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---

Phase 1: Input Analysis & Characterization
├── Sequence annotation (CDRs, framework)
├── Species identification
├── Target antigen identification
├── Clinical precedent search
└── OUTPUT: Input characterization
    ↓
Phase 2: Humanization Strategy
├── Germline gene alignment (IMGT)
├── Framework selection
├── CDR grafting design
├── Backmutation identification
└── OUTPUT: Humanization plan
    ↓
Phase 3: Structure Modeling & Analysis
├── AlphaFold prediction
├── CDR conformation analysis
├── Epitope mapping
├── Interface analysis
└── OUTPUT: Structural assessment
    ↓
Phase 4: Affinity Optimization
├── In silico mutation screening
├── CDR optimization strategies
├── Interface improvement
└── OUTPUT: Affinity variants
    ↓
Phase 5: Developability Assessment
├── Aggregation propensity
├── PTM site identification
├── Stability prediction
├── Expression prediction
└── OUTPUT: Developability score
    ↓
Phase 6: Immunogenicity Prediction
├── MHC-II epitope prediction (IEDB)
├── T-cell epitope risk
├── Aggregation-related immunogenicity
└── OUTPUT: Immunogenicity risk score
    ↓
Phase 7: Manufacturing Feasibility
├── Expression level prediction
├── Purification considerations
├── Formulation stability
└── OUTPUT: Manufacturing assessment
    ↓
Phase 8: Final Report & Recommendations
├── Ranked variant list
├── Experimental validation plan
├── Next steps
└── OUTPUT: Comprehensive report

def annotate_antibody_sequence(sequence):
    """Annotate antibody sequence with CDRs and framework regions."""

    # Use IMGT numbering scheme (standard for antibodies)
    # CDR definitions (IMGT):
    # CDR-H1: 27-38, CDR-H2: 56-65, CDR-H3: 105-117
    # CDR-L1: 27-38, CDR-L2: 56-65, CDR-L3: 105-117

    annotation = {
        'sequence': sequence,
        'length': len(sequence),
        'regions': {
            'FR1': sequence[0:26],
            'CDR1': sequence[26:38],
            'FR2': sequence[38:55],
            'CDR2': sequence[55:65],
            'FR3': sequence[65:104],
            'CDR3': sequence[104:117],
            'FR4': sequence[117:]
        }
    }

    return annotation

def identify_germline(tu, vh_sequence, vl_sequence):
    """Identify germline genes for VH and VL chains using IMGT."""

    # Search for human germline genes
    vh_germlines = tu.tools.IMGT_search_genes(
        gene_type="IGHV",
        species="Homo sapiens"
    )

    vl_germlines = tu.tools.IMGT_search_genes(
        gene_type="IGKV",  # or IGLV for lambda
        species="Homo sapiens"
    )

    # Get sequences for top matches
    # Calculate identity % for each germline
    # Return closest matches

    return {
        'vh_germline': 'IGHV1-69*01',
        'vh_identity': 87.2,
        'vl_germline': 'IGKV1-39*01',
        'vl_identity': 89.5
    }

def search_clinical_precedents(tu, target_antigen):
    """Find approved/clinical antibodies against same target."""

    # Search Thera-SAbDab for clinical antibodies
    therapeutics = tu.tools.TheraSAbDab_search_by_target(
        target=target_antigen
    )

    approved = [ab for ab in therapeutics if ab['phase'] == 'Approved']
    clinical = [ab for ab in therapeutics if 'Phase' in ab['phase']]

    return {
        'approved_count': len(approved),
        'clinical_count': len(clinical),
        'examples': approved[:3],
        'insights': extract_design_patterns(approved)
    }

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def select_human_framework(tu, mouse_sequence, cdr_sequences):
    """Select optimal human framework for CDR grafting."""

    # Search IMGT for human germline genes
    vh_genes = tu.tools.IMGT_search_genes(
        gene_type="IGHV",
        species="Homo sapiens"
    )

    # For each candidate framework:
    # 1. Calculate sequence identity to mouse FR
    # 2. Check CDR canonical class compatibility
    # 3. Assess structural compatibility
    # 4. Consider clinical precedents

    candidates = []
    for gene in vh_genes[:20]:  # Top 20 human germlines
        gene_seq = tu.tools.IMGT_get_sequence(
            accession=gene['accession'],
            format='fasta'
        )

        score = calculate_framework_score(
            mouse_fr=extract_framework(mouse_sequence),
            human_fr=extract_framework(gene_seq),
            cdr_compatibility=check_cdr_compatibility(cdr_sequences, gene_seq)
        )

        candidates.append({
            'germline': gene['name'],
            'identity': score['identity'],
            'cdr_compatibility': score['cdr_compatibility'],
            'clinical_use': count_clinical_uses(gene['name']),
            'overall_score': score['total']
        })

    # Sort by overall score
    return sorted(candidates, key=lambda x: x['overall_score'], reverse=True)

def design_cdr_grafting(mouse_sequence, human_framework, cdr_sequences):
    """Design CDR grafting with backmutation identification."""

