new pipeline and subsets

data/{subsets/mutation_lm/test.parquet → fireprotdb_with_cluster_splits.parquet}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b09520f184217a8894faf285681294c56d7e0d8536d96f4ea04ff4ea17a1f2cb
-size 8654908
+oid sha256:7a7685586c8a203b6548f7379f99e3dfec24fd1c8b161df8f4d879da455a86b4
+size 109389319

data/{subsets/mutation_lm/train.parquet → fireprotdb_with_sequences.parquet}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:20c2b1ac6f218e111f03a71ff7537365a49971e7a8a4c6fd079a341483fde2bf
-size 83746592
+oid sha256:dbf94bfb8917bfc24593c4ad7d8b65de0c06e36ee626112e55b41d9b81146576
+size 109262102

data/mmseqs_clusters_cluster.tsv

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data/mmseqs_tmp/11046995532769277730/clu_rep.source

@@ -0,0 +1 @@
+/data/hackathon/FireProtDB/data/mmseqs_tmp/11046995532769277730/input.source

data/mmseqs_tmp/11046995532769277730/clu_rep_h

@@ -0,0 +1 @@
+/data/hackathon/FireProtDB/data/proteins.fasta

data/mmseqs_tmp/11046995532769277730/clu_rep_h.dbtype

@@ -0,0 +1 @@
+/data/hackathon/FireProtDB/data/mmseqs_tmp/11046995532769277730/input_h.dbtype

data/mmseqs_tmp/11046995532769277730/clu_rep_h.index

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+/data/hackathon/FireProtDB/data/mmseqs_tmp/11046995532769277730/input_h.index

