Update scripts/data_curation/fetch_abc_sequences.py

scripts/data_curation/fetch_abc_sequences.py

@@ -1,8 +1,4 @@
-# ==== Internet-enabled ABC harvest (SGD + UniProt) → protein.csv (ESM2) ====
-# - Pulls ABC transporters via SGD YeastMine (GO terms) with retries
-# - Fallback: UniProt query for "ATP-binding cassette" in S. cerevisiae (taxid:559292)
-# - Fetches FASTA (longest per gene), embeds with ESM2, saves protein.csv + manifest
-# - Checkpoints every 10 genes (safe to interrupt/resume)
+
 
 import os, re, time, json, math, requests, numpy as np, pandas as pd, torch
 from pathlib import Path
@@ -10,9 +6,6 @@ from tqdm.auto import tqdm
 from transformers import AutoTokenizer, AutoModel
 from contextlib import contextmanager
 
-# -------------------------------------------
-# Config
-# -------------------------------------------
 DATA_RAW  = Path("data/raw"); DATA_RAW.mkdir(parents=True, exist_ok=True)
 DATA_PROC = Path("data/processed"); DATA_PROC.mkdir(parents=True, exist_ok=True)
 FASTA_OUT = DATA_RAW/"yeast_abc_full.fasta"
@@ -20,26 +13,21 @@ MANIFEST  = DATA_PROC/"protein_manifest.csv"
 PROT_CSV  = DATA_PROC/"protein.csv"
 
 DEVICE    = "cuda" if torch.cuda.is_available() else "cpu"
-ESM_MODEL = "facebook/esm2_t33_650M_UR50D"  # for speed: "facebook/esm2_t12_35M_UR50D"
+ESM_MODEL = "facebook/esm2_t33_650M_UR50D"  
 
 GO_TERMS = [
-    # ABC-type transporter activity (MF)
     "ABC-type transporter activity",
-    # ATPase-coupled transmembrane transporter activity (MF)
     "ATPase-coupled transmembrane transporter activity",
 ]
-MIN_ABC_TARGET = 30  # Nature Gate threshold
+MIN_ABC_TARGET = 30  
 
-# Optional: seed list to guarantee we never fall below threshold
 SEED_ABCS = {
     "PDR5","SNQ2","YOR1","PDR15","PDR10","PDR11","PDR12","PDR18",
     "YCF1","YBT1","ATM1","VBA1","VBA2","VBA3","VBA4",
     "MDL1","MDL2","AUS1","PDR16","PDR17","STE6",
 }
 
-# -------------------------------------------
-# Helpers
-# -------------------------------------------
+
 session = requests.Session()
 session.headers.update({"User-Agent":"abc-atlas-colab/1.0"})
 
@@ -58,7 +46,6 @@ def yeastmine_abc_symbols():
     base = "https://yeastmine.yeastgenome.org/yeastmine/service/query/results"
     symbols = set(); rows_all=[]
     for term in GO_TERMS:
-        # Minimal XML query
         q = f"""
         <query model="genomic" view="Gene.primaryIdentifier Gene.symbol Gene.secondaryIdentifier Gene.name Gene.organism.shortName Gene.goAnnotation.ontologyTerm.name">
           <constraint path="Gene.organism.name" op="=" value="Saccharomyces cerevisiae"/>
@@ -69,7 +56,7 @@ def yeastmine_abc_symbols():
             r = backoff_get(base, method="POST", data={"format":"json","query":q})
             rows = r.json().get("results", [])
             for row in rows:
-                sgdid = row.get("field1")           # primaryIdentifier
+                sgdid = row.get("field1") 
                 symbol = row.get("field2") or row.get("field1")
                 sysid = row.get("field3") or ""
                 gohit = row.get("field6") or ""
@@ -77,16 +64,12 @@ def yeastmine_abc_symbols():
                     symbols.add(symbol)
                     rows_all.append({"sgd_primary":sgdid,"symbol":symbol,"systematic":sysid,"go_term":gohit})
         except Exception as e:
-            # continue; we'll fallback to UniProt too
             pass
-    # ensure seed ABCs included
     for s in SEED_ABCS: symbols.add(s)
     return symbols, pd.DataFrame(rows_all).drop_duplicates()
 
 def uniprot_symbols_by_keyword():
     """Fallback: UniProt keyword/family text search to collect additional ABCs in S. cerevisiae."""
-    # query for reviewed + proteome of S. cerevisiae (559292) and 'ATP-binding cassette' in annotation
-    # We retrieve gene symbols from results
     q = 'organism_id:559292 AND (annotation:"ATP-binding cassette" OR keyword:"Transport" OR family:"ABC")'
     url = f"https://rest.uniprot.org/uniprotkb/search?query={requests.utils.quote(q)}&format=json&size=500&fields=accession,genes(PREFERRED),protein_name"
     try:
@@ -127,9 +110,6 @@ def maybe_amp(device=DEVICE):
     else:
         yield
 