    # Graft mouse CDRs onto human framework
    grafted_sequence = graft_cdrs(
        human_framework=human_framework,
        mouse_cdrs=cdr_sequences
    )

    # Identify Vernier zone residues (affect CDR conformation)
    vernier_residues = [2, 27, 28, 29, 30, 47, 48, 67, 69, 71, 78, 93, 94]

    # Identify potential backmutations
    backmutations = []
    for pos in vernier_residues:
        if mouse_sequence[pos] != human_framework[pos]:
            backmutations.append({
                'position': pos,
                'human_aa': human_framework[pos],
                'mouse_aa': mouse_sequence[pos],
                'reason': 'Vernier zone - may affect CDR conformation',
                'priority': 'High' if pos in [27, 29, 30, 48] else 'Medium'
            })

    return {
        'grafted_sequence': grafted_sequence,
        'backmutations': backmutations,
        'humanness_score': calculate_humanness(grafted_sequence)
    }

def calculate_humanization_score(sequence, human_germline):
    """Calculate comprehensive humanization score."""

    # Framework humanness (% identity to human germline)
    fr_identity = calculate_framework_identity(sequence, human_germline)

    # T-cell epitope content (lower is better)
    tcell_epitope_count = predict_tcell_epitopes(sequence)

    # Unusual residues in human context
    unusual_residues = count_unusual_residues(sequence)

    # Aggregation hotspots
    aggregation_motifs = find_aggregation_motifs(sequence)

    score = {
        'framework_humanness': fr_identity,  # 0-100%
        'cdr_preservation': 100,  # Always 100% initially
        'tcell_epitopes': tcell_epitope_count,
        'unusual_residues': unusual_residues,
        'aggregation_risk': len(aggregation_motifs),
        'overall_score': calculate_weighted_score(
            fr_identity, tcell_epitope_count, unusual_residues, aggregation_motifs
        )
    }

    return score

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>VH_Humanized_v1 | 87% human framework
EVQLVQSGAEVKKPGASVKVSCKASGYTFTSYYMHWVRQAPGQGLEWMGGIIPIFGTANY
AQKFQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARARDDGSYSPFDYWGQGTLVTVSS

>VH_Humanized_v2 | 85% human framework + backmutations
EVQLVQSGAEVKKPGASVKVSCKASGYAFTSYYMHWVRQAPGQGLEWMVGIIPIFGTANY
AQKFQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARARDDGSYSPFDYWGQGTLVTVSS

---

def predict_antibody_structure(tu, vh_sequence, vl_sequence):
    """Predict antibody Fv structure using AlphaFold."""

    # Combine VH and VL with linker
    fv_sequence = vh_sequence + ":" + vl_sequence  # AlphaFold uses : for chain separator

    # Predict structure
    prediction = tu.tools.AlphaFold_get_prediction(
        sequence=fv_sequence,
        return_format='pdb'
    )

    # Extract pLDDT scores
    plddt_scores = extract_plddt(prediction)

    # Analyze by region
    regions = {
        'VH_FR': np.mean([plddt_scores[i] for i in range(0, 26)]),
        'CDR_H1': np.mean([plddt_scores[i] for i in range(26, 38)]),
        'CDR_H2': np.mean([plddt_scores[i] for i in range(55, 65)]),
        'CDR_H3': np.mean([plddt_scores[i] for i in range(104, 117)]),
        'VL_FR': np.mean([plddt_scores[i] for i in range(len(vh_sequence), len(vh_sequence)+26)]),
        'CDR_L1': np.mean([plddt_scores[i] for i in range(len(vh_sequence)+26, len(vh_sequence)+38)]),
    }

    return {
        'structure': prediction,
        'mean_plddt': np.mean(plddt_scores),
        'regional_plddt': regions,
        'cdr_confidence': np.mean([regions['CDR_H1'], regions['CDR_H2'], regions['CDR_H3']])
    }

def analyze_cdr_conformation(structure):
    """Analyze CDR loop conformations and canonical classes."""