data/mmseqs_tmp/latest

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+11046995532769277730

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src/01_process_csv.py

@@ -0,0 +1,330 @@
+#!/usr/bin/env python3
+"""
+Clean FireProtDB 2.0 CSV into ML-ready canonical table.
+
+Outputs:
+- A canonical row-per-experiment table with parsed mutation fields and normalized columns.
+- Optionally writes Parquet for speed.
+
+Usage:
+  python scripts/clean_fireprotdb.py \
+    --input fireprotdb_2_0.csv \
+    --output data/fireprotdb_clean.parquet
+
+Notes:
+- This script is conservative: it does NOT impute missing ddg/dtm.
+- It standardizes a few categorical fields; extend mappings as needed.
+"""
+
+from __future__ import annotations
+
+import argparse
+import math
+import re
+from typing import Optional, Tuple, Dict
+
+import pandas as pd
+###PDB parsing
+_PDB_SPLIT = re.compile(r"[;,| ]+")
+_PDB_ID = re.compile(r"^[0-9][A-Za-z0-9]{3}$")  # 4-char PDB id, first char numeric
+
+def parse_pdb_ids(x: object):
+    """
+    Returns (pdb_id, pdb_ids) where:
+      - pdb_id: first valid 4-char PDB id (lowercase), or None
+      - pdb_ids: sorted unique list of valid ids (lowercase)
+    """
+    if not isinstance(x, str):
+        return None, []
+    s = x.strip()
+    if not s:
+        return None, []
+
+    parts = [p.strip() for p in _PDB_SPLIT.split(s) if p.strip()]
+    ids = []
+    for p in parts:
+        p = p.strip()
+        # sometimes entries include chain like "1ABC:A" or "1ABC_A"
+        p = re.split(r"[:_]", p)[0].strip()
+        if _PDB_ID.match(p):
+            ids.append(p.lower())
+
+    ids = sorted(set(ids))
+    return (ids[0] if ids else None), ids
+
+# --- Mutation parsing ---
+# Accept common patterns:
+#   A123V
+#   p.Ala123Val (rare)
+#   123A>V (rare)
+_MUT_A123V = re.compile(r"^(?P<wt>[ACDEFGHIKLMNPQRSTVWY])(?P<pos>\d+)(?P<mut>[ACDEFGHIKLMNPQRSTVWY])$")
+_MUT_123A_GT_V = re.compile(r"^(?P<pos>\d+)(?P<wt>[ACDEFGHIKLMNPQRSTVWY])>(?P<mut>[ACDEFGHIKLMNPQRSTVWY])$")
+
+
+def parse_substitution(s: str) -> Tuple[Optional[str], Optional[int], Optional[str], Optional[str]]:
+    """
+    Returns (wt_residue, position, mut_residue, normalized_mutation_string)
+    """
+    if not isinstance(s, str) or not s.strip():
+        return None, None, None, None
+    s = s.strip()
+
+    m = _MUT_A123V.match(s)
+    if m:
+        wt = m.group("wt")
+        pos = int(m.group("pos"))
+        mut = m.group("mut")
+        return wt, pos, mut, f"{wt}{pos}{mut}"
+
+    m = _MUT_123A_GT_V.match(s)
+    if m:
+        pos = int(m.group("pos"))
+        wt = m.group("wt")
+        mut = m.group("mut")
+        return wt, pos, mut, f"{wt}{pos}{mut}"
+
+    # If it's something else (multi-mutation, insertion/deletion notation, etc.),
+    # keep it in "mutation_raw" but do not parse.
+    return None, None, None, None
+
+
+# --- Categorical normalization ---
+def norm_str(x: object) -> Optional[str]:
+    if not isinstance(x, str):
+        return None
+    x = x.strip()
+    return x if x else None
+
+
+BUFFER_MAP: Dict[str, str] = {
+    "sodium tetraborate": "Sodium tetraborate",
+    "tetra-borate": "Sodium tetraborate",
+    "tetraborate": "Sodium tetraborate",
+    "sodium phosphate": "Sodium phosphate",
+}
+
+
+METHOD_MAP: Dict[str, str] = {
+    "dsc": "DSC",
+    "cd": "CD",
+}
+
+
+MEASURE_MAP: Dict[str, str] = {
+    "thermal": "Thermal",
+}
+
+
+def normalize_categoricals(df: pd.DataFrame) -> pd.DataFrame:
+    def map_lower(series: pd.Series, mapping: Dict[str, str]) -> pd.Series:
+        s = series.astype("string")
+        s_lower = s.str.lower().str.strip()
+        return s_lower.map(mapping).fillna(s.str.strip())
+
+    if "BUFFER" in df.columns:
+        df["buffer_norm"] = map_lower(df["BUFFER"], BUFFER_MAP)
+    else:
+        df["buffer_norm"] = pd.NA
+
+    if "METHOD" in df.columns:
+        df["method_norm"] = map_lower(df["METHOD"], METHOD_MAP)
+    else:
+        df["method_norm"] = pd.NA
+
+    if "MEASURE" in df.columns:
+        df["measure_norm"] = map_lower(df["MEASURE"], MEASURE_MAP)
+    else:
+        df["measure_norm"] = pd.NA
+
+    return df
+
+
+# --- Numeric cleanup ---
+def to_float(x: object) -> Optional[float]:
+    if x is None or (isinstance(x, float) and math.isnan(x)):
+        return None
+    if isinstance(x, (int, float)):
+        return float(x)
+    if isinstance(x, str):
+        s = x.strip()
+        if not s:
+            return None
+        # Handle "1mM" vs "1 mM" etc. for numeric fields by stripping units if present.
+        # For now: attempt raw float parse.
+        try:
+            return float(s)
+        except ValueError:
+            # try to extract first float substring
+            m = re.search(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?", s)
+            if m:
+                try:
+                    return float(m.group(0))
+                except ValueError:
+                    return None
+    return None
+
+
+def clean_numeric_columns(df: pd.DataFrame) -> pd.DataFrame:
+    # ddg-like
+    for col in ["DDG", "DOMAINOME_DDG", "DG", "DH", "DHVH"]:
+        if col in df.columns:
+            df[col.lower()] = df[col].map(to_float)
+        else:
+            df[col.lower()] = pd.NA
+
+    # temperature-like
+    for col in ["TM", "DTM", "EXP_TEMPERATURE"]:
+        if col in df.columns:
+            df[col.lower()] = df[col].map(to_float)
+        else:
+            df[col.lower()] = pd.NA
+
+    # pH
+    if "PH" in df.columns:
+        df["ph"] = df["PH"].map(to_float)
+    else:
+        df["ph"] = pd.NA
