Datasets:
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0613483 | 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 | """Classify each Chroma pose as surface-binding or buried inside the target.
Runs the surface-pose filter from ``utils.pose_filtering`` over the pose PDBs
on disk and writes one verdict per (target, length, pose) to
``benchmark/geometric_pose_classification.csv``. The verdict depends only on the
backbone, so it applies to every sequence designed on that pose.
"""
from __future__ import annotations
import re
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
import pandas as pd
import sys
ROOT = Path(__file__).resolve().parents[2]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from utils.pose_filtering import ( # noqa: E402
DEFAULT_SURFACE_FILTER,
passes_surface_pose_filter,
surface_pose_metrics,
)
BENCH = ROOT / "benchmark"
# (target, peptide_mode, designs_root). ``designs_root`` is the directory that
# holds ``len_*/Poses/Binder_{D,L}_pose_*.pdb``.
SCAN_DIRS = [
("TNFalpha", "D_peptide", BENCH / "new_targets" / "designs" / "TNFalpha" / "D_peptide"),
("CXCR2", "D_peptide", BENCH / "new_targets" / "designs" / "CXCR2" / "D_peptide"),
("CXCR4", "D_peptide", BENCH / "new_targets" / "designs" / "CXCR4" / "D_peptide"),
("PDL1", "D_peptide", BENCH / "PDL1" / "D_peptide"),
("PDL1", "L_peptide", BENCH / "PDL1" / "L_peptide"),
("MDM2", "D_peptide", BENCH / "MDM2" / "D_peptide"),
("MDM2", "L_peptide", BENCH / "MDM2" / "L_peptide"),
("IL23R", "D_peptide", BENCH / "IL23R" / "D_peptide"),
("IL23R", "L_peptide", BENCH / "IL23R" / "L_peptide"),
]
POSE_RE = re.compile(r"Binder_[DL]_pose_(\d+)\.pdb$", re.IGNORECASE)
def discover() -> list[dict]:
rows = []
for target, mode, root in SCAN_DIRS:
if not root.exists():
print(f" [skip] missing: {root}")
continue
for pose_pdb in sorted(root.glob("len_*/Poses/Binder_[DL]_pose_*.pdb")):
m = POSE_RE.search(pose_pdb.name)
if not m:
continue
length = int(pose_pdb.parents[1].name.split("_")[1])
pose = int(m.group(1))
rows.append(
{
"target": target,
"peptide_mode": mode,
"length": length,
"pose": pose,
"pose_pdb": str(pose_pdb.relative_to(ROOT)),
}
)
return rows
def classify_one(row: dict) -> dict:
pdb = ROOT / row["pose_pdb"]
try:
metrics = surface_pose_metrics(
pdb,
binder_chain="B",
nearby_distance=DEFAULT_SURFACE_FILTER["nearby_distance"],
contact_distance=DEFAULT_SURFACE_FILTER["contact_distance"],
)
passed = passes_surface_pose_filter(
metrics,
radial_min=DEFAULT_SURFACE_FILTER["radial_min"],
outward_nearby_max=DEFAULT_SURFACE_FILTER["outward_nearby_max"],
occupied_octants_max=DEFAULT_SURFACE_FILTER["occupied_octants_max"],
min_binder_atoms_within_contact=DEFAULT_SURFACE_FILTER[
"min_binder_atoms_within_contact"
],
)
out = dict(row)
out.update(metrics)
out["passes_surface"] = bool(passed)
return out
except Exception as exc: # pragma: no cover - defensive
out = dict(row)
out["error"] = f"{type(exc).__name__}: {exc}"
out["passes_surface"] = False
return out
def main():
jobs = discover()
print(f"Discovered {len(jobs)} pose PDBs to classify")
results = []
with ProcessPoolExecutor(max_workers=16) as ex:
futs = {ex.submit(classify_one, j): j for j in jobs}
done = 0
for fut in as_completed(futs):
results.append(fut.result())
done += 1
if done % 500 == 0:
print(f" ...{done}/{len(jobs)}")
df = pd.DataFrame(results)
metric_cols = [
"radial_percentile",
"outward_nearby_fraction",
"occupied_octants",
"binder_atoms_within_contact",
"min_receptor_distance",
"median_receptor_distance",
]
cols = ["target", "peptide_mode", "length", "pose", "passes_surface"] + metric_cols + ["pose_pdb"]
for c in cols:
if c not in df.columns:
df[c] = pd.NA
df = df[cols]
out = BENCH / "geometric_pose_classification.csv"
df.to_csv(out, index=False)
print(f"\nSaved {out} ({len(df)} rows)")
print("\nSurface-pose verdict by target/mode:")
summary = (
df.groupby(["target", "peptide_mode"])
.agg(
n_poses=("pose", "size"),
n_surface=("passes_surface", "sum"),
)
.reset_index()
)
summary["n_buried"] = summary["n_poses"] - summary["n_surface"]
summary["pct_surface"] = (summary["n_surface"] / summary["n_poses"] * 100).round(1)
print(summary.to_string(index=False))
errors = df[df["radial_percentile"].isna()]
if len(errors):
print(f"\n[warn] {len(errors)} poses failed to compute metrics")
if __name__ == "__main__":
main()
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