import pandas as pd import numpy as np import requests import time import json from pathlib import Path import matplotlib.pyplot as plt from sklearn.metrics import (roc_auc_score, average_precision_score, roc_curve, precision_recall_curve) from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA import torch import torch.nn as nn import warnings warnings.filterwarnings('ignore') BASE_PATH = Path('/content/IDP') PATHS = { 'features': BASE_PATH / 'features', 'embeddings': BASE_PATH / 'embeddings', 'benchmark': BASE_PATH / 'results' / 'benchmark', 'figures': BASE_PATH / 'results' / 'figures', } PATHS['benchmark'].mkdir(parents=True, exist_ok=True) PATHS['figures'].mkdir(parents=True, exist_ok=True) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') df = pd.read_parquet(PATHS['features'] / 'features_classical_full.parquet') id_cols = ['mutation_idx', 'uniprot_acc', 'gene_symbol', 'position', 'wt_aa', 'mut_aa', 'label'] feature_cols = [c for c in df.columns if c not in id_cols] X_features = np.nan_to_num(df[feature_cols].values.astype(np.float32), nan=0.0, posinf=0.0, neginf=0.0) X_emb_raw = np.load(PATHS['embeddings'] / 'embeddings_combined_full.npy').astype(np.float32) y = df['label'].values proteins = df['uniprot_acc'].values print(f" Variants: {len(df)} | Proteins: {df['uniprot_acc'].nunique()}") PP_SIFT_CACHE = PATHS['benchmark'] / 'polyphen_sift_filtered.parquet' if PP_SIFT_CACHE.exists(): print(" ✓ PolyPhen-2/SIFT cache found — loading") df_pp_sift = pd.read_parquet(PP_SIFT_CACHE) else: our_variants = {} for _, row in df.iterrows(): acc = row['uniprot_acc'].split('-')[0] key = (acc, int(row['position']) + 1, row['wt_aa'], row['mut_aa']) our_variants[key] = row['uniprot_acc'] print(f" Lookup: {len(our_variants)} variants across " f"{df['uniprot_acc'].nunique()} proteins\n") session = requests.Session() session.headers.update({ "Accept": "application/json", "User-Agent": "research-query/1.0" }) collected = [] unique_accs = df['uniprot_acc'].unique() PARTIAL = PATHS['benchmark'] / 'pp_sift_partial.parquet' if PARTIAL.exists(): done_df = pd.read_parquet(PARTIAL) done_accs = set(done_df['uniprot_acc'].str.split('-').str[0]) print(f" Resuming — {len(done_accs)} proteins already fetched, " f"{done_df['polyphen2_score'].notna().sum()} PP2 hits so far") collected = done_df.to_dict('records') else: done_accs = set() todo_accs = [a for a in unique_accs if a.split('-')[0] not in done_accs] print(f" Fetching {len(todo_accs)} proteins from UniProt variation API …") for i, acc in enumerate(todo_accs): acc_bare = acc.split('-')[0] url = f"https://www.ebi.ac.uk/proteins/api/variation/{acc_bare}" pp2_hits = sift_hits = 0 for attempt in range(4): try: r = session.get(url, timeout=30) if r.status_code == 200: data = r.json() for feat in data.get('features', []): if feat.get('type') != 'VARIANT': continue pos_begin = feat.get('begin') wt_aa = feat.get('wildType', '') mut_aa = feat.get('alternativeSequence', '') if not pos_begin or not wt_aa or not mut_aa: continue if len(wt_aa) != 1 or len(mut_aa) != 1: continue try: pos_1 = int(pos_begin) except (ValueError, TypeError): continue key = (acc_bare, pos_1, wt_aa, mut_aa) if key not in our_variants: continue pp2_score = None sift_score = None for pred in feat.get('predictions', []): algo = pred.get('predAlgorithmNameType', '') score = pred.get('score') if score is None: continue if 'PolyPhen' in algo or 'polyphen' in algo.lower(): pp2_score = float(score) pp2_hits += 1 elif 'SIFT' in algo or 'sift' in algo.lower(): sift_score = float(score) sift_hits += 1 collected.append({ 'uniprot_acc': our_variants[key], 'position': pos_1 - 1, 'wt_aa': wt_aa, 'mut_aa': mut_aa, 'polyphen2_score': pp2_score, 'sift_score': sift_score, }) break elif r.status_code == 404: break elif r.status_code == 429: time.sleep(5 * (attempt + 1)) else: time.sleep(2 ** attempt) except requests.exceptions.Timeout: time.sleep(3) except Exception as e: time.sleep(2) time.sleep(0.2) if (i + 1) % 50 == 0: partial_df = pd.DataFrame(collected).drop_duplicates( subset=['uniprot_acc', 