
MlogP 0.026 −0.184 −0.785
*growth inhibition (experimental data), activity, and correlation matrix.*
*3.2.3.4 SDA model*
**Table 4** shows the results for the KNN models obtained with the KNN technique and constructed with one (1NN) to four (4NN) nearest neighbors. To all models the percentage of correct information was 100%. We used the model 4NN because the greater the number of the nearest neighbors, the better the reliability of the KNN technique, and the same was used for validation of the training set from **Figure 2**.
*Growth inhibition ≥ 75, more active (MA)<sup>c</sup>*
*Values for the four most important descriptors which classify the studied nitrofuran compounds, in vitro T. cruzi*
In the construction of the SDA model, the discrimination functions for groups
more active and less active, respectively, are given below:
*Score plots of the two first PCs, PC1 and PC2, responsible for the separation of the 23 nitrofurans (training set) into two classes: (+) more active and (−) less active against T. cruzi.*
#### **Figure 8.**
*Loading vector plots of the first PCs, PC1 and PC2, for four variables responsible for the separation of the 23 nitrofurans (training set) into two classes: (+) more active and (−) less active against T. cruzi.*
Group MA (more active):
0.51(QN1) + 0.43Gap energy + 3.05Mor05u − 1.5MlogP–0.62 (3)
Group LA (less active):
$$-0.80 \text{QN1} \text{ - } 0.67 \text{Gap energy} \text{ - } 4.75 \text{Mor} \text{05u} \text{ + } 2.34 \text{MlogP} \text{ - } 3.92 \qquad \text{(4)}$$
Also, through the discrimination functions, Eqs. (3) and (4), and of the value of each descriptor for the nitrofurans, we obtain the classification matrix by using all compounds from the training set (**Table 5**). The classification error was 0.00% resulting in a satisfactory separation of more active and less active compounds. From SDA model, the allocation rule was derived when the activity against *T. cruzi* of new nitrofurans is investigated: (a) initially calculate, for the new compound, the value of the most important descriptors obtained in the construction of the SDA model, (b) put these auto-scaled values in the two discrimination functions
#### *Cheminformatics and Its Applications*
**Table 3.**
*Variables matrix for the first three principal components.*
#### **Figure 9.**
*Dendrogram obtained with HCA technique for the separation of the nitrofurans into two classes: (+) more active and (−) less active against T. cruzi.*
#### **Table 4.**
*Classification obtained with the KKN technique.*
performed in this work, and (c) check which discrimination function, Eq. (3) or Eq. (4), presents higher value. The new compound is more active if it is related to discrimination function of group more active and vice versa.
In order to check the reliability of the model, the "leave-one-out technique" was employed. One nitrofuran compound is excluded from the data set, and the remaining compounds are used in building the classification functions.
Subsequently, the removed analogue is classified according the generated classification functions. In the further step, the omitted compound is included, and a new nitrofuran is removed, and the procedure goes on until the last compound is removed. In **Table 6** the results obtained with the cross-validation model are summarized.
**63**
biological receptor.
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
**Classification group or class Number of compounds More active Less active** Group (Class): more active 9 9 0 Group (Class): less active 14 0 14 Total 23 9 14 % Correct information — 100 100
**Classification group or class Number of compounds More active Less active** Group (class): more active 9 9 0 Group (class): less active 14 0 14 Total 23 9 14 % correct information — 100 100
**True group**
**True group**
The SIMCA model were built with the same descriptors as PCA, HCA, KNN, and SDA models and used two (2) PCs in the modeling of the two classes: more active nitrofurans (**4–7**, **10–12**, **18,** and **23**) and less active (**1–3, 8, 9, 13–17,** and **19–22**) nitrofurans. In **Table 7**, the obtained results for the SIMCA model are shown. In this case, the information percentage was also 100%. According to the PCA, HCA, KNN, SDA, and SIMCA models, we can also notice that the QN1, gap energy, Mor05u, and MlogP descriptors are key properties for explaining the anti-*T.*
*Classification matrix obtained by using SDA technique with cross-validation technique.*
As QN1, gap energy, Mor05u, and MlogP properties were selected in the chemometric modeling as the most important characteristics to describe the antitrypanosomal activity, some considerations about them may be relevant to the understanding of the behavior of more active nitrofurans. According to classical chemical theory, chemical interactions can be classified in two categories: electrostatic (polar) or orbital (covalent). Electrical charges in the molecule are indubitably the impelling cause of electrostatic interactions. It has been demonstrated that local electron densities or charges are important in many chemical reactions, physicochemical properties, and ligand–receptor interactions [89, 90]. Thus, charge-based parameters have been widely employed as chemical reactivity indices or as measures of weak intermolecular interactions. Many quantum–chemical descriptors are derived from the partial charge distribution in a molecule or from the electron densities on particular atoms [91]. From **Table 2**, we can observe that, in general, QN1 for more active analogues must present lower values than the less active ones. This is an indication that biological processes can occur through electrostatic interactions between the more active nitrofurans and an eventual
Gap energy is an important stability index. A large gap energy implies high stability for the molecule in the sense of its lower reactivity in chemical reactions.
*cruzi* activity of the nitrofurans training set (**Figure 2**).
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
*Classification matrix obtained using SDA technique.*
*3.2.3.5 SIMCA model*
**Table 5.**
**Table 6.**
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
#### **Table 5.**
*Cheminformatics and Its Applications*
*Variables matrix for the first three principal components.*
**62**
summarized.
**Figure 9.**
**Table 3.**
**Table 4.**
*active and (−) less active against T. cruzi.*
*Classification obtained with the KKN technique.*
performed in this work, and (c) check which discrimination function, Eq. (3) or Eq. (4), presents higher value. The new compound is more active if it is related to
*Dendrogram obtained with HCA technique for the separation of the nitrofurans into two classes: (+) more*
**Variable PC1 PC2 PC3** QN1 0.20 0.66 0.69 Gap energy 0.06 −0.70 0.70 Mor05u 0.71 0.11 −0.10 MlogP −0.68 0.26 0.17
**Category Number of compounds Compounds incorrectly classified**
Class:more active 9 0 0 0 0 Class: less active 14 0 0 0 0 Total 23 0 0 0 0 % Correct information 100 100 100 100
**1NN 2NN 3NN 4NN**
In order to check the reliability of the model, the "leave-one-out technique" was employed. One nitrofuran compound is excluded from the data set, and the remain-
Subsequently, the removed analogue is classified according the generated classification functions. In the further step, the omitted compound is included, and a new nitrofuran is removed, and the procedure goes on until the last compound is removed. In **Table 6** the results obtained with the cross-validation model are
discrimination function of group more active and vice versa.
ing compounds are used in building the classification functions.
*Classification matrix obtained using SDA technique.*
#### **Table 6.**
*Classification matrix obtained by using SDA technique with cross-validation technique.*
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ffe82432-4883-4adc-b03b-937c1baf5090.60 | *3.2.3.5 SIMCA model*
The SIMCA model were built with the same descriptors as PCA, HCA, KNN, and SDA models and used two (2) PCs in the modeling of the two classes: more active nitrofurans (**4–7**, **10–12**, **18,** and **23**) and less active (**1–3, 8, 9, 13–17,** and **19–22**) nitrofurans. In **Table 7**, the obtained results for the SIMCA model are shown. In this case, the information percentage was also 100%. According to the PCA, HCA, KNN, SDA, and SIMCA models, we can also notice that the QN1, gap energy, Mor05u, and MlogP descriptors are key properties for explaining the anti-*T. cruzi* activity of the nitrofurans training set (**Figure 2**).
