"""BioQL Resistance Profiling - Mutation Analysis"""
from typing import List, Dict, Any, Tuple
from dataclasses import dataclass

@dataclass
class Mutation:
    """Protein mutation definition"""
    original_residue: str
    position: int
    mutant_residue: str
    frequency: float  # Population frequency (MAF)
    clinical_significance: str
    
    def __str__(self):
        return f"{self.original_residue}{self.position}{self.mutant_residue}"

# Common resistance mutations for drug targets
RESISTANCE_MUTATIONS = {
    'EGFR': [
        Mutation('T', 790, 'M', 0.5, 'Erlotinib/Gefitinib resistance'),
        Mutation('L', 858, 'R', 0.4, 'Activating mutation'),
        Mutation('E', 746, 'del', 0.3, 'Exon 19 deletion'),
    ],
    'BCR-ABL': [
        Mutation('T', 315, 'I', 0.4, 'Imatinib resistance (gatekeeper)'),
        Mutation('M', 351, 'T', 0.2, 'Nilotinib resistance'),
    ],
    'HIV-1 Protease': [
        Mutation('D', 30, 'N', 0.3, 'Nelfinavir resistance'),
        Mutation('I', 50, 'V', 0.25, 'Multi-drug resistance'),
        Mutation('V', 82, 'A', 0.35, 'Ritonavir resistance'),
    ],
    'GLP1R': [
        Mutation('E', 364, 'Q', 0.12, 'Reduced agonist response'),
        Mutation('R', 299, 'C', 0.05, 'Partial resistance'),
    ]
}

def model_mutation(pdb_path: str, mutation: Mutation) -> str:
    """Model protein mutation using homology modeling (placeholder)"""
    print(f"   Modeling mutation: {mutation}...")
    # Would use PyMOL, Modeller, or FoldX here
    mutant_pdb = f"{pdb_path.replace('.pdb', '')}_{mutation}.pdb"
    return mutant_pdb

def analyze_resistance_profile(smiles: str, target: str, pdb_id: str) -> Dict[str, Any]:
    """Analyze molecule resistance profile against known mutations"""
    print(f"🧬 Analyzing resistance profile for {target}...")
    
    mutations = RESISTANCE_MUTATIONS.get(target, [])
    if not mutations:
        return {
            'target': target,
            'mutations_tested': 0,
            'resistance_risk': 'Unknown',
            'message': f'No resistance mutations defined for {target}'
        }
    
    results = {
        'target': target,
        'pdb_id': pdb_id,
        'smiles': smiles,
        'mutations_tested': len(mutations),
        'wild_type_affinity': None,
        'mutant_affinities': [],
        'resistance_risk': 'Low',
        'vulnerable_residues': []
    }
    
    # Placeholder - would dock to each mutant
    for mut in mutations:
        results['mutant_affinities'].append({
            'mutation': str(mut),
            'frequency': mut.frequency,
            'affinity_change_fold': 1.2,  # Placeholder
            'retained_activity_percent': 83.0,  # Placeholder
            'clinical_significance': mut.clinical_significance
        })
    
    return results

def suggest_resistance_optimizations(resistance_results: Dict) -> List[str]:
    """Suggest structural modifications to reduce resistance risk"""
    suggestions = [
        "Target conserved residues (mutation rate < 1%)",
        "Add interactions with backbone atoms (invariant)",
        "Increase binding surface area (multi-point binding)",
        "Design for induced fit (accommodate mutations)",
    ]
    return suggestions