+
+    return df
+
+
+def derive_labels(df: pd.DataFrame) -> pd.DataFrame:
+    # Stabilizing classification: prefer explicit STABILIZING column if present,
+    # else use ddg sign if ddg available.
+    if "STABILIZING" in df.columns:
+        s = df["STABILIZING"].astype("string").str.lower().str.strip()
+        df["stabilizing_explicit"] = s.map({"yes": True, "no": False})
+    else:
+        df["stabilizing_explicit"] = pd.NA
+
+    # ddg-based label (common convention: ddg < 0 stabilizing)
+    df["stabilizing_ddg"] = df["ddg"].apply(lambda v: True if isinstance(v, float) and v < 0 else (False if isinstance(v, float) and v > 0 else pd.NA))
+
+    # unified label: explicit if available else ddg-based
+    df["stabilizing"] = df["stabilizing_explicit"]
+    df.loc[df["stabilizing"].isna(), "stabilizing"] = df.loc[df["stabilizing"].isna(), "stabilizing_ddg"]
+
+    return df
+
+
+def select_and_rename(df: pd.DataFrame) -> pd.DataFrame:
+    # canonical columns (keep more if you want)
+    keep = {
+        "EXPERIMENT_ID": "experiment_id",
+        "SEQUENCE_ID": "sequence_id",
+        "MUTANT_ID": "mutant_id",
+        "SOURCE_SEQUENCE_ID": "source_sequence_id",
+        "TARGET_SEQUENCE_ID": "target_sequence_id",
+        "SEQUENCE_LENGTH": "sequence_length",
+        "SUBSTITUTION": "substitution_raw",
+        "INSERTION": "insertion_raw",
+        "DELETION": "deletion_raw",
+        "PROTEIN": "protein_name",
+        "ORGANISM": "organism",
+        "UNIPROTKB": "uniprotkb",
+        "EC_NUMBER": "ec_number",
+        "INTERPRO": "interpro",
+        "PUBLICATION_PMID": "pmid",
+        "PUBLICATION_DOI": "doi",
+        "PUBLICATION_YEAR": "publication_year",
+        "SOURCE_DATASET": "source_dataset",
+        "REFERENCING_DATASET": "referencing_dataset",
+        "WWPDB": "wwpdb_raw",
+    }
+
+    out = pd.DataFrame()
+    for src, dst in keep.items():
+        out[dst] = df[src] if src in df.columns else pd.NA
+
+    # numeric & normalized categorical fields added earlier
+    extra_cols = [
+        "ddg", "domainome_ddg", "dg", "dh", "dhvh",
+        "tm", "dtm", "exp_temperature",
+        "ph",
+        "buffer_norm", "method_norm", "measure_norm",
+        "stabilizing",
+    ]
+    for c in extra_cols:
+        out[c] = df[c] if c in df.columns else pd.NA
+
+    # keep raw text fields that matter for conditions (optional)
+    for src, dst in [("BUFFER", "buffer_raw"), ("BUFFER_CONC", "buffer_conc_raw"), ("ION", "ion_raw"), ("ION_CONC", "ion_conc_raw"), ("STATE", "state")]:
+        out[dst] = df[src] if src in df.columns else pd.NA
+    out["pdb_id"] = df["pdb_id"] if "pdb_id" in df.columns else pd.NA
+    out["pdb_ids"] = df["pdb_ids"] if "pdb_ids" in df.columns else [[] for _ in range(len(df))]
+    return out
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--input", required=True, help="Path to raw FireProtDB 2.0 CSV")
+    ap.add_argument("--output", required=True, help="Path to output .parquet or .csv")
+    ap.add_argument("--min_seq_len", type=int, default=1, help="Drop sequences shorter than this")
+    ap.add_argument("--drop_no_label", action="store_true", help="Drop rows with neither ddg nor dtm")
+    args = ap.parse_args()
+
+    # Load as strings to avoid pandas guessing mixed types
+    df = pd.read_csv(args.input, dtype="string", keep_default_na=False, na_values=["", "NA", "NaN", "nan"])
+    df = df.replace({"": pd.NA})
+
+    # Basic trimming
+    for c in df.columns:
+        if pd.api.types.is_string_dtype(df[c]):
+            df[c] = df[c].astype("string").str.strip()
+
+    # Normalize & parse
+    df = normalize_categoricals(df)
+    df = clean_numeric_columns(df)
+
+    # Parse substitution into structured columns
+    parsed = df["SUBSTITUTION"].apply(lambda x: parse_substitution(x) if "SUBSTITUTION" in df.columns else (None, None, None, None))
+    df["wt_residue"] = parsed.map(lambda t: t[0])
+    df["position"] = parsed.map(lambda t: t[1]).astype("Int64")
+    df["mut_residue"] = parsed.map(lambda t: t[2])
+    df["mutation"] = parsed.map(lambda t: t[3])
+
+    df = derive_labels(df)
+
+    if "WWPDB" in df.columns:
+        parsed_pdb = df["WWPDB"].astype("string").fillna("").apply(lambda v: parse_pdb_ids(str(v)))
+        df["pdb_id"] = parsed_pdb.map(lambda t: t[0])
+        df["pdb_ids"] = parsed_pdb.map(lambda t: t[1])
+    else:
+        df["pdb_id"] = pd.NA
+        df["pdb_ids"] = [[] for _ in range(len(df))]
+
+    # Filter
+    if "SEQUENCE_LENGTH" in df.columns:
+        seq_len = df["SEQUENCE_LENGTH"].map(to_float)
+        df["sequence_length_num"] = seq_len
+        df = df[df["sequence_length_num"].fillna(0) >= args.min_seq_len]
+
+    if args.drop_no_label:
+        df = df[~(df["ddg"].isna() & df["dtm"].isna())]
+
+    # Select final schema
+    out = select_and_rename(df)
+
+    # Add parsed mutation columns
+    out["wt_residue"] = df["wt_residue"]
+    out["position"] = df["position"]
+    out["mut_residue"] = df["mut_residue"]
+    out["mutation"] = df["mutation"]
+
+    # De-dupe obvious duplicates (same experiment id)
+    if "experiment_id" in out.columns:
+        out = out.drop_duplicates(subset=["experiment_id"])
+
+    # Write
+    if args.output.lower().endswith(".parquet"):
+        out.to_parquet(args.output, index=False)
+    elif args.output.lower().endswith(".csv"):
+        out.to_csv(args.output, index=False)
+    else:
+        raise ValueError("Output must end with .parquet or .csv")
+
+    print(f"Wrote {len(out):,} rows to {args.output}")
+
+
+if __name__ == "__main__":
+    main()

