scripts/phase1_freeze_and_classical_features.py.py

# -*- coding: utf-8 -*-
"""Untitled17.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1GwdSjrwh3f6QCzOa8KHr_XkWy0KmZvdV
"""

import pandas as pd
import numpy as np
from pathlib import Path
from tqdm import tqdm
from datetime import datetime
import hashlib
import json


PATHS = {
    'data_processed': BASE_PATH / 'data' / 'processed',
    'data_frozen': BASE_PATH / 'data' / 'frozen',
    'features': BASE_PATH / 'features',
}

for path in PATHS.values():
    path.mkdir(parents=True, exist_ok=True)

AA_PROPERTIES = {
    'A': {'hydro': 1.8, 'charge': 0, 'volume': 88.6, 'disorder': 0.06, 'aromatic': 0},
    'R': {'hydro': -4.5, 'charge': 1, 'volume': 173.4, 'disorder': 0.18, 'aromatic': 0},
    'N': {'hydro': -3.5, 'charge': 0, 'volume': 114.1, 'disorder': 0.14, 'aromatic': 0},
    'D': {'hydro': -3.5, 'charge': -1, 'volume': 111.1, 'disorder': 0.19, 'aromatic': 0},
    'C': {'hydro': 2.5, 'charge': 0, 'volume': 108.5, 'disorder': -0.02, 'aromatic': 0},
    'Q': {'hydro': -3.5, 'charge': 0, 'volume': 143.8, 'disorder': 0.16, 'aromatic': 0},
    'E': {'hydro': -3.5, 'charge': -1, 'volume': 138.4, 'disorder': 0.20, 'aromatic': 0},
    'G': {'hydro': -0.4, 'charge': 0, 'volume': 60.1, 'disorder': 0.17, 'aromatic': 0},
    'H': {'hydro': -3.2, 'charge': 0.5, 'volume': 153.2, 'disorder': 0.10, 'aromatic': 1},
    'I': {'hydro': 4.5, 'charge': 0, 'volume': 166.7, 'disorder': -0.49, 'aromatic': 0},
    'L': {'hydro': 3.8, 'charge': 0, 'volume': 166.7, 'disorder': -0.37, 'aromatic': 0},
    'K': {'hydro': -3.9, 'charge': 1, 'volume': 168.6, 'disorder': 0.21, 'aromatic': 0},
    'M': {'hydro': 1.9, 'charge': 0, 'volume': 162.9, 'disorder': -0.23, 'aromatic': 0},
    'F': {'hydro': 2.8, 'charge': 0, 'volume': 189.9, 'disorder': -0.41, 'aromatic': 1},
    'P': {'hydro': -1.6, 'charge': 0, 'volume': 112.7, 'disorder': 0.41, 'aromatic': 0},
    'S': {'hydro': -0.8, 'charge': 0, 'volume': 89.0, 'disorder': 0.13, 'aromatic': 0},
    'T': {'hydro': -0.7, 'charge': 0, 'volume': 116.1, 'disorder': 0.04, 'aromatic': 0},
    'W': {'hydro': -0.9, 'charge': 0, 'volume': 227.8, 'disorder': -0.35, 'aromatic': 1},
    'Y': {'hydro': -1.3, 'charge': 0, 'volume': 193.6, 'disorder': -0.26, 'aromatic': 1},
    'V': {'hydro': 4.2, 'charge': 0, 'volume': 140.0, 'disorder': -0.38, 'aromatic': 0},
}



df_full = pd.read_parquet(PATHS['data_processed'] / 'mutations_dataset_final.parquet')
print(f"   Dataset complet: {len(df_full):,} mutations")

