import argparse import copy import json import pickle parser = argparse.ArgumentParser() parser.add_argument('--sim_ckpt', type=str, default=None, required=True) parser.add_argument('--data_dir', type=str, default='share/4AA_data') parser.add_argument('--mddir', type=str, default='/data/cb/scratch/share/mdgen/4AA_sims') parser.add_argument('--suffix', type=str, default='') parser.add_argument('--pdb_id', nargs='*', default=[]) parser.add_argument('--num_frames', type=int, default=100) parser.add_argument('--num_batches', type=int, default=100) parser.add_argument('--batch_size', type=int, default=10) parser.add_argument('--out_dir', type=str, default=".") parser.add_argument('--random_start_idx', action='store_true') parser.add_argument('--split', type=str, default='splits/4AA_test.csv') parser.add_argument('--chunk_idx', type=int, default=0) parser.add_argument('--n_chunks', type=int, default=1) args = parser.parse_args() import mdgen.analysis import os, torch, mdtraj, tqdm import numpy as np from mdgen.geometry import atom14_to_frames, atom14_to_atom37, atom37_to_torsions from mdgen.tensor_utils import tensor_tree_map from mdgen.residue_constants import restype_order, restype_atom37_mask from mdgen.wrapper import NewMDGenWrapper from mdgen.dataset import atom14_to_frames import pandas as pd import contextlib import numpy as np @contextlib.contextmanager def temp_seed(seed): state = np.random.get_state() np.random.seed(seed) try: yield finally: np.random.set_state(state) os.makedirs(args.out_dir, exist_ok=True) def get_sample(arr, seqres, start_idxs, start_state, end_state, num_frames=100): start_idx = np.random.choice(start_idxs, 1).item() if args.random_start_idx: start_idx = np.random.randint(low=0,high=len(arr)-num_frames) end_idx = start_idx + num_frames arr = np.copy(arr[start_idx: end_idx]).astype(np.float32) seqres = torch.tensor([restype_order[c] for c in seqres]) frames = atom14_to_frames(torch.from_numpy(arr)) atom37 = torch.from_numpy(atom14_to_atom37(arr, seqres)).float() torsions, torsion_mask = atom37_to_torsions(atom37, seqres[None]) L = frames.shape[1] mask = torch.ones(L) return { 'torsions': torsions, 'torsion_mask': torsion_mask[0], 'trans': frames._trans, 'rots': frames._rots._rot_mats, 'seqres': seqres, 'start_idx': start_idx, 'end_idx': end_idx, 'start_state': start_state, 'end_state': end_state, 'mask': mask, # (L,) } def do(model, name, seqres): print('doing', name) if os.path.exists(f'{args.out_dir}/{name}_metadata.pkl'): pkl_metadata = pickle.load(open(f'{args.out_dir}/{name}_metadata.pkl', 'rb')) msm = pkl_metadata['msm'] cmsm = pkl_metadata['cmsm'] ref_kmeans = pkl_metadata['ref_kmeans'] else: with temp_seed(137): feats, ref = mdgen.analysis.get_featurized_traj(f'{args.mddir}/{name}/{name}', sidechains=True) tica, _ = mdgen.analysis.get_tica(ref) kmeans, ref_kmeans = mdgen.analysis.get_kmeans(tica.transform(ref)) try: msm, pcca, cmsm = mdgen.analysis.get_msm(ref_kmeans, nstates=10) except Exception as e: print('ERROR', e, name, flush=True) return pickle.dump({ 'msm': msm, 'cmsm': cmsm, 'tica': tica, 'pcca': pcca, 'kmeans': kmeans, 'ref_kmeans': ref_kmeans, }, open(f'{args.out_dir}/{name}_metadata.pkl', 'wb')) flux_mat = cmsm.transition_matrix * cmsm.pi[None, :] np.fill_diagonal(flux_mat, 0) start_state, end_state = np.unravel_index(np.argmax(flux_mat, axis=None), flux_mat.shape) ref_discrete = msm.metastable_assignments[ref_kmeans] arr = np.lib.format.open_memmap(f'{args.data_dir}/{name}.npy', 'r') if model.args.frame_interval: arr = arr[::model.args.frame_interval] ref_discrete = ref_discrete[::model.args.frame_interval] is_start = ref_discrete == start_state is_end = ref_discrete == end_state trans_indices = is_start[:-args.num_frames] * is_end[args.num_frames:] start_idxs = np.where(trans_indices)[0] if (trans_indices).sum() == 0: print('No transition path found for ', name, 'skipping...') return metadata = [] for i in tqdm.tqdm(range(args.num_batches), desc='num batch'): batch_list = [] for _ in range(args.batch_size): batch_list.append( get_sample(arr, seqres, copy.deepcopy(start_idxs), start_state, end_state, num_frames=args.num_frames)) batch = next(iter(torch.utils.data.DataLoader(batch_list, batch_size=args.batch_size))) batch = tensor_tree_map(lambda x: x.cuda(), batch) print('Start tps for ', name, 'with start coords', batch['trans'][0, 0, 0], 'and with end coords', batch['trans'][0, -1, 0]) atom14s, aa_out = model.inference(batch) for j in range(args.batch_size): idx = i * args.batch_size + j path = os.path.join(args.out_dir, f'{name}_{idx}.pdb') atom14_to_pdb(atom14s[j].cpu().numpy(), batch['seqres'][0].cpu().numpy(), path) traj = mdtraj.load(path) traj.superpose(traj) traj.save(os.path.join(args.out_dir, f'{name}_{idx}.xtc')) traj[0].save(os.path.join(args.out_dir, f'{name}_{idx}.pdb')) metadata.append({ 'name': name, 'start_idx': batch['start_idx'][j].cpu().item(), 'end_idx': batch['end_idx'][j].cpu().item(), 'start_state': batch['start_state'][j].cpu().item(), 'end_state': batch['end_state'][j].cpu().item(), 'aa_out': aa_out[j].cpu().numpy().tolist(), # 'aa_out': 'aa_out', 'path': path, }) json.dump(metadata, open(f'{args.out_dir}/{name}_metadata.json', 'w')) @torch.no_grad() def main(): model = NewMDGenWrapper.load_from_checkpoint(args.sim_ckpt) model.eval().to('cuda') df = pd.read_csv(args.split, index_col='name') names = np.array(df.index) chunks = np.array_split(names, args.n_chunks) chunk = chunks[args.chunk_idx] print('#' * 20) print(f'RUN NUMBER: {args.chunk_idx}, PROCESSING IDXS {args.chunk_idx * len(chunk)}-{(args.chunk_idx + 1) * len(chunk)}') print('#' * 20) for name in tqdm.tqdm(chunk, desc='num peptides'): if args.pdb_id and name not in args.pdb_id: continue do(model, name, df.seqres[name]) main()