Quantum annealer-based molecular docking methods can be integrated with experimental evolution data to predict how antibiotic resistance mutations alter protein-ligand binding geometries.

Quantum annealer-based molecular docking (QA-MD) algorithms, when trained or calibrated using experimental evolution datasets containing characterized antibiotic resistance mutations, will predict protein-ligand binding geometries (RMSD, binding pose, key contact residues) for mutant proteins with statistically superior accuracy compared to classical docking methods (AutoDock Vina, Glide SP) for at least 3 distinct antibiotic-target systems, achieving mean RMSD ≤ 2.0 Å versus crystallographic or cryo-EM ground truth structures, with a minimum 15% improvement in binding pose prediction accuracy over classical baselines.

Minimum Viable Test (MVT): Focus on three well-characterized antibiotic-resistance systems: (1) E. coli DHFR + trimethoprim (TMP), (2) S. aureus PBP2a + beta-lactams, (3) M. tuberculosis InhA + isoniazid. Use existing DMS (deep mutational scanning) datasets for each. Encode docking problem as QUBO on D-Wave Advantage. Compare predicted binding geometries against PDB crystal structures of mutant-drug complexes. Primary metric: RMSD of predicted vs. experimental binding pose. Secondary metric: Pearson correlation of predicted ΔΔG with experimental MIC fold-change. Run 3 independent replicates per system. Statistical test: Wilcoxon signed-rank test vs. AutoDock Vina baseline, α = 0.05.

# Quantum Annealer Molecular Docking + Evolution Integration Pipeline
# Architecture: Hybrid Classical-Quantum Pipeline

## MODULE 1: Data Preparation
class ProteinLigandSystem:
    def __init__(self, pdb_id, mutation_list, dms_dataset):
        self.structure = load_pdb(pdb_id)  # BioPython
        self.mutations = mutation_list      # [(pos, WT_aa, mut_aa), ...]
        self.dms_data = dms_dataset         # {mutation: MIC_fold_change}
    
    def prepare_structure(self, mutation):
        # Apply point mutation using PyRosetta
        pose = rosetta.pose_from_pdb(self.structure)
        mutant_pose = apply_point_mutation(pose, mutation)
        minimize_energy(mutant_pose, scorefxn='ref2015')
        return mutant_pose
    
    def get_evolution_prior(self, mutation):
        # Convert MIC fold-change to energy prior
        mic_fc = self.dms_data.get(mutation, 1.0)
        # Higher MIC = weaker binding = positive energy penalty
        return 0.5 * np.log2(mic_fc)  # kcal/mol units

## MODULE 2: QUBO Formulation
class DockingQUBO:
    def __init__(self, protein_pose, ligand_mol, grid_spacing=0.5, angle_step=15):
        self.protein = protein_pose
        self.ligand = ligand_mol
        self.grid = generate_binding_site_grid(protein_pose, spacing=grid_spacing)
        self.angle_bins = np.arange(0, 360, angle_step)
        
    def encode_ligand_pose(self):
        # Binary encoding: one-hot for translation + torsion angles
        n_grid = len(self.grid)           # ~1000 grid points
        n_torsions = self.ligand.n_rotatable_bonds
        n_angles = len(self.angle_bins)   # 24 bins
        
        # Total qubits: n_grid * 3 (xyz) + n_torsions * n_angles
        # For typical ligand: ~3000 + 15*24 = ~3360 qubits
        self.n_qubits = n_grid + n_torsions * n_angles
        return self.n_qubits
    
    def compute_energy_matrix(self, evolution_prior=0.0):
        # Q matrix for QUBO: Q[i,j] = interaction energy
        Q = np.zeros((self.n_qubits, self.n_qubits))
        
        # Diagonal: single-variable energies (LJ + electrostatics)
        for i, pose_var in enumerate(self.pose_variables):
            Q[i,i] = self.compute_vdw_energy(pose_var) + \
                     self.compute_electrostatic_energy(pose_var) + \
                     evolution_prior  # Evolution data integration
        
        # Off-diagonal: pairwise constraints (one-hot enforcement)
        for group in self.one_hot_groups:
            for i, j in combinations(group, 2):
                Q[i,j] += 10.0  # Penalty for selecting >1 in group
        
        return Q
    
    def add_evolution_constraint(self, evolution_prior):
        # Modify Q matrix based on experimental evolution data
        # Mutations with high MIC get increased binding energy penalties
        binding_site_residues = self.get_binding_site_residues(cutoff=4.0)
        for residue_idx in binding_site_residues:
            qubit_indices = self.residue_to_qubit_map[residue_idx]
            for qi in qubit_indices:
                self.Q[qi, qi] += evolution_prior * self.contact_weight[residue_idx]
        return self.Q

