Spaces:
Runtime error
Runtime error
File size: 9,946 Bytes
753e275 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 |
import os
import argparse
import copy
import json
from tqdm.auto import tqdm
from torch.utils.data import DataLoader
from diffab.datasets import get_dataset
from diffab.models import get_model
from diffab.modules.common.geometry import reconstruct_backbone_partially
from diffab.modules.common.so3 import so3vec_to_rotation
from diffab.utils.inference import RemoveNative
from diffab.utils.protein.writers import save_pdb
from diffab.utils.train import recursive_to
from diffab.utils.misc import *
from diffab.utils.data import *
from diffab.utils.transforms import *
from diffab.utils.inference import *
def create_data_variants(config, structure_factory):
structure = structure_factory()
structure_id = structure['id']
data_variants = []
if config.mode == 'single_cdr':
cdrs = sorted(list(set(find_cdrs(structure)).intersection(config.sampling.cdrs)))
for cdr_name in cdrs:
transform = Compose([
MaskSingleCDR(cdr_name, augmentation=False),
MergeChains(),
])
data_var = transform(structure_factory())
residue_first, residue_last = get_residue_first_last(data_var)
data_variants.append({
'data': data_var,
'name': f'{structure_id}-{cdr_name}',
'tag': f'{cdr_name}',
'cdr': cdr_name,
'residue_first': residue_first,
'residue_last': residue_last,
})
elif config.mode == 'multiple_cdrs':
cdrs = sorted(list(set(find_cdrs(structure)).intersection(config.sampling.cdrs)))
transform = Compose([
MaskMultipleCDRs(selection=cdrs, augmentation=False),
MergeChains(),
])
data_var = transform(structure_factory())
data_variants.append({
'data': data_var,
'name': f'{structure_id}-MultipleCDRs',
'tag': 'MultipleCDRs',
'cdrs': cdrs,
'residue_first': None,
'residue_last': None,
})
elif config.mode == 'full':
transform = Compose([
MaskAntibody(),
MergeChains(),
])
data_var = transform(structure_factory())
data_variants.append({
'data': data_var,
'name': f'{structure_id}-Full',
'tag': 'Full',
'residue_first': None,
'residue_last': None,
})
elif config.mode == 'abopt':
cdrs = sorted(list(set(find_cdrs(structure)).intersection(config.sampling.cdrs)))
for cdr_name in cdrs:
transform = Compose([
MaskSingleCDR(cdr_name, augmentation=False),
MergeChains(),
])
data_var = transform(structure_factory())
residue_first, residue_last = get_residue_first_last(data_var)
for opt_step in config.sampling.optimize_steps:
data_variants.append({
'data': data_var,
'name': f'{structure_id}-{cdr_name}-O{opt_step}',
'tag': f'{cdr_name}-O{opt_step}',
'cdr': cdr_name,
'opt_step': opt_step,
'residue_first': residue_first,
'residue_last': residue_last,
})
else:
raise ValueError(f'Unknown mode: {config.mode}.')
return data_variants
def main():
parser = argparse.ArgumentParser()
parser.add_argument('index', type=int)
parser.add_argument('-c', '--config', type=str, default='./configs/test/codesign_single.yml')
parser.add_argument('-o', '--out_root', type=str, default='./results')
parser.add_argument('-t', '--tag', type=str, default='')
parser.add_argument('-s', '--seed', type=int, default=None)
parser.add_argument('-d', '--device', type=str, default='cuda')
parser.add_argument('-b', '--batch_size', type=int, default=16)
args = parser.parse_args()
# Load configs
config, config_name = load_config(args.config)
seed_all(args.seed if args.seed is not None else config.sampling.seed)
# Testset
dataset = get_dataset(config.dataset.test)
get_structure = lambda: dataset[args.index]
# Logging
structure_ = get_structure()
structure_id = structure_['id']
tag_postfix = '_%s' % args.tag if args.tag else ''
log_dir = get_new_log_dir(os.path.join(args.out_root, config_name + tag_postfix), prefix='%04d_%s' % (args.index, structure_['id']))
logger = get_logger('sample', log_dir)
logger.info('Data ID: %s' % structure_['id'])
data_native = MergeChains()(structure_)