src/02_add_uniprot_sequences.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+"""
+Add UniProt sequences to FireProtDB canonical parquet.
+
+Input:  data/fireprotdb_cleaned.parquet
+Output: data/fireprotdb_with_sequences.parquet
+Cache:  data/uniprot_cache.tsv  (acc \t sequence)
+
+Features:
+- Caches sequences locally (resume-friendly)
+- Uses UniProt REST FASTA endpoint for single accessions
+- Verifies sequence length against FireProtDB SEQUENCE_LENGTH
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import time
+from typing import Dict, Optional
+
+import pandas as pd
+import requests
+
+UNIPROT_FASTA = "https://rest.uniprot.org/uniprotkb/{acc}.fasta"
+
+def read_cache(path: str) -> Dict[str, str]:
+    if not os.path.exists(path):
+        return {}
+    cache = {}
+    with open(path, "r") as f:
+        for line in f:
+            line = line.rstrip("\n")
+            if not line:
+                continue
+            acc, seq = line.split("\t", 1)
+            cache[acc] = seq
+    return cache
+
+def append_cache(path: str, acc: str, seq: str) -> None:
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    with open(path, "a") as f:
+        f.write(f"{acc}\t{seq}\n")
+
+def parse_fasta(text: str) -> Optional[str]:
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    if not lines or not lines[0].startswith(">"):
+        return None
+    return "".join(lines[1:]).strip() or None
+
+def fetch_uniprot_fasta(acc: str, session: requests.Session, timeout: int = 20) -> Optional[str]:
+    url = UNIPROT_FASTA.format(acc=acc)
+    r = session.get(url, timeout=timeout)
+    if r.status_code != 200:
+        return None
+    return parse_fasta(r.text)
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--input", default="../data/fireprotdb_cleaned.parquet")
+    ap.add_argument("--output", default="../data/fireprotdb_with_sequences.parquet")
+    ap.add_argument("--cache", default="../data/cache/uniprot_cache.tsv")
+    ap.add_argument("--sleep", type=float, default=0.05, help="politeness delay between requests")
+    ap.add_argument("--limit", type=int, default=0, help="debug: only fetch first N missing")
+    args = ap.parse_args()
+
+    df = pd.read_parquet(args.input)
+
+    # Normalize accession column
+    if "uniprotkb" not in df.columns:
+        raise ValueError("Expected column 'uniprotkb' in input parquet")
+
+    accs = df["uniprotkb"].astype("string").fillna("").str.strip()
+    unique_accs = sorted({a for a in accs.unique().tolist() if a})
+
+    cache = read_cache(args.cache)
+
+    missing = [a for a in unique_accs if a not in cache]
+    if args.limit and args.limit > 0:
+        missing = missing[: args.limit]
+
+    print(f"Unique accessions: {len(unique_accs):,}")
+    print(f"Cached: {len(cache):,}")
+    print(f"Missing to fetch: {len(missing):,}")
+
+    session = requests.Session()
+    fetched = 0
+
+    for i, acc in enumerate(missing, 1):
+        seq = fetch_uniprot_fasta(acc, session=session)
+        if seq:
+            cache[acc] = seq
+            append_cache(args.cache, acc, seq)
+            fetched += 1
+        # brief delay (avoid hammering API)
+        time.sleep(args.sleep)
+
+        if i % 250 == 0:
+            print(f"Fetched {fetched:,}/{i:,} missing...")
+
+    # Add sequences
+    df["sequence"] = df["uniprotkb"].astype("string").fillna("").str.strip().map(lambda a: cache.get(a, None))
+
+    # Compute lengths as plain numeric (avoid pd.NA boolean ambiguity)
+    df["sequence_len_uniprot"] = df["sequence"].map(lambda s: len(s) if isinstance(s, str) else None)
+
+    fp_len = pd.to_numeric(df.get("sequence_length", pd.NA), errors="coerce")
+    up_len = pd.to_numeric(df["sequence_len_uniprot"], errors="coerce")
+
+    df["sequence_length_num"] = fp_len
+    df["length_match"] = (up_len == fp_len) & up_len.notna() & fp_len.notna()
+    
+    match_rate = float(df["length_match"].fillna(False).mean()) if len(df) else 0.0
+
+    print(f"Rows with sequence: {df['sequence'].notna().sum():,}/{len(df):,}")
+    print(f"Length match rate (rows): {match_rate:.3f}")
+
+    df.to_parquet(args.output, index=False)
+    print(f"Wrote: {args.output}")
+
+if __name__ == "__main__":
+    main()