'position', 'wt_aa', 'mut_aa']) partial_df.to_parquet(PARTIAL, index=False) n_pp = partial_df['polyphen2_score'].notna().sum() n_sift = partial_df['sift_score'].notna().sum() print(f" … {i+1}/{len(todo_accs)} proteins | " f"variants matched: {len(partial_df)} | " f"PP2: {n_pp} | SIFT: {n_sift}") df_pp_sift = pd.DataFrame(collected).drop_duplicates( subset=['uniprot_acc', 'position', 'wt_aa', 'mut_aa']) df_pp_sift.to_parquet(PP_SIFT_CACHE, index=False) print(f"\n Matched variants: {len(df_pp_sift)}") print(f" PolyPhen-2: {df_pp_sift['polyphen2_score'].notna().sum()}") print(f" SIFT: {df_pp_sift['sift_score'].notna().sum()}") print("\n" + "=" * 60) print(" MERGING SCORES") print("=" * 60) df_am = pd.read_parquet(PATHS['benchmark'] / 'alphamissense_filtered.parquet') df_bench = df[id_cols].copy() df_bench = df_bench.merge( df_am[['uniprot_acc','position','wt_aa','mut_aa','am_pathogenicity']], on=['uniprot_acc','position','wt_aa','mut_aa'], how='left') df_bench = df_bench.merge( df_pp_sift[['uniprot_acc','position','wt_aa','mut_aa', 'polyphen2_score','sift_score']], on=['uniprot_acc','position','wt_aa','mut_aa'], how='left') df_bench['sift_score_inv'] = 1 - df_bench['sift_score'] print(f" AlphaMissense: {df_bench['am_pathogenicity'].notna().sum()} " f"({100*df_bench['am_pathogenicity'].notna().mean():.1f}%)") print(f" PolyPhen-2: {df_bench['polyphen2_score'].notna().sum()} " f"({100*df_bench['polyphen2_score'].notna().mean():.1f}%)") print(f" SIFT: {df_bench['sift_score_inv'].notna().sum()} " f"({100*df_bench['sift_score_inv'].notna().mean():.1f}%)") df_bench.to_parquet(PATHS['benchmark'] / 'benchmark_merged.parquet', index=False) class SimpleMLP(nn.Module): def __init__(self, d, h=256): super().__init__() self.net = nn.Sequential( nn.Linear(d,h), nn.ReLU(), nn.Dropout(0.3), nn.Linear(h,h//2), nn.ReLU(), nn.Dropout(0.2), nn.Linear(h//2,1), nn.Sigmoid()) def forward(self, x): return self.net(x).squeeze() def prepare(Xf, Xe, tr, te, n=128): sf = StandardScaler() Xf_tr = sf.fit_transform(Xf[tr]); Xf_te = sf.transform(Xf[te]) pca = PCA(n_components=min(n, Xe[tr].shape[0]-1), random_state=42) Xp_tr = pca.fit_transform(Xe[tr]); Xp_te = pca.transform(Xe[te]) se = StandardScaler() Xe_tr = se.fit_transform(Xp_tr); Xe_te = se.transform(Xp_te) return (np.c_[Xf_tr,Xe_tr].astype(np.float32), np.c_[Xf_te,Xe_te].astype(np.float32)) def train_pred(Xtr, ytr, Xte, epochs=50): m = SimpleMLP(Xtr.shape[1]).to(device) opt = torch.optim.Adam(m.parameters(), lr=0.001, weight_decay=1e-4) crit = nn.BCELoss() Xt = torch.FloatTensor(Xtr).to(device) yt = torch.FloatTensor(ytr).to(device) Xv = torch.FloatTensor(Xte).to(device) m.train() for _ in range(epochs): opt.zero_grad(); crit(m(Xt), yt).backward(); opt.step() m.eval() with torch.no_grad(): return m(Xv).cpu().numpy() OUR_CACHE = PATHS['benchmark'] / 'our_model_lpocv_preds.npy' if OUR_CACHE.exists(): print("\n ✓ Model predictions cache found") our_preds = np.load(OUR_CACHE) else: our_preds = np.full(len(df), np.nan) ups = np.unique(proteins) print(f"\n Running LPOCV ({len(ups)} proteins) …") for i, p in enumerate(ups): te = proteins == p; tr = ~te if te.sum() < 2 or tr.sum() < 10: continue Xtr, Xte = prepare(X_features, X_emb_raw, tr, te) our_preds[te] = train_pred(Xtr, y[tr], Xte) if i % 50 == 0: print(f" … {i}/{len(ups)} proteins") np.save(OUR_CACHE, our_preds) print(" Saved") df_bench['our_score'] = our_preds tools = { 'Our model (MLP + ESM-2)': 'our_score', 'AlphaMissense': 'am_pathogenicity', 'PolyPhen-2': 'polyphen2_score', 'SIFT (inverted)': 'sift_score_inv', } results = {} for name, col in tools.items(): mask = df_bench[col].notna() & df_bench['our_score'].notna() sub = df_bench[mask] if len(sub) < 50: print(f" ⚠ {name}: only {len(sub)} variants — skipping") continue results[name] = { 'auc_roc': roc_auc_score(sub['label'], sub[col]), 'auc_pr': average_precision_score(sub['label'], sub[col]), 'n': len(sub), 'col': col, 'mask': mask} print(f" {name:<35} n={len(sub):>6,} " f"AUC-ROC={results[name]['auc_roc']:.3f} " f"AUC-PR={results[name]['auc_pr']:.3f}")