File size: 4,856 Bytes
9d6a2a3 | 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 | import argparse
import os
import pickle
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from _common import add_model_args, formatter, runtime_kwargs, score_ligand_file
def parse_args():
parser = argparse.ArgumentParser(
description="Run GenScore CASF-2016 docking benchmark.",
formatter_class=formatter(),
)
add_model_args(parser)
parser.add_argument("--casf-dir", required=True, help="CASF-2016 root directory.")
parser.add_argument("--pdbbind-dir", required=True, help="PDBbind root directory.")
parser.add_argument(
"--native-ligand-dir",
default=None,
help="Directory containing <pdbid>_ligand.mol2 files.",
)
parser.add_argument("--outdir", required=True, help="Directory for CASF docking score .dat files.")
parser.add_argument("--decoys-subdir", default="decoys_docking")
parser.add_argument("--coreset-subdir", default="coreset")
parser.add_argument("--refined-subdir", default="v2020-refined")
parser.add_argument("--other-pl-subdir", default="v2020-other-PL")
parser.add_argument("--parallel", action="store_true", default=False)
return parser.parse_args()
def _complex_ids(path):
return [name for name in os.listdir(path) if os.path.isdir(os.path.join(path, name))]
def _pocket_path(pdbbind_dir, subset, pdbid, cutoff):
return os.path.join(
pdbbind_dir,
subset,
pdbid,
f"{pdbid}_prot",
f"{pdbid}_p_pocket_{cutoff}.pdb",
)
def _native_ligand_path(pdbbind_dir, native_ligand_dir, subset, pdbid):
candidates = []
if native_ligand_dir:
candidates.append(
os.path.join(native_ligand_dir, f"{pdbid}_ligand.mol2")
)
candidates.extend([
os.path.join(pdbbind_dir, "mol2", f"{pdbid}_ligand.mol2"),
os.path.join(
pdbbind_dir, subset, pdbid, f"{pdbid}_prot",
f"{pdbid}_l.mol2",
),
os.path.join(
pdbbind_dir, subset, pdbid, f"{pdbid}_prot",
f"{pdbid}_l.sdf",
),
])
for candidate in candidates:
if os.path.isfile(candidate):
return candidate
raise FileNotFoundError(
f"No native ligand found for {pdbid}. Checked: "
+ ", ".join(candidates)
)
def _decoys_path(casf_dir, decoys_subdir, pdbid):
root = os.path.join(casf_dir, decoys_subdir)
return _ligand_path(root, f"{pdbid}_decoys")
def _ligand_path(root, stem):
candidates = [
os.path.join(root, f"{stem}.{extension}")
for extension in ("sdf", "mol2")
]
for candidate in candidates:
if os.path.isfile(candidate):
return candidate
raise FileNotFoundError(
f"No ligand file found for {stem}. Checked: {', '.join(candidates)}"
)
def score_compound(pdbid, subset, args):
prot = _pocket_path(args.pdbbind_dir, subset, pdbid, args.cutoff)
decoys = _decoys_path(args.casf_dir, args.decoys_subdir, pdbid)
ids_decoy, scores_decoy = score_ligand_file(prot, decoys, args, parallel=True)
native = _native_ligand_path(
args.pdbbind_dir,
args.native_ligand_dir,
subset,
pdbid,
)
ids_native, scores_native = score_ligand_file(prot, native, args, parallel=False)
ids_native = list(ids_native)
ids_native.pop(-1)
ids_native.append(f"{pdbid}_ligand")
return ids_decoy + ids_native, np.append(scores_decoy, scores_native)
def run_for_subset(pdbids, subset, args):
if args.parallel and runtime_kwargs(args)["device"] == "cpu":
return Parallel(n_jobs=-1, backend="threading")(
delayed(score_compound)(pdbid, subset, args) for pdbid in pdbids
)
return [score_compound(pdbid, subset, args) for pdbid in pdbids]
def main():
args = parse_args()
coreset = _complex_ids(os.path.join(args.casf_dir, args.coreset_subdir))
refined_ids = set(_complex_ids(os.path.join(args.pdbbind_dir, args.refined_subdir)))
other_ids = set(_complex_ids(os.path.join(args.pdbbind_dir, args.other_pl_subdir)))
ids_refined = [pdbid for pdbid in coreset if pdbid in refined_ids]
ids_other = [pdbid for pdbid in coreset if pdbid in other_ids]
results = run_for_subset(ids_refined, args.refined_subdir, args)
results += run_for_subset(ids_other, args.other_pl_subdir, args)
os.makedirs(args.outdir, exist_ok=True)
for ids, scores in results:
pdbid = ids[0].split("_")[0]
df = pd.DataFrame(zip(ids, scores), columns=["#code", "score"])
df["#code"] = df["#code"].str.split("-").apply(lambda item: item[0])
df.to_csv(os.path.join(args.outdir, f"{pdbid}_score.dat"), index=False, sep="\t")
with open(f"{args.outprefix}_docking.pkl", "wb") as handle:
pickle.dump(results, handle)
if __name__ == "__main__":
main()
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