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31d1d63 | 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 | #!/usr/bin/env python
import json
import sys
from pathlib import Path
import numpy as np
ROOT = Path(__file__).resolve().parents[2]
FIG = ROOT / "data" / "p53"
CACHE = FIG / "fig_data"
sys.path.insert(0, str(ROOT))
HIGHLIGHT = [56, 0]
RARE_THRESH = 0.98
def main():
from scoobdoob.validation.pocket import site_drug
CACHE.mkdir(parents=True, exist_ok=True)
M = np.load(FIG / "rmsd_matrix.npy")
mids = sorted(
int(p.stem.replace("micro", ""))
for p in (FIG / "pocket_reps").glob("micro*.pdb")
)
if len(mids) != M.shape[0]:
raise SystemExit(
f"{len(mids)} representatives but rmsd_matrix has {M.shape[0]} rows"
)
drug = np.array([site_drug(m) for m in mids], dtype=float)
truth = np.nanmedian(M, axis=1)
closest = np.nanmin(M, axis=1)
order = np.argsort(truth)
rank = {mids[j]: r + 1 for r, j in enumerate(order)}
pij = json.loads((FIG / "pocket_msm_pi.json").read_text())
pi = np.array([float(pij[str(m)]) if str(m) in pij else np.nan for m in mids])
if not np.isfinite(pi).all():
missing = [m for m, p in zip(mids, pi) if not np.isfinite(p)]
raise SystemExit(
f"no stationary probability for microstates {missing}; "
f"run upstream/dump_pocket_msm_pi.py first"
)
rows = json.loads((FIG / "frame_drug" / "frames.json").read_text())
v = np.array(
[
r.get("site_drug") if r.get("site_drug") is not None else np.nan
for r in rows
],
dtype=float,
)
import pickle
meta = pickle.load(open(FIG / "pocket_feats_s10_cb_ALL.pkl", "rb"))["meta"]
cls = np.array(
[meta[r["traj"]]["cls"] if r["traj"] < len(meta) else "?" for r in rows]
)
fin = np.isfinite(v)
v_ref, v_seed = v[fin & (cls == "folded")], v[fin & (cls == "seed")]
v = v[fin]
cov = json.loads((FIG / "coverage_all_y220c.json").read_text())
apo_profile = np.sort(np.array([r["apo_to_holo"] for r in cov], dtype=float))
hi_idx = [mids.index(m) for m in HIGHLIGHT]
n_rare = int((v >= RARE_THRESH).sum())
share = {
f"ge_{t}": {
"n": int((v >= t).sum()),
"reference": int((v_ref >= t).sum()),
"seeded": int((v_seed >= t).sum()),
}
for t in (0.8, 0.9, 0.95, RARE_THRESH)
}
np.savez_compressed(
CACHE / "discovery.npz",
mids=np.array(mids),
drug=drug,
truth=truth,
closest=closest,
pi=pi,
order=order,
frame_scores=v,
frame_scores_ref=v_ref,
frame_scores_seed=v_seed,
highlight=np.array(HIGHLIGHT),
highlight_idx=np.array(hi_idx),
highlight_rmsd_profiles=np.sort(M[hi_idx], axis=1),
apo_rmsd_profile=apo_profile,
n_complexes=np.array(M.shape[1]),
)
stats = {
"n_microstates": len(mids),
"n_complexes": int(M.shape[1]),
"n_frames": int(len(v)),
"frame_median": float(np.median(v)),
"rare_threshold": RARE_THRESH,
"n_frames_ge_thresh": n_rare,
"n_frames_reference": int(len(v_ref)),
"n_frames_seeded": int(len(v_seed)),
"high_druggability_share": share,
"frame_max": float(v.max()),
"top_rep_drug": float(drug.max()),
"apo_to_holo_median": float(np.median(apo_profile)),
"n_ensemble_closer_than_apo": int(
sum(1 for r in cov if r["best_ens"] < r["apo_to_holo"])
),
"highlight": {
str(m): {
"rank": int(rank[m]),
"median_rmsd": float(truth[mids.index(m)]),
"closest_rmsd": float(closest[mids.index(m)]),
"rep_drug": float(drug[mids.index(m)]),
"pi": float(pi[mids.index(m)]),
}
for m in HIGHLIGHT
},
"table_top10": [
{
"rank": r + 1,
"micro": int(mids[j]),
"median_rmsd": float(truth[j]),
"closest_rmsd": float(closest[j]),
"rep_drug": float(drug[j]),
"pi": float(pi[j]),
}
for r, j in enumerate(order[:10])
],
}
(CACHE / "discovery.json").write_text(json.dumps(stats, indent=1, default=float))
print(f" {len(mids)} microstates, {M.shape[1]} complexes, {len(v)} scored frames")
print(
f" most holo-like: microstate {mids[order[0]]} "
f"(median {truth[order[0]]:.2f} A, closest {closest[order[0]]:.2f} A, "
f"druggability {drug[order[0]]:.3f}, pi {pi[order[0]]:.4f})"
)
print(
f" frames >= {RARE_THRESH}: {n_rare}/{len(v)} "
f"({100 * n_rare / len(v):.3f}%), ensemble median {np.median(v):.3f}"
)
print(f"-> {CACHE / 'discovery.npz'}\n-> {CACHE / 'discovery.json'}")
if __name__ == "__main__":
main()
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