src/03_export_fasta.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+import argparse
+import pandas as pd
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--input", default="../data/fireprotdb_with_sequences.parquet")
+    ap.add_argument("--output_fasta", default="../data/proteins.fasta")
+    ap.add_argument("--only_length_match", action="store_true")
+    args = ap.parse_args()
+
+    df = pd.read_parquet(args.input)
+
+    # keep only rows with sequence
+    df = df[df["sequence"].notna()].copy()
+    if args.only_length_match and "length_match" in df.columns:
+        df = df[df["length_match"] == True].copy()
+
+    # one sequence per uniprotkb; fallback to sequence_id if needed
+    df["uniprotkb"] = df["uniprotkb"].astype("string").fillna("").str.strip()
+    df["sequence_id"] = df["sequence_id"].astype("string").fillna("").str.strip()
+
+    # prefer uniprotkb as id, else sequence_id
+    df["protein_id"] = df["uniprotkb"]
+    df.loc[df["protein_id"] == "", "protein_id"] = "seqid:" + df.loc[df["protein_id"] == "", "sequence_id"]
+    df.loc[df["protein_id"] == "seqid:", "protein_id"] = "unknown"
+
+    # dedupe by protein_id (keep first)
+    prot = df.drop_duplicates(subset=["protein_id"])[["protein_id", "sequence"]]
+
+    with open(args.output_fasta, "w") as f:
+        for _, r in prot.iterrows():
+            pid = r["protein_id"]
+            seq = r["sequence"]
+            if not isinstance(seq, str) or not seq:
+                continue
+            f.write(f">{pid}\n{seq}\n")
+
+    print(f"Wrote {len(prot):,} proteins to {args.output_fasta}")
+
+if __name__ == "__main__":
+    main()