As QN1, gap energy, Mor05u, and MlogP properties were selected in the chemometric modeling as the most important characteristics to describe the antitrypanosomal activity, some considerations about them may be relevant to the understanding of the behavior of more active nitrofurans. According to classical chemical theory, chemical interactions can be classified in two categories: electrostatic (polar) or orbital (covalent). Electrical charges in the molecule are indubitably the impelling cause of electrostatic interactions. It has been demonstrated that local electron densities or charges are important in many chemical reactions, physicochemical properties, and ligand–receptor interactions [89, 90]. Thus, charge-based parameters have been widely employed as chemical reactivity indices or as measures of weak intermolecular interactions. Many quantum–chemical descriptors are derived from the partial charge distribution in a molecule or from the electron densities on particular atoms [91]. From **Table 2**, we can observe that, in general, QN1 for more active analogues must present lower values than the less active ones. This is an indication that biological processes can occur through electrostatic interactions between the more active nitrofurans and an eventual biological receptor.
Gap energy is an important stability index. A large gap energy implies high stability for the molecule in the sense of its lower reactivity in chemical reactions.
#### **Table 7.**
*Classification obtained by using SIMCA technique.*
It is an approximation of the lowest excitation energy of the molecule and can be used for the definition of absolute and activation hardness [89, 90]. In **Table 2**, we can observe that, in general, the more active nitrofurans present lower gap energy than the less active ones. This indicates that the more active nitrofurans have a great probability of interacting with the biological receptor through a charge transfer mechanism.
Mor05u is a 3D-MoRSE descriptor based on the idea of obtaining information from 3D atomic coordinates through the transformed used in electrons diffraction studies [91] and is strictly related to the stereochemistry of the compounds [92]. According to **Table 2**, the more active nitrofurans present lower values of Mor5u. This may be, in general, an indication of the importance of the stereochemical properties of the more active nitrofurans in a possible mechanism of action of its own.
MlogP is an important hydrophobic descriptor in diverse biochemical, pharmacological, and toxicological processes involved in drug absorption [93]. As identified in **Table 2**, the more active reported nitrofurans exhibit the higher MlogP values. This is an indication that in processes involving nitrofurans and a biological receptor, hydrophobic interactions may be important in the mechanism of action of these compounds.
Knowing the performance of the RP models constructed for the 23 studied nitrofurans, we decided to apply them to a series of eight compounds (**Figure 3**) designed to maintain the key structural features that are necessary for their biological activities evidenced by the MEP maps of the compounds of the training set. The basic nucleus of these compounds corresponds to that of the most active nitrofurans with double unsaturation, containing vicinal O atom to carbonyl (see compounds **10**–**12**). The eight molecules proposed for the study of prediction of activity were drawn with the help of one of the collaborators of this work, who belong to the research group in organic chemistry of the Federal University of Pará, Brazil, and the most promising syntheses are in progress. In the future, antitrypanosomal tests with the most promising nitrofurans can be used to validate our RP models.
The results obtained of the application of the PR models (PCA, HCA, KNN, SDA, and SIMCA) and the descriptors for the compounds of the prediction set are summarized in **Tables 8** and **9**, respectively. In **Table 8**, the compounds **25** and **30** were predicted as more active against *T. cruzi* with the five models. Only the KNN model predicted compound **26** as the most active. Meanwhile, only the PCA and HCA models predicted compound **31** as the most active. On the other hand, all models, except the SDA model, predicted compounds **24**, **27**, and **28** as the most active. In turn, the SIMCA model did not classify compounds **29** and **31** into any of the two classes. Thus, we can consider nitrofurans **25** and **30** as potentially more active in a future test against *T. cruzi*. For the values reported for compounds **25** and **30** (**Table 9**), it can be shown that in order to design more active nitrofurans we must combine smaller values for the descriptors QN1, gap energy, and Mor05u with higher value for the descriptor MlogP.
**65**
process.
**Table 8.**
**Table 9.**
**3.3 Concluding remarks**
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
*Results of application of chemometric models for the nitrofurans of the prediction set.*
**Nitrofuran PCA model HCA model KNN model SDA model SIMCA model** MA MA MA LA MA MA MA MA MA MA LA LA MA LA LA MA MA MA LA MA MA MA MA LA MA MA MA MA MA 0 MA MA MA MA MA MA MA LA LA 0
**Nitrofuran QN1 Gap energy (kcal/mol) Mor05u MLogP** 0.165 205.2 −6.352 3.155 0.165 203.3 −7.332 2.250 0.165 204.6 −5.835 1.146 0.169 203.9 −6.164 2.508 0.166 203.9 −7.146 1.875 0.164 229.7 −8.201 3.854 0.164 229.7 −6.421 3.373 0.164 223.4 −5.525 2.167
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
*3.2.4 MEP maps for compounds of the prediction set*
*Values for descriptors for the prediction set.*
the range + 4.84 to +57.58 kcal/mol (green and blue).
**Figure 10** shows the MEP maps for the most active nitrofurans in the validation set (**25** and **30**). Also, in these compounds, as can be seen, raising the carbon chain increases the electron density in the atoms of the nitro group, extending through the O of the furan ring to the O atoms of the ester group accompanying the unsaturated chain. In these compounds, the MEP maps show more negative values between −74.27 and − 1.76 kcal/mol (red and yellow). They exhibit positive MEP in
The negative MEP region of compounds **25** and **30**, similar to the more active compounds in the training set, is susceptible to attack in a biological recognition
MEP and chemometric techniques in the last decades have become efficient tools in the study of the structure–activity relationships of bioactive molecules. The use of such tools has occurred through the inherent principles of each or combining their potentials to more efficiently unravel information about the structure–activity relationships of pharmacologically interesting compounds. This chapter is circumscribed in this second possibility. MEP maps were constructed for 23 nitrofurans with activity against *T. cruzi* reported in the literature. The key structural features
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
#### **Table 8.**
*Cheminformatics and Its Applications*
TOTAL 23
*Classification obtained by using SIMCA technique.*
mechanism.
**Table 7.**
action of its own.
these compounds.
It is an approximation of the lowest excitation energy of the molecule and can be used for the definition of absolute and activation hardness [89, 90]. In **Table 2**, we can observe that, in general, the more active nitrofurans present lower gap energy than the less active ones. This indicates that the more active nitrofurans have a great probability of interacting with the biological receptor through a charge transfer
**Category Number of compounds Correct classification**
Class: more active 9 9 Class: less active 14 14
% correct information 100
Mor05u is a 3D-MoRSE descriptor based on the idea of obtaining information from 3D atomic coordinates through the transformed used in electrons diffraction studies [91] and is strictly related to the stereochemistry of the compounds [92]. According to **Table 2**, the more active nitrofurans present lower values of Mor5u. This may be, in general, an indication of the importance of the stereochemical properties of the more active nitrofurans in a possible mechanism of
MlogP is an important hydrophobic descriptor in diverse biochemical, pharmacological, and toxicological processes involved in drug absorption [93]. As identified in **Table 2**, the more active reported nitrofurans exhibit the higher MlogP values. This is an indication that in processes involving nitrofurans and a biological receptor, hydrophobic interactions may be important in the mechanism of action of
Knowing the performance of the RP models constructed for the 23 studied nitrofurans, we decided to apply them to a series of eight compounds (**Figure 3**) designed to maintain the key structural features that are necessary for their biological activities evidenced by the MEP maps of the compounds of the training set. The basic nucleus of these compounds corresponds to that of the most active nitrofurans with double unsaturation, containing vicinal O atom to carbonyl (see compounds **10**–**12**). The eight molecules proposed for the study of prediction of activity were drawn with the help of one of the collaborators of this work, who belong to the research group in organic chemistry of the Federal University of Pará, Brazil, and the most promising syntheses are in progress. In the future, antitrypanosomal tests
with the most promising nitrofurans can be used to validate our RP models.