-# -------------------------------------------
-# 1) Harvest ABC gene symbols (SGD → UniProt fallback)
-# -------------------------------------------
 symbols_sgd, sgd_table = yeastmine_abc_symbols()
 symbols_uni = uniprot_symbols_by_keyword()
 symbols = sorted(set(symbols_sgd) | set(symbols_uni) | SEED_ABCS)
@@ -137,9 +117,6 @@ print(f"Collected candidate ABC symbols: n={len(symbols)}")
 if len(symbols) < MIN_ABC_TARGET:
     print("Warning: few symbols found via network; will still proceed with seeds.")
 
-# -------------------------------------------
-# 2) Fetch FASTA (longest per symbol) and build manifest
-# -------------------------------------------
 by_gene = {}
 manifest_rows = []
 for g in tqdm(symbols, desc="Fetch UniProt FASTA"):
@@ -148,22 +125,18 @@ for g in tqdm(symbols, desc="Fetch UniProt FASTA"):
         recs = parse_fasta(txt)
         if not recs:
             continue
-        # keep the longest sequence
         h, seq = max(recs, key=lambda r: len(r[1]))
         by_gene[g] = (h, seq)
-        # extract a UniProt accession if present in header
         acc = None
         m = re.search(r"\|([A-Z0-9]{6,10})\|", h)
         if m: acc = m.group(1)
         manifest_rows.append({"symbol": g, "uniprot_header": h, "uniprot_acc": acc})
     except Exception:
-        # skip problematic gene, continue
         continue
 
 if not by_gene:
     raise SystemExit("No FASTA fetched; check network and retry.")
 
-# Save FASTA (one record per symbol)
 with open(FASTA_OUT, "w") as f:
     for g, (_, seq) in by_gene.items():
         f.write(f">{g}\n")
@@ -171,22 +144,18 @@ with open(FASTA_OUT, "w") as f:
             f.write(seq[i:i+80] + "\n")
 print(f"Saved FASTA for {len(by_gene)} genes → {FASTA_OUT}")
 
-# Merge manifest with SGD info when possible
 mf = pd.DataFrame(manifest_rows)
 if not sgd_table.empty:
     mf = mf.merge(sgd_table, how="left", left_on="symbol", right_on="symbol")
 mf.to_csv(MANIFEST, index=False)
 print(f"Saved manifest → {MANIFEST} | columns: {list(mf.columns)}")
 
-# -------------------------------------------
-# 3) ESM2 embeddings (1280-D) with AMP + checkpointing
-# -------------------------------------------
 tok = AutoTokenizer.from_pretrained(ESM_MODEL)
 mdl = AutoModel.from_pretrained(ESM_MODEL).eval().to(DEVICE)
 
 rows = []
 done = 0
-# resume support if partial exists
+
 if PROT_CSV.exists():
     prev = pd.read_csv(PROT_CSV)
     done_syms = set(prev["transporter"])
@@ -209,24 +178,16 @@ for i, g in enumerate(tqdm(keys, desc="ESM2 embed"), 1):
         emb = vec.squeeze(0).cpu().numpy().astype(np.float32)
     rows.append([g] + emb.tolist())
 
-    # checkpoint every 10 genes
     if (i % 10 == 0) or (i == len(keys)):
         df = pd.DataFrame(rows, columns=["transporter"] + [f"d{i}" for i in range(emb.shape[0])])
         df = df.drop_duplicates("transporter").sort_values("transporter").reset_index(drop=True)
         df.to_csv(PROT_CSV, index=False)
 
-# Final save & report
 P = pd.read_csv(PROT_CSV)
 print("protein.csv →", PROT_CSV, "| shape:", P.shape, "| n_transporters:", P["transporter"].nunique())
 if P["transporter"].nunique() < MIN_ABC_TARGET:
     print("⚠️ Note: fewer than 30 ABCs detected. Consider re-running later or adding extra symbols to SEED_ABCS.")
 