src/04_mmseqs_cluster.sh

@@ -0,0 +1 @@
+mmseqs easy-cluster ../data/proteins.fasta ../data/mmseqs_clusters ../data/mmseqs_tmp --min-seq-id 0.3 -c 0.8

src/05_assign_cluster_splits.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+"""
+Assign cluster-based splits to FireProtDB rows.
+
+Reads MMseqs2 cluster TSV and assigns a split per cluster, then joins back to all rows.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import pandas as pd
+
+def stable_hash(s: str) -> int:
+    h = hashlib.sha256(s.encode("utf-8")).hexdigest()
+    return int(h[:8], 16)
+
+def split_from_cluster(cluster_id: str, ratios=(0.8, 0.1, 0.1)) -> str:
+    r = stable_hash(cluster_id) / 0xFFFFFFFF
+    if r < ratios[0]:
+        return "train"
+    if r < ratios[0] + ratios[1]:
+        return "validation"
+    return "test"
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--input", default="../data/fireprotdb_with_sequences.parquet")
+    ap.add_argument("--clusters_tsv", default="../data/mmseqs_clusters_cluster.tsv",
+                    help="MMseqs2 cluster output TSV (representative\\tmember)")
+    ap.add_argument("--output", default="../data/fireprotdb_with_cluster_splits.parquet")
+    ap.add_argument("--ratios", default="0.8,0.1,0.1")
+    args = ap.parse_args()
+
+    ratios = tuple(float(x) for x in args.ratios.split(","))
+
+    df = pd.read_parquet(args.input)
+
+    # Load MMseqs2 TSV: representative \t member
+    cl = pd.read_csv(args.clusters_tsv, sep="\t", header=None, names=["rep", "member"], dtype="string")
+    cl["rep"] = cl["rep"].astype("string").fillna("").str.strip()
+    cl["member"] = cl["member"].astype("string").fillna("").str.strip()
+
+    # Use rep as cluster id
+    cl["cluster_id"] = cl["rep"]
+    member_to_cluster = cl.set_index("member")["cluster_id"].to_dict()
+
+    # Build protein_id same way as FASTA export
+    df["uniprotkb"] = df["uniprotkb"].astype("string").fillna("").str.strip()
+    df["sequence_id"] = df["sequence_id"].astype("string").fillna("").str.strip()
+
+    df["protein_id"] = df["uniprotkb"]
+    df.loc[df["protein_id"] == "", "protein_id"] = "seqid:" + df.loc[df["protein_id"] == "", "sequence_id"]
+    df.loc[df["protein_id"] == "seqid:", "protein_id"] = "unknown"
+
+    df["cluster_id"] = df["protein_id"].map(lambda pid: member_to_cluster.get(pid, None))
+
+    # If a protein didn't get clustered (missing sequence etc.), treat it as its own cluster
+    df["cluster_id"] = df["cluster_id"].fillna(df["protein_id"].map(lambda x: f"singleton:{x}"))
+
+    df["split"] = df["cluster_id"].map(lambda cid: split_from_cluster(str(cid), ratios=ratios))
+
+    df.to_parquet(args.output, index=False)
+    print(f"Wrote: {args.output}")
+    print(df["split"].value_counts(dropna=False))
+
+if __name__ == "__main__":
+    main()

