File size: 4,832 Bytes
48b5986 | 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 | from pathlib import Path
import sys
_PROJECT_FILE = Path(__file__).resolve()
for _PROJECT_ROOT in _PROJECT_FILE.parents:
if (_PROJECT_ROOT / "model").is_dir():
if str(_PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(_PROJECT_ROOT))
break
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Sample sequences based on a given structure (multinomial sampling, no beam search).
#
# usage: sample_sequences.py [-h] [--chain CHAIN] [--temperature TEMPERATURE]
# [--outpath OUTPATH] [--num-samples NUM_SAMPLES] pdbfile
import argparse
import numpy as np
from pathlib import Path
import torch
import model.esm as esm
import model.esm.inverse_folding
def sample_seq_singlechain(model, alphabet, args):
if torch.cuda.is_available() and not args.nogpu:
model = model.cuda()
print("Transferred model to GPU")
device = next(model.parameters()).device
coords, native_seq = esm.inverse_folding.util.load_coords(args.pdbfile, args.chain)
print('Native sequence loaded from structure file:')
print(native_seq)
print(f'Saving sampled sequences to {args.outpath}.')
Path(args.outpath).parent.mkdir(parents=True, exist_ok=True)
with open(args.outpath, 'w') as f:
for i in range(args.num_samples):
print(f'\nSampling.. ({i+1} of {args.num_samples})')
sampled_seq = model.sample(coords, temperature=args.temperature, device=device)
print('Sampled sequence:')
print(sampled_seq)
f.write(f'>sampled_seq_{i+1}\n')
f.write(sampled_seq + '\n')
recovery = np.mean([(a==b) for a, b in zip(native_seq, sampled_seq)])
print('Sequence recovery:', recovery)
def sample_seq_multichain(model, alphabet, args):
if torch.cuda.is_available() and not args.nogpu:
model = model.cuda()
print("Transferred model to GPU")
structure = esm.inverse_folding.util.load_structure(args.pdbfile)
coords, native_seqs = esm.inverse_folding.multichain_util.extract_coords_from_complex(structure)
target_chain_id = args.chain
native_seq = native_seqs[target_chain_id]
print('Native sequence loaded from structure file:')
print(native_seq)
print('\n')
print(f'Saving sampled sequences to {args.outpath}.')
Path(args.outpath).parent.mkdir(parents=True, exist_ok=True)
with open(args.outpath, 'w') as f:
for i in range(args.num_samples):
print(f'\nSampling.. ({i+1} of {args.num_samples})')
sampled_seq = esm.inverse_folding.multichain_util.sample_sequence_in_complex(
model, coords, target_chain_id, temperature=args.temperature)
print('Sampled sequence:')
print(sampled_seq)
f.write(f'>sampled_seq_{i+1}\n')
f.write(sampled_seq + '\n')
recovery = np.mean([(a==b) for a, b in zip(native_seq, sampled_seq)])
print('Sequence recovery:', recovery)
def main():
parser = argparse.ArgumentParser(
description='Sample sequences based on a given structure.'
)
parser.add_argument(
'pdbfile', type=str,
help='input filepath, either .pdb or .cif',
)
parser.add_argument(
'--chain', type=str,
help='chain id for the chain of interest', default=None,
)
parser.add_argument(
'--temperature', type=float,
help='temperature for sampling, higher for more diversity',
default=1.,
)
parser.add_argument(
'--outpath', type=str,
help='output filepath for saving sampled sequences',
default='output/sampled_seqs.fasta',
)
parser.add_argument(
'--num-samples', type=int,
help='number of sequences to sample',
default=1,
)
parser.set_defaults(multichain_backbone=False)
parser.add_argument(
'--multichain-backbone', action='store_true',
help='use the backbones of all chains in the input for conditioning'
)
parser.add_argument(
'--singlechain-backbone', dest='multichain_backbone',
action='store_false',
help='use the backbone of only target chain in the input for conditioning'
)
parser.add_argument("--nogpu", action="store_true", help="Do not use GPU even if available")
args = parser.parse_args()
model, alphabet = esm.pretrained.esm_if1_gvp4_t16_142M_UR50()
model = model.eval()
if args.multichain_backbone:
sample_seq_multichain(model, alphabet, args)
else:
sample_seq_singlechain(model, alphabet, args)
if __name__ == '__main__':
main()
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