The results obtained of the application of the PR models (PCA, HCA, KNN, SDA, and SIMCA) and the descriptors for the compounds of the prediction set are summarized in **Tables 8** and **9**, respectively. In **Table 8**, the compounds **25** and **30** were predicted as more active against *T. cruzi* with the five models. Only the KNN model predicted compound **26** as the most active. Meanwhile, only the PCA and HCA models predicted compound **31** as the most active. On the other hand, all models, except the SDA model, predicted compounds **24**, **27**, and **28** as the most active. In turn, the SIMCA model did not classify compounds **29** and **31** into any of the two classes. Thus, we can consider nitrofurans **25** and **30** as potentially more active in a future test against *T. cruzi*. For the values reported for compounds **25** and **30** (**Table 9**), it can be shown that in order to design more active nitrofurans we must combine smaller values for the descriptors QN1, gap energy, and Mor05u with
**64**
higher value for the descriptor MlogP.
*Results of application of chemometric models for the nitrofurans of the prediction set.*
#### **Table 9.**
*Values for descriptors for the prediction set.*
### *3.2.4 MEP maps for compounds of the prediction set*
**Figure 10** shows the MEP maps for the most active nitrofurans in the validation set (**25** and **30**). Also, in these compounds, as can be seen, raising the carbon chain increases the electron density in the atoms of the nitro group, extending through the O of the furan ring to the O atoms of the ester group accompanying the unsaturated chain. In these compounds, the MEP maps show more negative values between −74.27 and − 1.76 kcal/mol (red and yellow). They exhibit positive MEP in the range + 4.84 to +57.58 kcal/mol (green and blue).
The negative MEP region of compounds **25** and **30**, similar to the more active compounds in the training set, is susceptible to attack in a biological recognition process.
#### **3.3 Concluding remarks**
MEP and chemometric techniques in the last decades have become efficient tools in the study of the structure–activity relationships of bioactive molecules. The use of such tools has occurred through the inherent principles of each or combining their potentials to more efficiently unravel information about the structure–activity relationships of pharmacologically interesting compounds. This chapter is circumscribed in this second possibility. MEP maps were constructed for 23 nitrofurans with activity against *T. cruzi* reported in the literature. The key structural features
**Figure 10.** *MEP maps (kcal/mol) for most promising nitrofurans in the prediction set against T. cruzi.*
required for antitrypanosomal activity, along with chemical intuition, allowed the introduction of substituents in one of the most active nitrofurans in the training set to obtain eight new derivatives.
PR models (PCA, HCA, KNN, SDA, and SIMCA) were constructed and demonstrated that 23 nitrofurans can be classified into two classes or groups: more active and less active according to their degrees of activity against *T. cruzi*. The properties QN1, gap energy, Mor05u, and MlogP are responsible for the classification into more active and less active studied nitrofurans. It is interesting to notice that these properties represent three distinct classes of interactions between the nitrofurans and the biological receptor: electronic (QN1 and gap energy), steric (Mor05u), and hydrophobic (MlogP). Here it is important to mention that Paulino et al.*,* studying the influence of molecular parameters on the activity of 5-nitrofurans against *T. cruzi,* reported the importance of electronic properties and molecular hydrophobicity as well as the variation of the nitrofurans electronic structure to explain the greater activity of these compounds as inhibitors of the growth of this protozoan [94].
The results of the application of PR models on the validation set evidenced two nitrofurans (**25** and **30**) as more promising for synthesis and biological assays, which in the future can be used to validate our PR models.
| doab | 2025-04-07T04:13:04.425651 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.61 | **Acknowledgements**
We gratefully acknowledge the financial support of the Brazilian agencies: Conselho Nacional de Desenvolvimento Científico e Tecnológico and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. The authors would like to thank the Virtual Computational Chemistry Laboratory (VCCLAB–Munich) and the Swiss Center for Scientific Computing for the use of the DRAGON and MOLEKEL software, respectively. We employed computing facilities at the Laboratório de Química Teórica e Computacional (LQTC)–Universidade Federal do Pará.
**67**
**Author details**
Pará, Brazil
Marcos Antônio B. dos Santos1
Márcio de Souza Farias2
Edilson Luiz C. de Aquino2
Antônio Florêncio de Figueiredo3
Raimundo Dirceu de P. Farreira<sup>2</sup>
1 University of the State of Pará, Pará, Brazil
\*Address all correspondence to: [email protected]
provided the original work is properly cited.
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*
, Luã Felipe S. de Oliveira2
, Heriberto Rodrigues Bitencourt4
and José Ciríaco-Pinheiro2
2 Computational and Theoretical Chemistry Laboratory, Federal University of Pará,
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,
3 Federal Institute of Education, Science and Technology, Pará, Brazil
4 Group of Organic Chemistry, Federal University of Pará, Pará, Brazil
, Fábio dos Santos Gil2
, Sady Salomão da S. Alves3
,
,
, José Ribamar B. Lobato2
,
,
\*
*DOI: http://dx.doi.org/10.5772/intechopen.89113*
*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*
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ffe82432-4883-4adc-b03b-937c1baf5090.62 | **Author details**
*Cheminformatics and Its Applications*
to obtain eight new derivatives.
protozoan [94].
**Figure 10.**
**Acknowledgements**
required for antitrypanosomal activity, along with chemical intuition, allowed the introduction of substituents in one of the most active nitrofurans in the training set
*MEP maps (kcal/mol) for most promising nitrofurans in the prediction set against T. cruzi.*
PR models (PCA, HCA, KNN, SDA, and SIMCA) were constructed and demonstrated that 23 nitrofurans can be classified into two classes or groups: more active and less active according to their degrees of activity against *T. cruzi*. The properties QN1, gap energy, Mor05u, and MlogP are responsible for the classification into more active and less active studied nitrofurans. It is interesting to notice that these properties represent three distinct classes of interactions between the nitrofurans and the biological receptor: electronic (QN1 and gap energy), steric (Mor05u), and hydrophobic (MlogP). Here it is important to mention that Paulino et al.*,* studying the influence of molecular parameters on the activity of 5-nitrofurans against *T. cruzi,* reported the importance of electronic properties and molecular hydrophobicity as well as the variation of the nitrofurans electronic structure to explain the greater activity of these compounds as inhibitors of the growth of this
The results of the application of PR models on the validation set evidenced two nitrofurans (**25** and **30**) as more promising for synthesis and biological assays,
We gratefully acknowledge the financial support of the Brazilian agencies: Conselho Nacional de Desenvolvimento Científico e Tecnológico and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. The authors would like to thank the Virtual Computational Chemistry Laboratory (VCCLAB–Munich) and the Swiss Center for Scientific Computing for the use of the DRAGON and MOLEKEL software, respectively. We employed computing facilities at the Laboratório de Química Teórica e Computacional (LQTC)–Universidade Federal do Pará.
which in the future can be used to validate our PR models.