mito_strict_file = PATHS['data_processed'] / 'mutations_dataset_mito_strict.parquet'
if mito_strict_file.exists():
    df_strict = pd.read_parquet(mito_strict_file)
else:
    STRICT_MITO_GENES = {
        'OPA1', 'MFN1', 'MFN2', 'DNM1L', 'AFG3L2', 'SPG7', 'LONP1', 'CLPP', 'YME1L1',
        'NDUFAF1', 'NDUFAF2', 'NDUFAF3', 'NDUFAF4', 'NDUFAF5', 'NDUFAF6', 'NDUFAF7',
        'NUBPL', 'ACAD9', 'TIMMDC1', 'FOXRED1',
        'NDUFS1', 'NDUFS2', 'NDUFS3', 'NDUFS4', 'NDUFS6', 'NDUFS7', 'NDUFS8',
        'NDUFV1', 'NDUFV2', 'NDUFA1', 'NDUFA2', 'NDUFA9', 'NDUFA10', 'NDUFA11', 'NDUFA12', 'NDUFA13',
        'SDHA', 'SDHB', 'SDHC', 'SDHD', 'SDHAF1', 'SDHAF2',
        'BCS1L', 'TTC19', 'UQCRB', 'UQCRQ', 'UQCRC2', 'CYC1',
        'SURF1', 'SCO1', 'SCO2', 'COX10', 'COX14', 'COX15', 'COX20',
        'COA5', 'COA6', 'COA7', 'PET100', 'COX4I1', 'COX6A1', 'COX6B1', 'COX7B', 'COX8A',
        'ATP5F1A', 'ATP5F1D', 'ATP5F1E', 'TMEM70', 'ATPAF2',
        'TIMM50', 'TIMM8A', 'DNAJC19', 'AGK', 'TOMM20', 'TOMM40',
        'CHCHD2', 'CHCHD10', 'CHCHD4', 'AIFM1', 'COX17',
        'HSPA9', 'HSPD1', 'HSPE1', 'CLPB',
        'AARS2', 'DARS2', 'EARS2', 'FARS2', 'HARS2', 'IARS2', 'LARS2', 'MARS2',
        'NARS2', 'RARS2', 'SARS2', 'TARS2', 'VARS2', 'YARS2',
        'GFM1', 'TSFM', 'TUFM', 'C12orf65', 'RMND1', 'GTPBP3', 'MTO1', 'TRMU',
        'POLG', 'POLG2', 'TWNK', 'TFAM', 'RRM2B', 'MPV17', 'DGUOK', 'TK2',
        'SUCLA2', 'SUCLG1', 'FBXL4',
        'PDHA1', 'PDHB', 'PDHX', 'DLD', 'DLAT',
        'PC', 'PCCA', 'PCCB', 'MUT', 'MMAA', 'MMAB', 'MMACHC',
        'LIAS', 'LIPT1', 'BOLA3', 'NFU1', 'ISCA1', 'ISCA2', 'IBA57', 'GLRX5', 'FDXR',
        'COQ2', 'COQ4', 'COQ6', 'COQ7', 'COQ8A', 'COQ9', 'PDSS1', 'PDSS2',
        'SLC25A4', 'SLC25A3', 'SLC25A12', 'SLC25A13', 'SLC25A19', 'SLC25A22',
        'TAZ', 'SERAC1', 'LRPPRC', 'TACO1', 'ELAC2', 'TRNT1', 'PNPT1',
    }
    df_strict = df_full[df_full['gene_symbol'].isin(STRICT_MITO_GENES)].copy()


def compute_hash(df):
    """Calculer un hash du dataset pour vérification d'intégrité"""
    content = df.to_json()
    return hashlib.md5(content.encode()).hexdigest()

freeze_metadata = {
    'freeze_date': datetime.now().isoformat(),
    'freeze_version': '1.0',
    'datasets': {
        'full': {
            'filename': 'mutations_dataset_final_FROZEN.parquet',
            'n_mutations': len(df_full),
            'n_pathogenic': int((df_full['label'] == 1).sum()),
            'n_benign': int((df_full['label'] == 0).sum()),
            'n_genes': int(df_full['gene_symbol'].nunique()),
            'hash': compute_hash(df_full),
        },
        'mito_strict': {
            'filename': 'mutations_dataset_mito_strict_FROZEN.parquet',
            'n_mutations': len(df_strict),
            'n_pathogenic': int((df_strict['label'] == 1).sum()),
            'n_benign': int((df_strict['label'] == 0).sum()),
            'n_genes': int(df_strict['gene_symbol'].nunique()),
            'hash': compute_hash(df_strict),
        }
    },
    'note': 'FROZEN - DO NOT MODIFY LABELS AFTER THIS POINT'
}

df_full.to_parquet(PATHS['data_frozen'] / 'mutations_dataset_final_FROZEN.parquet')
df_strict.to_parquet(PATHS['data_frozen'] / 'mutations_dataset_mito_strict_FROZEN.parquet')