## MODULE 3: Quantum Annealing Execution
class QuantumDockingSolver:
    def __init__(self, sampler='hybrid'):
        if sampler == 'hybrid':
            from dwave.system import LeapHybridCQMSampler
            self.sampler = LeapHybridCQMSampler()
        else:
            from dwave.system import DWaveSampler, EmbeddingComposite
            self.sampler = EmbeddingComposite(DWaveSampler())
    
    def solve(self, Q_matrix, n_reads=1000, annealing_time=100):
        bqm = dimod.BinaryQuadraticModel.from_qubo(Q_matrix)
        
        if self.sampler_type == 'hybrid':
            response = self.sampler.sample(bqm, time_limit=60)
        else:
            response = self.sampler.sample(bqm, 
                                           num_reads=n_reads,
                                           annealing_time=annealing_time,
                                           chain_strength=self.auto_chain_strength(Q_matrix))
        
        # Extract best solution
        best_sample = response.first.sample
        chain_break_fraction = response.first.chain_break_fraction
        
        assert chain_break_fraction < 0.05, f"Chain breaks too high: {chain_break_fraction}"
        return self.decode_pose(best_sample), response.first.energy
    
    def decode_pose(self, binary_sample):
        # Convert binary solution back to 3D coordinates
        translation = self.decode_translation(binary_sample)
        rotations = self.decode_rotations(binary_sample)
        torsions = self.decode_torsions(binary_sample)
        return LigandPose(translation, rotations, torsions)

## MODULE 4: Validation and Comparison
class ValidationPipeline:
    def compute_rmsd(self, predicted_pose, experimental_pose):
        # Heavy-atom RMSD after optimal superposition
        return rdMolAlign.CalcRMS(predicted_pose.mol, experimental_pose.mol)
    
    def run_classical_baseline(self, protein_pdbqt, ligand_pdbqt):
        # AutoDock Vina
        result = subprocess.run(['vina', '--receptor', protein_pdbqt,
                                '--ligand', ligand_pdbqt,
                                '--exhaustiveness', '32',
                                '--num_modes', '10'], capture_output=True)
        return parse_vina_output(result.stdout)
    
    def statistical_comparison(self, qa_rmsds, classical_rmsds):
        stat, p_value = scipy.stats.wilcoxon(qa_rmsds, classical_rmsds)
        effect_size = cohens_d(qa_rmsds, classical_rmsds)
        return {'p_value': p_value, 'effect_size': effect_size,
                'qa_mean': np.mean(qa_rmsds), 'classical_mean': np.mean(classical_rmsds)}

## MODULE 5: Main Pipeline
def run_evp(systems, n_mutants_per_system=50):
    results = {}
    for system in systems:  # ['DHFR', 'PBP2a', 'InhA']
        pls = ProteinLigandSystem(system.pdb_id, system.mutations, system.dms_data)
        qa_rmsds, classical_rmsds = [], []
        
        for mutation in system.mutations[:n_mutants_per_system]:
            # Prepare mutant structure
            mutant_pose = pls.prepare_structure(mutation)
            evolution_prior = pls.get_evolution_prior(mutation)
            
            # QA docking with evolution integration
            qubo = DockingQUBO(mutant_pose, system.ligand)
            Q = qubo.compute_energy_matrix(evolution_prior)
            solver = QuantumDockingSolver(sampler='hybrid')
            qa_pose, qa_energy = solver.solve(Q)
            
            # Classical baseline
            validator = ValidationPipeline()
            classical_pose = validator.run_classical_baseline(mutant_pose, system.ligand)
            
            # Compute RMSD vs experimental
            exp_pose = load_experimental_pose(system, mutation)
            qa_rmsds.append(validator.compute_rmsd(qa_pose, exp_pose))
            classical_rmsds.append(validator.compute_rmsd(classical_pose, exp_pose))
        
        results[system.name] = validator.statistical_comparison(qa_rmsds, classical_rmsds)
        
        # Abort checkpoint
        if results[system.name]['p_value'] > 0.1 and system.name == 'DHFR':
            print("ABORT: First system shows no signal. Reassess QUBO formulation.")
            break
    
    return results