**66**
Marcos Antônio B. dos Santos1 , Luã Felipe S. de Oliveira2 , Antônio Florêncio de Figueiredo3 , Fábio dos Santos Gil2 , Márcio de Souza Farias2 , Heriberto Rodrigues Bitencourt4 , José Ribamar B. Lobato2 , Raimundo Dirceu de P. Farreira<sup>2</sup> , Sady Salomão da S. Alves3 , Edilson Luiz C. de Aquino2 and José Ciríaco-Pinheiro2 \*
1 University of the State of Pará, Pará, Brazil
2 Computational and Theoretical Chemistry Laboratory, Federal University of Pará, Pará, Brazil
3 Federal Institute of Education, Science and Technology, Pará, Brazil
4 Group of Organic Chemistry, Federal University of Pará, Pará, Brazil
\*Address all correspondence to: [email protected]
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
| doab | 2025-04-07T04:13:04.426466 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.64 | **Chapter 5**
*Cheminformatics and Its Applications*
[89] Todeschini R, Consonni V. In: Mannhold R, Kubinyi H, Timmerman H, editors. Molecular Descriptors for Chemoinformatics. Vol I & II. Weinheim: Wiley-VCH; 2009.
ISBN: 978-3-527-31852-0
[90] Karelson M, Victor S,
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bmc.2007.02.005
130 ISSN: 1347-5223
Lobanov A, Katritzky R. Quantumchemical descriptors in QSAR/ QSPR studies. Chemical Reviews. 1996;**96**:1027-1043. DOI: 10.1021/
[91] Gosav S, Praisler M, Dorohoi DO. ANN expert system screening for illicit amphetamines using molecular descriptors. Journal of Molecular Structure. 2007;**834**:188-194. DOI: 10.1016/j.molstruc.2006.12.059
[92] Scotti M, Fernandes MA, Ferreira MJP, Esmereciano VP. Quantitative structure– activity relationship of sesquiterpene lactones with cytotoxic activity. Bioorganic & Medicinal Chemistry. 2007;**15**:2927-2934. DOI: 10.1016/j.
[93] Moriguchi I, Hirano S, Liu Q, Nakagome I, Matsushita Y. Simple method of calculating ocatanol/water partition coefficient. Chemical and Pharmaceutical Bulletin. 1992;**40**:127-
[94] Paulino-Blumenfeld M, Hansz M, Hikici N. Electronic properties and free radical production by nitrofuran compounds. Free Radical Research Communications. 1992;**16**:207-215. DOI:
10.3109/10715769209049174
**74**
| doab | 2025-04-07T04:13:04.426534 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.65 | Chemical Reactivity Properties and Bioactivity Scores of the Angiotensin II Vasoconstrictor Octapeptide
*Norma Flores-Holguín, Juan Frau and Daniel Glossman-Mitnik*
| doab | 2025-04-07T04:13:04.426637 | 20-4-2021 18:19 | {
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ffe82432-4883-4adc-b03b-937c1baf5090.66 | **Abstract**
Eight density functionals, CAM-B3LYP, LC-*ω*PBE, M11, MN12SX, N12SX, *ω*B97, *ω*B97X, and *ω*B97XD, in connection with the Def2TZVP basis set were assessed together with the SMD solvation model for the calculation of the molecular and chemical reactivity properties of the angiotensin II vasoconstrictor octapeptide in the presence of water. All the chemical reactivity descriptors for the systems were calculated via conceptual density functional theory (CDFT). The potential bioavailability and druggability as well as the bioactivity scores for angiotensin II were predicted through different methodologies already reported in the literature which have been previously validated during the study of different peptidic systems.
**Keywords:** angiotensin II, conceptual DFT, chemical reactivity, drug-likeness features, bioactivity scores
### **1. Introduction**
In order to consider peptides and related compounds as the starting point for the development of medical drugs, it is mandatory to acquire a knowledge about their chemical reactivity properties as well as the bioactivity associated with them. From the basics of medicinal chemistry, it is known that drugs exert their effect by interacting with the active site of a receptor which is generally a protein [1]. These interactions rely on the different kinds of bindings between the pharmacophore and the chemical groups present in the active site and thus intimately related to their chemical reactivity from a molecular perspective [2, 3]. One of the most powerful tools to understand the chemical reactivity of interacting molecular systems within computational chemistry is probably the conceptual density functional theory (CDFT) [4, 5], also called chemical reactivity theory, which allows to accomplish this task by resorting to several global and local descriptors which are in turn related to variations in the electronic densities of the studied systems.
On the basis of the previous considerations, the objective of this work is to study the chemical reactivity of an octapeptide known as angiotensin II that acts
constricting the blood vessels and retaining the fluid in the kidneys [1], using the techniques of the conceptual DFT, determining their global reactivity properties, that is, of the molecule as a whole. Moreover, during the process of the development of new drugs, there is a need to learn about the drug-like properties of the involved molecular systems [6]. Thus, the descriptors of bioavailability and bioactivity (bioactivity scores) will be calculated through different procedures described in the literature [7, 8] trying to relate them with the calculated conceptual DFT descriptors.
### **2. Computational methodology**
In the same way as we have proceeded in our recent studies [9–16], the computational tasks in this work have been done by considering the popular Gaussian 09 software [17]. Following the conclusions obtained from those studies, eight density functionals have been chosen, CAM-B3LYP, LC-*ω*PBE, M11, MN12SX [18], N12SX, *ω*B97, *ω*B97X, and *ω*B97XD, because they can be considered to be well-behaved for our purposes according to our proposed KID (for Koopmans in DFT) criteria [19–23] related to the approximate validity of the Koopmans' theorem within DFT [19–23]. For the calculation of the electronic properties, several model chemistries have been considered, based on the mentioned density functionals in connection with the Def2TZVP basis set, while a smaller Def2SVP was considered for the prediction of the most stable structures [24, 25]. In order to obtain accurate results, all calculations were performed using water, which is the universal biological solvent, simulated with the SMD model [26].
### **3. Results and discussion**
The molecular structures of the conformers of the angiotensin II vasoconstrictor octapeptide graphically presented in **Figure 1** were optimized in the gas phase by means of the DFTBA model available in the software and then reoptimized with the eight density functionals described previously, the Def2SVP basis set, and water as the solvent. The calculation of the electronic properties was performed by using the same model chemistries but changing the basis set with the Def2TZVP one.
*JA* <sup>¼</sup> <sup>∣</sup>*ε<sup>L</sup>* <sup>þ</sup> *Egs*ð Þ� *<sup>N</sup> Egs*ð Þ *<sup>N</sup>* <sup>þ</sup> <sup>1</sup> <sup>∣</sup>, and *JHL* <sup>¼</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
**3.1 Calculation of the global reactivity descriptors**
Electronegativity *<sup>χ</sup>* ¼ � <sup>1</sup>
Electrophilicity *<sup>ω</sup>* <sup>¼</sup> *<sup>μ</sup>*<sup>2</sup>
Electrodonating power *<sup>ω</sup>*� <sup>¼</sup> ð Þ <sup>3</sup>*I*þ*<sup>A</sup>* <sup>2</sup>
Electroaccepting power *<sup>ω</sup>*<sup>þ</sup> <sup>¼</sup> ð Þ *<sup>I</sup>*þ3*<sup>A</sup>* <sup>2</sup>
presented in **Table 1**.