-# === Augment ABC panel to ≥30 (real-first, synthetic fallback) ===
-# - Re-queries UniProt for a conservative list of known S. cerevisiae ABC transporters
-# - Embeds any newly found sequences with ESM2
-# - If still <30, creates synthetic ABC-like sequences (clearly labeled) and embeds them
-# - Updates protein.csv and writes/extends protein_manifest.csv with provenance
-
 import re, time, requests, numpy as np, pandas as pd, torch
 from pathlib import Path
 from transformers import AutoTokenizer, AutoModel
@@ -238,9 +199,8 @@ MANIFEST  = DATA_PROC/"protein_manifest.csv"
 PROT_CSV  = DATA_PROC/"protein.csv"
 
 DEVICE    = "cuda" if torch.cuda.is_available() else "cpu"
-ESM_MODEL = "facebook/esm2_t33_650M_UR50D"  # swap to t12_35M to speed up if needed
+ESM_MODEL = "facebook/esm2_t33_650M_UR50D" 
 
-# Conservative canonical ABCs in S. cerevisiae (curated & widely reported)
 CANON = [
     "PDR5","PDR10","PDR11","PDR12","PDR15","PDR18",
     "SNQ2","YOR1","YCF1","YBT1","ATM1",
@@ -249,7 +209,6 @@ CANON = [
     "STE6",
 ]
 
-# ---------- helpers ----------
 sess = requests.Session()
 sess.headers.update({"User-Agent":"abc-atlas-colab/1.0"})
 
@@ -283,16 +242,13 @@ def esm_embed(seq: str) -> np.ndarray:
     return vec.squeeze(0).cpu().numpy().astype(np.float32)
 
 def synth_abc_sequence(seed=0, L=1350):
-    # ABC-like toy sequence with conserved motifs to keep embeddings in-distribution
     rng = np.random.default_rng(seed)
     alphabet = list("AVLIFWGSTMPQNDEKRHYC")
     core = "".join(rng.choice(alphabet, size=L-30))
-    # Walker A (GxxxxGKT), ABC signature (LSGGQ), Walker B (hhhhDE)
     motif = "GGKT" + "LSGGQ" + "VVVVDE"
     seq = core[:L-30] + motif + core[L-30:]
     return seq[:L]
 
-# ---------- load current protein.csv & manifest ----------
 if PROT_CSV.exists():
     P = pd.read_csv(PROT_CSV)
 else:
@@ -305,7 +261,6 @@ else:
 
 have = set(P["transporter"]) if not P.empty else set()
 
-# ---------- 1) Try to add missing CANON entries from UniProt ----------
 added_real = []
 man_rows = []
 for g in CANON:
@@ -327,10 +282,8 @@ for g in CANON:
         added_real.append(g)
         have.add(g)
     except Exception:
-        # skip and continue
         pass
 
-# ---------- 2) If still <30, synthesize placeholders ----------
 target = 30
 if P["transporter"].nunique() < target:
     need = target - P["transporter"].nunique()
@@ -344,7 +297,6 @@ if P["transporter"].nunique() < target:
         man_rows.append({"symbol": name, "uniprot_header": "NA", "uniprot_acc": None, "source": "synthetic"})
     P = pd.concat([P, pd.DataFrame(rows_syn, columns=["transporter"]+[f"d{i}" for i in range(1280)])], ignore_index=True)
 
-# ---------- 3) Save outputs (de-dup & sort) ----------
 P = P.drop_duplicates("transporter").sort_values("transporter").reset_index(drop=True)
 P.to_csv(PROT_CSV, index=False)
 
@@ -355,15 +307,12 @@ MF.to_csv(MANIFEST, index=False)
 print(f"protein.csv -> {PROT_CSV} | shape: {P.shape} | n_transporters: {P['transporter'].nunique()}")
 print(f"manifest -> {MANIFEST} | rows: {len(MF)} (new real added: {added_real})")
 
-# Optional: append synthetic entries to FASTA with clear headers
 if FASTA_OUT.exists():
     with open(FASTA_OUT, "a") as f:
         for r in man_rows:
             if r.get("source") == "synthetic":
                 f.write(f">{r['symbol']} | synthetic\n")
-                # we didn't store seq, so we skip writing actual sequences to FASTA for synthetic placeholders
 else:
-    # create minimal FASTA for book-keeping (headers only)
     with open(FASTA_OUT, "w") as f:
         for r in man_rows:
             f.write(f">{r['symbol']}\n")