uniprot_file = PATHS['data_processed'].parent / 'raw' / 'uniprot_human_reviewed.parquet'

if uniprot_file.exists():
    df_uniprot = pd.read_parquet(uniprot_file)
    seq_dict = dict(zip(df_uniprot['accession'], df_uniprot['sequence']))
else:
    import gzip
    uniprot_gz = Path("")
    with gzip.open(uniprot_gz, 'rt') as f:
        df_uniprot = pd.read_csv(f, sep='\t', low_memory=False)
    seq_dict = dict(zip(df_uniprot['Entry'], df_uniprot['Sequence']))



def extract_classical_features(row, seq_dict, window=15):
    """
    Extraire les features classiques IDP pour une mutation.

    Features extraites (~45):
    - Propriétés de substitution (delta)
    - Contexte local (fenêtre ±window)
    - Position dans la protéine
    - Composition locale
    - Indicateurs biologiques
    """

    acc = row['uniprot_acc']
    pos = row['position']
    wt = row['wt_aa']
    mut = row['mut_aa']

    seq = seq_dict.get(acc, '')

    features = {}

    if not seq or pos >= len(seq):
        return None

    wt_props = AA_PROPERTIES.get(wt, {})
    mut_props = AA_PROPERTIES.get(mut, {})

    features['delta_hydrophobicity'] = mut_props.get('hydro', 0) - wt_props.get('hydro', 0)
    features['delta_charge'] = mut_props.get('charge', 0) - wt_props.get('charge', 0)
    features['delta_volume'] = mut_props.get('volume', 0) - wt_props.get('volume', 0)
    features['delta_disorder_propensity'] = mut_props.get('disorder', 0) - wt_props.get('disorder', 0)
    features['delta_aromatic'] = mut_props.get('aromatic', 0) - wt_props.get('aromatic', 0)

    features['abs_delta_hydro'] = abs(features['delta_hydrophobicity'])
    features['abs_delta_charge'] = abs(features['delta_charge'])
    features['abs_delta_volume'] = abs(features['delta_volume'])


    start = max(0, pos - window)
    end = min(len(seq), pos + window + 1)
    local_seq = seq[start:end]

    if len(local_seq) > 0:
        features['local_hydro_mean'] = np.mean([AA_PROPERTIES.get(aa, {}).get('hydro', 0) for aa in local_seq])
        features['local_charge_mean'] = np.mean([AA_PROPERTIES.get(aa, {}).get('charge', 0) for aa in local_seq])
        features['local_disorder_mean'] = np.mean([AA_PROPERTIES.get(aa, {}).get('disorder', 0) for aa in local_seq])

        features['local_charged_fraction'] = sum(1 for aa in local_seq if aa in 'RDEHK') / len(local_seq)
        features['local_aromatic_fraction'] = sum(1 for aa in local_seq if aa in 'FWY') / len(local_seq)
        features['local_proline_fraction'] = local_seq.count('P') / len(local_seq)
        features['local_glycine_fraction'] = local_seq.count('G') / len(local_seq)
        features['local_cysteine_fraction'] = local_seq.count('C') / len(local_seq)

        disorder_promoting = set('AEGRQSKP')
        order_promoting = set('WFYILMVC')
        features['local_disorder_promoting'] = sum(1 for aa in local_seq if aa in disorder_promoting) / len(local_seq)
        features['local_order_promoting'] = sum(1 for aa in local_seq if aa in order_promoting) / len(local_seq)
    else:
        for key in ['local_hydro_mean', 'local_charge_mean', 'local_disorder_mean',
                    'local_charged_fraction', 'local_aromatic_fraction', 'local_proline_fraction',
                    'local_glycine_fraction', 'local_cysteine_fraction',
                    'local_disorder_promoting', 'local_order_promoting']:
            features[key] = 0


    prot_len = len(seq)

    features['position_absolute'] = pos
    features['position_normalized'] = pos / prot_len if prot_len > 0 else 0
    features['protein_length'] = prot_len

    features['is_n_terminal'] = 1 if pos < 50 else 0
    features['is_c_terminal'] = 1 if pos > prot_len - 50 else 0
    features['distance_to_n_term'] = pos
    features['distance_to_c_term'] = prot_len - pos - 1