*basis set, and water as the solvent.*
**Table 1.**
can be expressed as
**77**
*JI* <sup>2</sup> <sup>þ</sup> *JA*
**Eo E+ E**� **HOMO LUMO**
**SOMO J***<sup>I</sup>* **J***<sup>A</sup>* **J***HL* Δ**SL**
CAM-B3LYP �1887.465 �1887.246 �1887.489 �7.462 0.828 LC-wBPE �1887.192 �1886.966 �1887.223 �8.786 1.767 M11 �1887.317 �1887.090 �1887.345 �8.601 1.582 MN12SX �1886.668 �1886.440 �1886.699 �6.164 �0.832 N12SX �1887.505 �1887.288 �1887.531 �5.881 �0.679 *ω*B97 �1888.093 �1887.871 �1888.118 �8.658 1.890 *ω*B97X �1887.933 �1887.711 �1887.959 �8.474 1.724 *ω*B97XD �1887.814 �1887.592 �1887.840 �8.087 1.374
*Chemical Reactivity Properties and Bioactivity Scores of the Angiotensin II Vasoconstrictor…*
*DOI: http://dx.doi.org/10.5772/intechopen.86736*
CAM-B3LYP �2.205 1.497 1.498 2.117 3.033 LC-wBPE �3.509 2.635 2.619 3.715 5.276 M11 �3.124 2.412 2.333 3.356 4.706 MN12SX �0.869 0.021 0.017 0.028 0.038 N12SX �0.785 0.000 0.053 0.053 0.106 *ω*B97 �3.303 2.619 2.575 3.673 5.192 *ω*B97X �3.144 2.432 2.410 3.424 4.868 *ω*B97XD �2.809 2.059 2.073 2.922 4.183
(the difference between the SOMO and the LUMO), was also designed to guide in verifying the accuracy of the approximation [9–15]. The results of this analysis are
*Total electronic energies of angiotensin II (in au) for the neutral and charged species, the corresponding orbital energies (in eV), and the KID-related descriptors obtained with the five density functionals, the Def2TZVP*
The overall conclusion that can be extracted from the inspection of the results presented in **Table 1** is that, in agreement with our previous studies on melanoidins and peptides, the model chemistries involving the MN12SX and N12SX density functionals are the best for verifying our proposed criteria of well-behavior.
By taking into account the KID procedure presented in our previous works together with the finite difference approximation, the global reactivity descriptors
Global hardness *η* ¼ ð Þ *I* � *A* ≈ð Þ *ε<sup>L</sup>* � *ε<sup>H</sup>* [4, 5]
Net electrophilicity Δ*ω*� ¼ *ω*<sup>þ</sup> � �*ω*� ð Þ¼ *ω*<sup>þ</sup> þ *ω*� [29]
<sup>2</sup> ð Þ *<sup>I</sup>* <sup>þ</sup> *<sup>A</sup>* <sup>≈</sup> <sup>1</sup>
<sup>2</sup>*<sup>η</sup>* <sup>¼</sup> ð Þ *<sup>I</sup>*þ*<sup>A</sup>* <sup>2</sup>
<sup>4</sup>ð Þ *<sup>I</sup>*�*<sup>A</sup>* <sup>≈</sup> ð Þ *<sup>ε</sup>L*þ*ε<sup>H</sup>* <sup>2</sup> 4ð Þ *εL*�*ε<sup>H</sup>*
<sup>16</sup>ð Þ *<sup>I</sup>*�*<sup>A</sup>* <sup>≈</sup> ð Þ <sup>3</sup>*ε<sup>H</sup>* <sup>þ</sup>*ε<sup>L</sup>* <sup>2</sup> 16*η*
<sup>16</sup>ð Þ *<sup>I</sup>*�*<sup>A</sup>* <sup>≈</sup> ð Þ *<sup>ε</sup><sup>H</sup>* <sup>þ</sup>3*ε<sup>L</sup>* <sup>2</sup> 16*η*
<sup>2</sup> p . Another descriptor, ΔSL
<sup>2</sup> ð Þ *ε<sup>L</sup>* þ *ε<sup>H</sup>* [4, 5]
[27]
[28]
[28]
In order to verify the fulfillment of our proposed KID procedure, it is necessary to perform a comparison of the orbital energies with the results obtained by means of the vertical I and A through the ΔSCF criterium. To this end, the three main descriptors are linked by *ε<sup>H</sup>* with �*I*, *ε<sup>L</sup>* with �*A*, and their behavior in describing the HOMO-LUMO gap as *JI* ¼ ∣*ε<sup>H</sup>* þ *Egs*ð Þ� *N* � 1 *Egs*ð Þ *N* ∣,
**Figure 1.** *Graphical sketch of the angiotensin II molecule.*
*Chemical Reactivity Properties and Bioactivity Scores of the Angiotensin II Vasoconstrictor… DOI: http://dx.doi.org/10.5772/intechopen.86736*
#### **Table 1.**
constricting the blood vessels and retaining the fluid in the kidneys [1], using the techniques of the conceptual DFT, determining their global reactivity properties, that is, of the molecule as a whole. Moreover, during the process of the development of new drugs, there is a need to learn about the drug-like properties of the involved molecular systems [6]. Thus, the descriptors of bioavailability and bioactivity (bioactivity scores) will be calculated through different procedures described in the literature [7, 8] trying to relate them with the calculated conceptual DFT
In the same way as we have proceeded in our recent studies [9–16], the computational tasks in this work have been done by considering the popular Gaussian 09 software [17]. Following the conclusions obtained from those studies, eight density functionals have been chosen, CAM-B3LYP, LC-*ω*PBE, M11, MN12SX [18], N12SX, *ω*B97, *ω*B97X, and *ω*B97XD, because they can be considered to be well-behaved for our purposes according to our proposed KID (for Koopmans in DFT) criteria [19–23] related to the approximate validity of the Koopmans' theorem within DFT [19–23]. For the calculation of the electronic properties, several model chemistries have been considered, based on the mentioned density functionals in connection with the Def2TZVP basis set, while a smaller Def2SVP was considered for the prediction of the most stable structures [24, 25]. In order to obtain accurate results, all calculations were performed using water, which is the universal biological sol-
The molecular structures of the conformers of the angiotensin II vasoconstrictor octapeptide graphically presented in **Figure 1** were optimized in the gas phase by means of the DFTBA model available in the software and then reoptimized with the eight density functionals described previously, the Def2SVP basis set, and water as the solvent. The calculation of the electronic properties was performed by using the
In order to verify the fulfillment of our proposed KID procedure, it is necessary to perform a comparison of the orbital energies with the results obtained by means of the vertical I and A through the ΔSCF criterium. To this end, the three main descriptors are linked by *ε<sup>H</sup>* with �*I*, *ε<sup>L</sup>* with �*A*, and their behavior in describing
same model chemistries but changing the basis set with the Def2TZVP one.
the HOMO-LUMO gap as *JI* ¼ ∣*ε<sup>H</sup>* þ *Egs*ð Þ� *N* � 1 *Egs*ð Þ *N* ∣,
descriptors.