src/06_gen_subsets.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import os
+import argparse
+import pandas as pd
+
+
+OUT_SPLITS = ['train', 'validation', 'test']
+
+def ensure_dir(p: str):
+    os.makedirs(p, exist_ok=True)
+
+def write_subset(out_dir: str, name: str, df: pd.DataFrame):
+    outp = os.path.join(out_dir, name)
+    ensure_dir(outp)
+    for split in OUT_SPLITS:
+        fp = os.path.join(outp, f"{split}.parquet")
+        d_split = df[df["split"] == split].copy()
+        d_split.to_parquet(fp, index=False)
+        print(f"{name}/{split}: {len(d_split):,} -> {fp}")
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--input", default="../data/fireprotdb_with_cluster_splits.parquet",
+                    help="Parquet with MMseqs2 cluster_id and split columns already assigned")
+    ap.add_argument("--out_dir", default="../data/subsets",
+                    help="Output dir for HF subset parquets")
+    ap.add_argument("--drop_columns", default="",
+                    help="Comma-separated columns to drop in outputs (optional)")
+    ap.add_argument("--require_sequence", action="store_true",
+                    help="If set, drop rows without a UniProt sequence before creating subsets")
+    ap.add_argument("--strict_split_check", action="store_true",
+                    help="If set, error if any protein/cluster appears in multiple splits")
+    args = ap.parse_args()
+
+    df = pd.read_parquet(args.input)
+
+    # Basic required columns
+    if "split" not in df.columns:
+        raise ValueError("Input parquet must contain a 'split' column (train/validation/test).")
+    if "mutation" not in df.columns:
+        raise ValueError("Input parquet must contain a 'mutation' column.")
+
+    # Normalize split values
+    df["split"] = df["split"].astype("string").str.strip().str.lower()
+    bad = sorted(set(df["split"].dropna().unique()) - set(OUT_SPLITS))
+    if bad:
+        raise ValueError(f"Unexpected split values: {bad}. Expected {OUT_SPLITS}.")
+
+    if args.require_sequence and "sequence" in df.columns:
+        before = len(df)
+        df = df[df["sequence"].notna()].copy()
+        print(f"Dropped rows without sequence: {before - len(df):,}")
+
+    # Optional leakage check: ensure each cluster/protein lives in exactly one split
+    # Prefer cluster_id if available, else fall back to uniprotkb/sequence_id
+    if args.strict_split_check:
+        if "cluster_id" in df.columns:
+            key = "cluster_id"
+        elif "uniprotkb" in df.columns:
+            key = "uniprotkb"
+        else:
+            key = "sequence_id"
+
+        counts = df.groupby(key, dropna=False)["split"].nunique()
+        leaked = counts[counts > 1]
+        if len(leaked):
+            example = leaked.head(10)
+            raise ValueError(
+                f"Leakage detected: {len(leaked):,} {key}s appear in multiple splits.\n"
+                f"Examples:\n{example}"
+            )
+        print(f"Split leakage check passed using key={key}.")
+
+    # Base filter: parsed mutation only (matches your fireprotdb.py intent)
+    has_mut = df["mutation"].notna()
+    df_mut = df[has_mut].copy()
+
+    # Build subsets (you said you only want these)
+    ddg = df_mut[df_mut["ddg"].notna()].copy()
+    dtm = df_mut[df_mut["dtm"].notna()].copy()
+    binary = df_mut[df_mut["stabilizing"].notna()].copy()
+
+    # Optional: drop columns to slim outputs
+    if args.drop_columns.strip():
+        drop_cols = [c.strip() for c in args.drop_columns.split(",") if c.strip()]
+        keep_drop = [c for c in drop_cols if c in df.columns]
+        if keep_drop:
+            ddg = ddg.drop(columns=keep_drop, errors="ignore")
+            dtm = dtm.drop(columns=keep_drop, errors="ignore")
+            binary = binary.drop(columns=keep_drop, errors="ignore")
+
+    write_subset(args.out_dir, "mutation_ddg", ddg)
+    write_subset(args.out_dir, "mutation_dtm", dtm)
+    write_subset(args.out_dir, "mutation_binary", binary)
+
+if __name__ == "__main__":
+    main()