File size: 6,093 Bytes
d766458 | 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 | import argparse
import shutil
import subprocess
from pathlib import Path
import numpy as np
from pxdbench.metrics.diversity import compute_diversity
from pxdbench.utils import str2bool
FOLDSEEK_BIN = "your_foldseek_dir/bin/foldseek"
FOLDSEEK_DB = "your_foldseek_dir/foldseek_db/pdb/pdb"
def compute_fs_diversity(input_dir: Path, num_threads=32):
num_pdbs = sum(1 for f in input_dir.glob("*.pdb") if f.is_file())
cluster_out = input_dir / "fs_diversity" / "res"
tmp_dir = input_dir / "fs_diversity_tmp"
cluster_out.parent.mkdir(parents=True, exist_ok=True)
cluster_tsv = cluster_out.with_name(cluster_out.stem + "_cluster.tsv")
if tmp_dir.exists():
shutil.rmtree(tmp_dir)
tmp_dir.mkdir(parents=True, exist_ok=True)
if not cluster_tsv.exists():
subprocess.run(
[
FOLDSEEK_BIN,
"easy-cluster",
str(input_dir),
str(cluster_out),
str(tmp_dir),
"--alignment-type",
"1",
"--cov-mode",
"0",
"--min-seq-id",
"0",
"--tmscore-threshold",
"0.5",
"--threads",
f"{num_threads}",
],
check=True,
)
# Count clusters and samples
num_clusters = 0
seen_clusters = set()
if cluster_tsv.exists():
with open(cluster_tsv) as f:
for line in f:
cluster_name, member = line.strip().split("\t")
if cluster_name not in seen_clusters:
seen_clusters.add(cluster_name)
num_clusters += 1
diversity_cluster = num_clusters / max(num_pdbs, 1)
shutil.rmtree(tmp_dir)
return diversity_cluster
def compute_fs_novelty(input_dir: Path, use_gpu=True, num_threads=32):
tmp_dir = input_dir / "fs_novelty_tmp"
if tmp_dir.exists():
shutil.rmtree(tmp_dir)
novelty_out = input_dir / "fs_novelty" / "novelty.tsv"
novelty_out.parent.mkdir(parents=True, exist_ok=True)
if not novelty_out.exists():
cmd = [
FOLDSEEK_BIN,
"easy-search",
str(input_dir),
FOLDSEEK_DB,
str(novelty_out),
str(tmp_dir),
"--alignment-type",
"1",
"--exhaustive-search",
"--tmscore-threshold",
"0.0",
"--max-seqs",
"10000000000",
"--format-output",
"query,target,alntmscore,lddt",
"--threads",
f"{num_threads}",
]
if use_gpu:
cmd.extend(["--gpu", "1", "--prefilter-mode", "1"])
subprocess.run(
cmd,
check=True,
)
max_scores = {}
if novelty_out.exists():
with open(novelty_out) as f:
for line in f:
query, _, tmscore, _ = line.strip().split("\t")
tmscore = float(tmscore)
if query not in max_scores or tmscore > max_scores[query]:
max_scores[query] = tmscore
novelty_score = np.mean(list(max_scores.values()))
shutil.rmtree(tmp_dir)
return novelty_score
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--input_dir", type=str)
parser.add_argument("--output_dir", type=str, default=None)
parser.add_argument("--eval_novelty", action="store_true", default=False)
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--num_threads", type=int, default=32)
args = parser.parse_args()
input_dir = Path(args.input_dir)
if not input_dir.exists():
print(f"Input dir does not exist! {input_dir}")
return
if args.output_dir is None:
output_dir = input_dir / "postprocess"
else:
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
output_csv_path = output_dir / "diversity_and_novelty.csv"
fieldnames = ["num_samples", "diversity_tm", "diversity_cluster", "novelty"]
# scan PDBs
pdb_paths = sorted(input_dir.glob("*.pdb"))
pdb_paths = [p for p in pdb_paths if p.is_file()]
if len(pdb_paths) == 0:
print(f"No PDB files found in {input_dir}")
# wrtie an empty file
with open(output_csv_path, "w") as f:
f.write(",".join(fieldnames) + "\n")
f.write("0,,,\n")
return
# Diversity (TM)
try:
diversity_tm = compute_diversity(pdb_paths) if len(pdb_paths) >= 2 else np.nan
except Exception as e:
print(f"compute_diversity failed: {e}")
diversity_tm = np.nan
# Diversity (Cluster, Foldseek)
try:
diversity_cluster = compute_fs_diversity(
input_dir, num_threads=args.num_threads
)
except Exception as e:
print(f"compute_fs_diversity failed: {e}")
diversity_cluster = np.nan
novelty = np.nan
if args.eval_novelty:
if not Path(FOLDSEEK_BIN).exists():
print(f"Foldseek binary not found: {FOLDSEEK_BIN}, skip novelty.")
elif (
not Path(FOLDSEEK_DB + ".dbtype").exists()
and not Path(FOLDSEEK_DB).exists()
):
print(f"Foldseek DB not found: {FOLDSEEK_DB}, skip novelty.")
else:
try:
novelty = compute_fs_novelty(
input_dir, args.use_gpu, num_threads=args.num_threads
)
except Exception as e:
print(f"compute_fs_novelty failed: {e}")
novelty = np.nan
with open(output_csv_path, "w") as f:
f.write(",".join(fieldnames) + "\n")
f.write(f"{len(pdb_paths)},{diversity_tm},{diversity_cluster},{novelty}\n")
print(f"Wrote results to {output_csv_path}")
if __name__ == "__main__":
main()
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