**2. Computational methodology**
*Cheminformatics and Its Applications*
vent, simulated with the SMD model [26].
**3. Results and discussion**
**Figure 1.**
**76**
*Graphical sketch of the angiotensin II molecule.*
*Total electronic energies of angiotensin II (in au) for the neutral and charged species, the corresponding orbital energies (in eV), and the KID-related descriptors obtained with the five density functionals, the Def2TZVP basis set, and water as the solvent.*
*JA* <sup>¼</sup> <sup>∣</sup>*ε<sup>L</sup>* <sup>þ</sup> *Egs*ð Þ� *<sup>N</sup> Egs*ð Þ *<sup>N</sup>* <sup>þ</sup> <sup>1</sup> <sup>∣</sup>, and *JHL* <sup>¼</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi *JI* <sup>2</sup> <sup>þ</sup> *JA* <sup>2</sup> p . Another descriptor, ΔSL (the difference between the SOMO and the LUMO), was also designed to guide in verifying the accuracy of the approximation [9–15]. The results of this analysis are presented in **Table 1**.
The overall conclusion that can be extracted from the inspection of the results presented in **Table 1** is that, in agreement with our previous studies on melanoidins and peptides, the model chemistries involving the MN12SX and N12SX density functionals are the best for verifying our proposed criteria of well-behavior.
### **3.1 Calculation of the global reactivity descriptors**
By taking into account the KID procedure presented in our previous works together with the finite difference approximation, the global reactivity descriptors can be expressed as
where I is the ionization potential and A the electronic affinity, while *ε<sup>H</sup>* and *ε<sup>L</sup>* are the energies of the HOMO and LUMO, respectively.
The results for the global reactivity descriptors for the angiotensin II octapeptide based on the values of the HOMO and LUMO energies calculated with the MN12SX and N12SX density functionals are presented in **Table 2**.
As expected from the molecular structure of this peptide, its electrodonating ability is more important that its electroaccepting character. It can be seen that MN12SX and N12SX density functionals (which verify the KID criteria) give results different than those obtained from the calculation with the other three density functionals.
#### **3.2 Bioactivity scores**
The molecular properties that are related to the concept of drug-likeness and in particular those associated with the criteria proposed by Lipinski et al. [30, 31] for the prediction of oral bioavailability have been calculated by feeding the corresponding SMILES notations into the Molinspiration software readily available online (Slovensky Grob, Slovak Republic: https://www.mol inspiration.com). The results are presented in **Table 3**.
compared to those that are being studied and with known pharmacological properties. The same software was used for the calculation of the bioactivity scores which are a measure of the ability of the potential drug to interact with the different receptors, that is, to act as GPCR ligands or kinase inhibitors, to perform as ion channel modulators, or to interact with enzymes and nuclear receptors. The values
*Bioactivity scores of the angiotensin II molecule calculated on the basis of GPCR ligand, ion channel modulator,*
*nuclear receptor ligand, kinase inhibitor, protease inhibitor, and enzyme inhibitor interactions.*
These bioactivity scores for organic molecules can be interpreted as active (when the bioactivity score > 0), moderately active (when the bioactivity score lies between �5.0 and 0.0), and inactive (when the bioactivity score < �5.0). The angiotensin II peptide was found to be moderately bioactive toward the protease
In this chapter we have presented a new study performed on the chemical reactivity of the angiotensin II vasoconstrictor octapeptide based on the conceptual
The knowledge of the values of the global descriptors of the molecular reactivity of angiotensin II could be useful in the development of new drugs based on this
Finally, the molecular properties related to bioavailability and drug-likeness have been predicted using a proven methodology already described in the literature, and the descriptors used for the quantification of the bioactivity allowed to characterize the studied molecule as being moderately bioactive toward the protease
Norma Flores-Holguín and Daniel Glossman-Mitnik are researchers of CIMAV and CONACYT from which partial support is gratefully acknowledged. Daniel Glossman-Mitnik conducted this work while being a visiting lecturer at the University of the Balearic Islands. This work was also cofunded by the Ministerio de Economía y Competitividad (MINECO) and the European Fund for Regional
The authors declare no conflict of interest regarding the publication of this chapter.
of the bioactivity scores for angiotensin II are presented in **Table 4**.
**Molecule Angiotensin II** GPCR ligand �3.59 Ion channel modulator �3.74 Kinase inhibitor �3.78 Nuclear receptor ligand �3.85 Protease inhibitor �3.25 Enzyme inhibitor �3.67
*DOI: http://dx.doi.org/10.5772/intechopen.86736*
*Chemical Reactivity Properties and Bioactivity Scores of the Angiotensin II Vasoconstrictor…*
inhibitor and the GPCR ligand considered in the study.
DFT as a tool to explain the molecular interactions.
inhibitor and the GPCR ligand considered in this study.
**4. Conclusions**
**Table 4.**
compound or some analogs.
**Acknowledgements**
Development (FEDER).
**Conflict of interest**
**79**
However, what the Lipinski's rule of five really measures is the oral bioavailability of a potential drug because this is the desired property for a molecule having drug-like character. Then, a different approach was followed by considering similarity searches in the chemical space of compounds with structures that can be
#### **Table 2.**
*Global reactivity descriptors for the angiotensin II molecule calculated with the MN12SX and N12SX density functionals with the Def2TZVP basis set and the SMD solvation model using water as the solvent.*
**Table 3.**
*Molecular properties of the angiotensin II peptide calculated to verify the Lipinski's rule of five.*
*Chemical Reactivity Properties and Bioactivity Scores of the Angiotensin II Vasoconstrictor… DOI: http://dx.doi.org/10.5772/intechopen.86736*
#### **Table 4.**
where I is the ionization potential and A the electronic affinity, while *ε<sup>H</sup>* and *ε<sup>L</sup>*
The results for the global reactivity descriptors for the angiotensin II octapeptide based on the values of the HOMO and LUMO energies calculated with the MN12SX
As expected from the molecular structure of this peptide, its electrodonating ability is more important that its electroaccepting character. It can be seen that MN12SX and N12SX density functionals (which verify the KID criteria) give results different than those obtained from the calculation with the other three density
The molecular properties that are related to the concept of drug-likeness and in particular those associated with the criteria proposed by Lipinski et al. [30, 31] for
corresponding SMILES notations into the Molinspiration software readily available online (Slovensky Grob, Slovak Republic: https://www.mol inspiration.com). The
However, what the Lipinski's rule of five really measures is the oral bioavailability of a potential drug because this is the desired property for a molecule having drug-like character. Then, a different approach was followed by considering similarity searches in the chemical space of compounds with structures that can be
**Electronegativity (***χ* **) Chemical hardness (***η***) Electrophilicity (***ω***)**
**Electroaccepting power (***ω*þ**)**
**Net electrophilicity (Δ***ω*�**)**
the prediction of oral bioavailability have been calculated by feeding the
MN12SX 3.3286 4.9685 1.1150 N12SX 3.1472 4.7664 1.0391
MN12SX 2.4725 1.1286 3.6011 N12SX 2.3225 1.0468 3.3693
**Molecule Angiotensin II** miLogP �3.91 TPSA 406.33 nAtoms 75 nON 25 nOHNH 16 nviol 3 nrotb 30 volume 955.57 MW 1046.20
*Molecular properties of the angiotensin II peptide calculated to verify the Lipinski's rule of five.*
*Global reactivity descriptors for the angiotensin II molecule calculated with the MN12SX and N12SX density functionals with the Def2TZVP basis set and the SMD solvation model using water as the solvent.*
are the energies of the HOMO and LUMO, respectively.
and N12SX density functionals are presented in **Table 2**.
functionals.