    features['protein_cysteine_count'] = seq.count('C')
    features['protein_cysteine_fraction'] = seq.count('C') / prot_len if prot_len > 0 else 0
    features['protein_charged_fraction'] = sum(1 for aa in seq if aa in 'RDEHK') / prot_len if prot_len > 0 else 0
    features['protein_disorder_mean'] = np.mean([AA_PROPERTIES.get(aa, {}).get('disorder', 0) for aa in seq])

    features['cysteine_gained'] = 1 if mut == 'C' else 0
    features['cysteine_lost'] = 1 if wt == 'C' else 0
    features['cysteine_change'] = features['cysteine_gained'] - features['cysteine_lost']

    features['nearby_cysteine_count'] = local_seq.count('C') - (1 if wt == 'C' else 0)
    features['cysteine_in_cys_rich_region'] = 1 if features['local_cysteine_fraction'] > 0.05 else 0


    features['charge_introducing'] = 1 if wt_props.get('charge', 0) == 0 and mut_props.get('charge', 0) != 0 else 0
    features['charge_removing'] = 1 if wt_props.get('charge', 0) != 0 and mut_props.get('charge', 0) == 0 else 0
    features['charge_reversing'] = 1 if wt_props.get('charge', 0) * mut_props.get('charge', 0) < 0 else 0



    features['proline_introduced'] = 1 if mut == 'P' and wt != 'P' else 0
    features['proline_removed'] = 1 if wt == 'P' and mut != 'P' else 0
    features['glycine_introduced'] = 1 if mut == 'G' and wt != 'G' else 0
    features['glycine_removed'] = 1 if wt == 'G' and mut != 'G' else 0



    features['idp_disruption_score'] = (
        abs(features['delta_disorder_propensity']) * 2 +
        abs(features['delta_charge']) * 1.5 +
        features['proline_introduced'] * 2 +
        features['proline_removed'] * 1
    )

    features['ros_vulnerability_score'] = (
        features['cysteine_lost'] * 3 +
        features['cysteine_gained'] * 1 +
        features['cysteine_in_cys_rich_region'] * 2 +
        (1 if features['protein_cysteine_fraction'] > 0.03 else 0) * 1
    )

    features['import_disruption_score'] = (
        features['is_n_terminal'] * 2 +
        features['charge_reversing'] * (2 if pos < 50 else 0) +
        abs(features['delta_hydrophobicity']) * (1 if pos < 30 else 0)
    )

    return features


features_list = []
failed = 0

for idx, row in tqdm(df_full.iterrows(), total=len(df_full), desc="Features"):
    feats = extract_classical_features(row, seq_dict)

    if feats:
        feats['mutation_idx'] = idx
        feats['uniprot_acc'] = row['uniprot_acc']
        feats['gene_symbol'] = row['gene_symbol']
        feats['position'] = row['position']
        feats['wt_aa'] = row['wt_aa']
        feats['mut_aa'] = row['mut_aa']
        feats['label'] = row['label']
        features_list.append(feats)
    else:
        failed += 1

df_features_full = pd.DataFrame(features_list)

print(f"\n    Features extraites: {len(df_features_full):,}")
print(f"    Échecs: {failed}")


features_list_strict = []

for idx, row in tqdm(df_strict.iterrows(), total=len(df_strict), desc="Features strict"):
    feats = extract_classical_features(row, seq_dict)

    if feats:
        feats['mutation_idx'] = idx
        feats['uniprot_acc'] = row['uniprot_acc']
        feats['gene_symbol'] = row['gene_symbol']
        feats['position'] = row['position']
        feats['wt_aa'] = row['wt_aa']
        feats['mut_aa'] = row['mut_aa']
        feats['label'] = row['label']
        features_list_strict.append(feats)

df_features_strict = pd.DataFrame(features_list_strict)


df_features_full.to_parquet(PATHS['features'] / 'features_classical_full.parquet')
df_features_strict.to_parquet(PATHS['features'] / 'features_classical_mito_strict.parquet')

feature_cols = [c for c in df_features_full.columns if c not in
                ['mutation_idx', 'uniprot_acc', 'gene_symbol', 'position', 'wt_aa', 'mut_aa', 'label']]