save_pdb(data_native, os.path.join(log_dir, 'reference.pdb'))
# Load checkpoint and model
logger.info('Loading model config and checkpoints: %s' % (config.model.checkpoint))
ckpt = torch.load(config.model.checkpoint, map_location='cpu')
cfg_ckpt = ckpt['config']
model = get_model(cfg_ckpt.model).to(args.device)
lsd = model.load_state_dict(ckpt['model'])
logger.info(str(lsd))
# Make data variants
data_variants = create_data_variants(
config = config,
structure_factory = get_structure,
)
# Save metadata
metadata = {
'identifier': structure_id,
'index': args.index,
'config': args.config,
'items': [{kk: vv for kk, vv in var.items() if kk != 'data'} for var in data_variants],
}
with open(os.path.join(log_dir, 'metadata.json'), 'w') as f:
json.dump(metadata, f, indent=2)
# Start sampling
collate_fn = PaddingCollate(eight=False)
inference_tfm = [ PatchAroundAnchor(), ]
if 'abopt' not in config.mode: # Don't remove native CDR in optimization mode
inference_tfm.append(RemoveNative(
remove_structure = config.sampling.sample_structure,
remove_sequence = config.sampling.sample_sequence,
))
inference_tfm = Compose(inference_tfm)
for variant in data_variants:
os.makedirs(os.path.join(log_dir, variant['tag']), exist_ok=True)
logger.info(f"Start sampling for: {variant['tag']}")
save_pdb(data_native, os.path.join(log_dir, variant['tag'], 'REF1.pdb')) # w/ OpenMM minimization
data_cropped = inference_tfm(
copy.deepcopy(variant['data'])
)
data_list_repeat = [ data_cropped ] * config.sampling.num_samples
loader = DataLoader(data_list_repeat, batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn)
count = 0
for batch in tqdm(loader, desc=variant['name'], dynamic_ncols=True):
torch.set_grad_enabled(False)
model.eval()
batch = recursive_to(batch, args.device)
if 'abopt' in config.mode:
# Antibody optimization starting from native
traj_batch = model.optimize(batch, opt_step=variant['opt_step'], optimize_opt={
'pbar': True,
'sample_structure': config.sampling.sample_structure,
'sample_sequence': config.sampling.sample_sequence,
})
else:
# De novo design
traj_batch = model.sample(batch, sample_opt={
'pbar': True,
'sample_structure': config.sampling.sample_structure,
'sample_sequence': config.sampling.sample_sequence,
})
aa_new = traj_batch[0][2] # 0: Last sampling step. 2: Amino acid.
pos_atom_new, mask_atom_new = reconstruct_backbone_partially(
pos_ctx = batch['pos_heavyatom'],
R_new = so3vec_to_rotation(traj_batch[0][0]),
t_new = traj_batch[0][1],
aa = aa_new,
chain_nb = batch['chain_nb'],
res_nb = batch['res_nb'],
mask_atoms = batch['mask_heavyatom'],
mask_recons = batch['generate_flag'],
)
aa_new = aa_new.cpu()
pos_atom_new = pos_atom_new.cpu()
mask_atom_new = mask_atom_new.cpu()
for i in range(aa_new.size(0)):
data_tmpl = variant['data']
aa = apply_patch_to_tensor(data_tmpl['aa'], aa_new[i], data_cropped['patch_idx'])
mask_ha = apply_patch_to_tensor(data_tmpl['mask_heavyatom'], mask_atom_new[i], data_cropped['patch_idx'])
pos_ha = (
apply_patch_to_tensor(
data_tmpl['pos_heavyatom'],
pos_atom_new[i] + batch['origin'][i].view(1, 1, 3).cpu(),
data_cropped['patch_idx']
)
)
save_path = os.path.join(log_dir, variant['tag'], '%04d.pdb' % (count, ))
save_pdb({
'chain_nb': data_tmpl['chain_nb'],
'chain_id': data_tmpl['chain_id'],
'resseq': data_tmpl['resseq'],
'icode': data_tmpl['icode'],
# Generated
'aa': aa,
'mask_heavyatom': mask_ha,
'pos_heavyatom': pos_ha,
}, path=save_path)
# save_pdb({
# 'chain_nb': data_cropped['chain_nb'],
# 'chain_id': data_cropped['chain_id'],
# 'resseq': data_cropped['resseq'],
# 'icode': data_cropped['icode'],
# # Generated
# 'aa': aa_new[i],
# 'mask_heavyatom': mask_atom_new[i],
# 'pos_heavyatom': pos_atom_new[i] + batch['origin'][i].view(1, 1, 3).cpu(),
# }, path=os.path.join(log_dir, variant['tag'], '%04d_patch.pdb' % (count, )))
count += 1
logger.info('Finished.\n')
if __name__ == '__main__':
main()
|