**Table 2.**
**Table 3.**
**78**
**3.2 Bioactivity scores**
results are presented in **Table 3**.
*Cheminformatics and Its Applications*
**Electrodonating power (***ω*�**)**
*Bioactivity scores of the angiotensin II molecule calculated on the basis of GPCR ligand, ion channel modulator, nuclear receptor ligand, kinase inhibitor, protease inhibitor, and enzyme inhibitor interactions.*
compared to those that are being studied and with known pharmacological properties. The same software was used for the calculation of the bioactivity scores which are a measure of the ability of the potential drug to interact with the different receptors, that is, to act as GPCR ligands or kinase inhibitors, to perform as ion channel modulators, or to interact with enzymes and nuclear receptors. The values of the bioactivity scores for angiotensin II are presented in **Table 4**.
These bioactivity scores for organic molecules can be interpreted as active (when the bioactivity score > 0), moderately active (when the bioactivity score lies between �5.0 and 0.0), and inactive (when the bioactivity score < �5.0). The angiotensin II peptide was found to be moderately bioactive toward the protease inhibitor and the GPCR ligand considered in the study.
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ffe82432-4883-4adc-b03b-937c1baf5090.67 | **4. Conclusions**
In this chapter we have presented a new study performed on the chemical reactivity of the angiotensin II vasoconstrictor octapeptide based on the conceptual DFT as a tool to explain the molecular interactions.
The knowledge of the values of the global descriptors of the molecular reactivity of angiotensin II could be useful in the development of new drugs based on this compound or some analogs.
Finally, the molecular properties related to bioavailability and drug-likeness have been predicted using a proven methodology already described in the literature, and the descriptors used for the quantification of the bioactivity allowed to characterize the studied molecule as being moderately bioactive toward the protease inhibitor and the GPCR ligand considered in this study.
### **Acknowledgements**
Norma Flores-Holguín and Daniel Glossman-Mitnik are researchers of CIMAV and CONACYT from which partial support is gratefully acknowledged. Daniel Glossman-Mitnik conducted this work while being a visiting lecturer at the University of the Balearic Islands. This work was also cofunded by the Ministerio de Economía y Competitividad (MINECO) and the European Fund for Regional Development (FEDER).
### **Conflict of interest**
The authors declare no conflict of interest regarding the publication of this chapter.
*Cheminformatics and Its Applications*
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ffe82432-4883-4adc-b03b-937c1baf5090.68 | **Author details**
Norma Flores-Holguín1†, Juan Frau2† and Daniel Glossman-Mitnik<sup>1</sup> \*†
1 Centro de Investigación en Materiales Avanzados, Departamento de Medio Ambiente y Energía, Laboratorio Virtual NANOCOSMOS, Chihuahua, Mexico **References**
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*DOI: http://dx.doi.org/10.5772/intechopen.86736*
[11] Frau J, Glossman-Mitnik D. Conceptual DFT study of the local chemical reactivity of the colored BISARG melanoidin and its protonated derivative. Frontiers in Chemistry.
[12] Frau J, Glossman-Mitnik D. Molecular reactivity of some Maillard reaction products studied through conceptual DFT. Contemporary Chemistry. 2018;**1**(1):1-14
[13] Frau J, Glossman-Mitnik D. Computational study of the chemical reactivity of the blue-M1 intermediate melanoidin. Computational and
[14] Frau J, Glossman-Mitnik D. Chemical reactivity theory applied to the calculation of the local reactivity descriptors of a colored Maillard reaction product. Chemical Science International Journal. 2018;**22**(4):1-14
[15] Frau J, Glossman-Mitnik D. Blue M2: An intermediate melanoidin studied via conceptual DFT. Journal of Molecular Modeling. 2018;**24**(138):
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2 Departament de Química, Universitat de les Illes Balears, Palma de Mallorca, Spain
\*Address all correspondence to: [email protected]
† These authors contributed equally.
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
*Chemical Reactivity Properties and Bioactivity Scores of the Angiotensin II Vasoconstrictor… DOI: http://dx.doi.org/10.5772/intechopen.86736*
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ffe82432-4883-4adc-b03b-937c1baf5090.70 | **Chapter 6**
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[21] Kar R, Song J-W, Hirao K. Longrange corrected functionals satisfy Koopmans' theorem: Calculation of correlation and relaxation energies. Journal of Computational Chemistry.
[22] Salzner U, Baer R. Koopmans' springs to life. The Journal of Chemical
[23] Vanfleteren D, Van Neck D, Ayers PW, Morrison RC, Bultinck P. Exact ionization potentials from wavefunction asymptotics: The extended Koopmans' theorem, revisited. The Journal of Chemical Physics. 2009;**130**(19):194104
[24] Weigend F, Ahlrichs R. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. Physical Chemistry Chemical Physics. 2005;**7**:
[25] Weigend F. Accurate Coulombfitting basis sets for H to R. Physical Chemistry Chemical Physics. 2006;**8**:
[26] Marenich AV, Cramer CJ, Truhlar DG. Universal solvation model based on solute electron density and a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. Journal of Physical Chemistry
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ffe82432-4883-4adc-b03b-937c1baf5090.71 | Chemoinformatic Approach: The Case of Natural Products of Panama
*Dionisio A. Olmedo and José L. Medina-Franco*
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ffe82432-4883-4adc-b03b-937c1baf5090.72 | **Abstract**
Chemoinformatic analysis was used to characterize a compound database of natural products from Panama and other reference collections. Data mining allowed to compare drug-likeness properties with public and commercial software and to achieve a statistical analysis of the physicochemical properties. Visualization of the chemical space in 3D indicates a high structural similarity. Molecular flexibility and complexity were evaluated using 2D descriptors, whereas the molecular scaffold was obtained using the Murcko method, and these showed few differences between the explored data set. In this chapter, we also present and discuss an example of the application of the chemoinformatic approach using the concept of modeling the activity landscape to study the structure-activity relationships (SARs) of compounds with activity against *Plasmodium falciparum.*
**Keywords:** chemoinformatic, complexity, data mining, physicochemical properties, scaffold
### **1. Introduction**
Natural products (NPs) and their derivatives constitute a significant fraction of approved drugs [1–3], bioactive compounds [4–8], and lead compounds for drug discovery [9]. NP fragment has been used to guide the synthesis of bioactive compounds and generate BIOS combinatorial libraries [10–15]. NPs have structures with different substituent patterns, giving rise to different biological activities for compounds with very similar structures [16–19]. These bioactive metabolites have greater affinity for biological targets and, overall, may have better bioavailability than synthetic compounds, and the presence of pan-assay interference compounds (PAIN) is less frequent in this type of product [20]. The chemoinformatic analysis of several databases of NPs developed by academic institutions and private companies [21] has been carried out in different countries. Thus, the following databases were obtained: BIOFACQUIM [22], CIFPMA [23], NuBBE [24, 25], NANPDB [26], TCM [27], HIT [28], and NPACT [29]. The application of chemoinformatic tools involves the generation, manipulation, and analysis of data set of chemical substances. This allows us through mathematical calculations to order, develop, and evaluate structural information that can be visualized in 2D and 3D [30]. The determination of the physicochemical properties carried out on different databases of NPs and principal component analysis (PCA) was used as an approximation to display the chemical spaces [22–24, 31–37].
**Figure 1.**
*Biological endpoints and targets in which natural products from Panama present bioactivity.*
Computational exploration of NPs has increased in recent years, giving greater relevance to studies that include structural diversity metrics calculated with parameters based on distances such as Euclidean distance, Manhattan distances, and Cosine distance. Other criteria are based on circular fingerprint (ECFP-4, ECFP-6) [22–24, 38–45] and fingerprint based on substructure (MACCS, PubChem) [22–24, 39–45]. Another metric used in NPs is the comparison by similarity that uses the Tanimoto index/Tanimoto coefficient [22–24, 45–49].
In this study, the molecular scaffolds of natural products have been obtained using the Murcko method [22–24, 50–57]. Meanwhile, the molecular complexity is frequently evaluated by descriptors in 2D such as fraction of sp3 hybridized carbons (Fsp3 ) [23], fraction of chiral centers (FCC) [23], and globularity [22–24, 58–63].
An update of the Natural Products Database from the University of Panama (UPMA) containing 454 compounds (Unpublished data) has been evaluated against different therapeutic targets such as cytotoxicity bioassay in cell lines, antifungal assay in vitro, parasites of tropical diseases (*Leishmania* sp., *Plasmodium falciparum*, and *Trypanosoma cruzi*), and the bioassay against HIV-1 virus, demonstrating an inhibitor effect on protease, reverse transcriptase, nuclear factor NFkappaB, and Tat protein affecting the viral replication. These are the most significant biological targets in which the natural products from Panama present bioactivity. The values of their biological activities are represented as percentages in **Figure 1**.
| doab | 2025-04-07T04:13:04.428500 | 20-4-2021 18:19 | {
"license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/3.0/",
"book_id": "ffe82432-4883-4adc-b03b-937c1baf5090",
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"author": "",
"title": "Cheminformatics and its Applications",
"publisher": "IntechOpen",
"isbn": "9781838800680",
"section_idx": 72
} |
ffe82432-4883-4adc-b03b-937c1baf5090.73 | **2. Application of chemoinformatic antimalarial databases: case of natural products from Panama**
#### **2.1 Preparation curated and processing of data set**
In this chapter, we present a chemoinformatic analysis of natural products with antimalarial activities (in vitro), expressed as pIC50 against sensitive and resistant
**85**
**Table 1.**
*Chemoinformatic Approach: The Case of Natural Products of Panama*
strains. Databases of natural products with antimalarial activity (NPAs) were constructed in-house by reviewing published articles including those compounds that were isolated and characterized by spectroscopic techniques of nuclear magnetic resonance. Around 1312 compounds were compared to 8 reference data sets: an open database, DrugBank (antimalarial drug), European Bioinformatics Institute. (CHEMBL drug indications) (antimalarial activities), Open Source Drug Discovery (OSDD) Malaria, Malaria Box (Medicines for Malaria Venture (MMV)), St. Jude Children's Research Hospital (St. Jude), Novartis (GNF Malaria Box), and GlaxoSmithKline (GSK) Tres Cantos antimalarial set. All data sets were curated using the "Wash" function implemented in the Molecular Operating Environment (MOE2018.0101) software [64]. The structure of the studied compounds was represented by simplified molecular input line entry system (SMILES) notation, thus obtaining 20,364 unique molecules that are summarized in **Table 1**. The difference between initial compounds and unique compounds is due to the fact that during the data preparation (curation process), the duplicate compounds are eliminated, those that have positive or negative partial loads have neutralized their protonation states, the metals are disconnected, and the energy is minimized using the molecular mechanistic force field (MMFF94). The result of the data curation is the reduction of the initial number of molecules present in the databases evaluated in this work.
The descriptors of physicochemical properties, hydrogen bond acceptors (HBAs), hydrogen bond donors (HBDs), number of rotatable bonds (NRBs), the octanol/water partition coefficient (logP), topological polar surface area (TPSA),
**compounds**
Novartis-GNF Malaria Box 4.878 4.868 Available in: https://www.
**Unique compounds**
1353 1312 Databases of NP in house
26 4 https://www.drugbank.ca
27 24 [https://www.ebi.ac.uk/
93 88 http://opensourcemalaria.
124 124 https://www.ebi.ac.uk/
1.478 1.478 https://www.ebi.ac.uk/
12.470 12.466 Open Source Malaria
**Source**
chembl]
org/
chemblntd
chembl/malaria/source
ncbi.nlm.nih.gov/pmc/ articles/PMC3941073/ Available in: https://www.ebi.
(GSK-TCMDC). Available in: https://www.ebi.ac.uk/
ac.uk/chemblntd
chemblntd
*DOI: http://dx.doi.org/10.5772/intechopen.87779*
**2.2 Molecular descriptors**
Natural Products Antimalarial
DrugBank Version 5.0. (Drug
Open Source Drug Discovery
St. Jude Children's Research
GlaxoSmithKline Tres Cantos
*Databases analyzed with chemoinformatic tools.*
Malaria Box-Medicine of Malaria
European Bioinformatics Institute. (CHEMBL Drugs Indications) (Antimalarial activities
(NPAs)
Antimalarial)
(OSDD) Malaria
Venture (MMV)
Hospital's
Antimalarial
**Databases Initial**
*Chemoinformatic Approach: The Case of Natural Products of Panama DOI: http://dx.doi.org/10.5772/intechopen.87779*
strains. Databases of natural products with antimalarial activity (NPAs) were constructed in-house by reviewing published articles including those compounds that were isolated and characterized by spectroscopic techniques of nuclear magnetic resonance. Around 1312 compounds were compared to 8 reference data sets: an open database, DrugBank (antimalarial drug), European Bioinformatics Institute. (CHEMBL drug indications) (antimalarial activities), Open Source Drug Discovery (OSDD) Malaria, Malaria Box (Medicines for Malaria Venture (MMV)), St. Jude Children's Research Hospital (St. Jude), Novartis (GNF Malaria Box), and GlaxoSmithKline (GSK) Tres Cantos antimalarial set. All data sets were curated using the "Wash" function implemented in the Molecular Operating Environment (MOE2018.0101) software [64]. The structure of the studied compounds was represented by simplified molecular input line entry system (SMILES) notation, thus obtaining 20,364 unique molecules that are summarized in **Table 1**. The difference between initial compounds and unique compounds is due to the fact that during the data preparation (curation process), the duplicate compounds are eliminated, those that have positive or negative partial loads have neutralized their protonation states, the metals are disconnected, and the energy is minimized using the molecular mechanistic force field (MMFF94). The result of the data curation is the reduction of the initial number of molecules present in the databases evaluated in this work.
| doab | 2025-04-07T04:13:04.428782 | 20-4-2021 18:19 | {
"license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/3.0/",
"book_id": "ffe82432-4883-4adc-b03b-937c1baf5090",
"url": "https://mts.intechopen.com/storage/books/9236/authors_book/authors_book.pdf",
"author": "",
"title": "Cheminformatics and its Applications",
"publisher": "IntechOpen",
"isbn": "9781838800680",
"section_idx": 73
} |
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