    # Extract CDR coordinates
    cdr_coords = extract_cdr_regions(structure)

    # Classify canonical structures
    cdr_classes = {
        'CDR-H1': classify_canonical_structure(cdr_coords['H1']),
        'CDR-H2': classify_canonical_structure(cdr_coords['H2']),
        'CDR-H3': 'Non-canonical (14 aa)',  # Usually unique
        'CDR-L1': classify_canonical_structure(cdr_coords['L1']),
        'CDR-L2': classify_canonical_structure(cdr_coords['L2']),
        'CDR-L3': classify_canonical_structure(cdr_coords['L3'])
    }

    # Calculate RMSD to known canonical structures
    rmsd_values = calculate_canonical_rmsd(cdr_coords, cdr_classes)

    return {
        'classes': cdr_classes,
        'rmsd': rmsd_values,
        'confidence': assess_conformation_confidence(rmsd_values)
    }

def map_epitope(tu, target_protein, antibody_structure):
    """Identify epitope on target protein."""

    # Get target structure or predict
    target_info = tu.tools.UniProt_get_protein_by_accession(
        accession=target_protein
    )

    # Search for known epitopes
    epitopes = tu.tools.iedb_search_epitopes(
        sequence_contains=target_protein,
        structure_type="Linear peptide",
        limit=20
    )

    # Search for structural antibody complexes
    sabdab_results = tu.tools.SAbDab_search_structures(
        query=target_info['protein_name']
    )

    # Analyze binding interface
    interface = {
        'epitope_candidates': epitopes,
        'structural_precedents': sabdab_results,
        'predicted_interface': predict_binding_interface(antibody_structure)
    }

    return interface

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def design_affinity_variants(antibody_structure, target_structure):
    """Design affinity maturation variants using computational screening."""

    # Identify interface residues
    interface_residues = identify_interface_residues(
        antibody_structure,
        target_structure,
        distance_cutoff=4.5  # Angstroms
    )

    # Focus on CDR residues
    cdr_interface = [res for res in interface_residues if is_cdr_residue(res)]

    # Design mutations for each position
    variants = []
    for position in cdr_interface:
        # Try all amino acids except original
        for aa in 'ACDEFGHIKLMNPQRSTVWY':
            if aa != antibody_structure.sequence[position]:
                predicted_ddg = predict_binding_energy_change(
                    structure=antibody_structure,
                    mutation=f"{antibody_structure.sequence[position]}{position}{aa}"
                )

                if predicted_ddg < -0.5:  # Favorable change (more negative = better)
                    variants.append({
                        'position': position,
                        'original': antibody_structure.sequence[position],
                        'mutant': aa,
                        'predicted_ddg': predicted_ddg,
                        'predicted_kd_fold': calculate_kd_change(predicted_ddg)
                    })

    # Rank by predicted improvement
    return sorted(variants, key=lambda x: x['predicted_ddg'])

def cdr_optimization_strategies(cdr_sequence, cdr_name):
    """Identify CDR optimization strategies based on sequence and structure."""

    strategies = []

    # Strategy 1: Extend CDR for increased contact area
    if len(cdr_sequence) < 12 and cdr_name == 'CDR-H3':
        strategies.append({
            'strategy': 'CDR-H3 extension',
            'rationale': 'Add 1-2 residues to increase contact surface',
            'expected_impact': '+2-5x affinity improvement',
            'examples': ['Extension with Gly-Tyr', 'Extension with Ser-Asp']
        })

    # Strategy 2: Tyrosine enrichment
    tyr_count = cdr_sequence.count('Y')
    if tyr_count < 2:
        strategies.append({
            'strategy': 'Tyrosine enrichment',
            'rationale': 'Tyr provides pi-stacking and H-bonds',
            'expected_impact': '+2-3x affinity improvement',
            'targets': suggest_tyr_positions(cdr_sequence)
        })

    # Strategy 3: Charged residue optimization
    if 'PD' in cdr_sequence or 'EP' in cdr_sequence:
        strategies.append({
            'strategy': 'Salt bridge formation',
            'rationale': 'Add charged residues for electrostatic interactions',
            'expected_impact': '+1-2x affinity and pH sensitivity',
            'targets': identify_salt_bridge_opportunities(cdr_sequence)
        })

    return strategies

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def assess_aggregation(sequence):
    """Comprehensive aggregation risk assessment."""

    # Identify aggregation-prone regions (APR)
    aprs = find_aggregation_motifs(sequence)

    # Hydrophobic patches on surface
    hydrophobic_patches = identify_surface_hydrophobic(sequence)

    # Charge patches (extreme pI regions)
    charge_patches = identify_charge_clusters(sequence)

    # Sequence-based prediction scores
    tango_score = predict_tango_score(sequence)  # Beta-aggregation
    aggrescan_score = predict_aggrescan(sequence)  # General aggregation

    # Isoelectric point
    pi = calculate_isoelectric_point(sequence)

    return {
        'apr_count': len(aprs),
        'apr_regions': aprs,
        'hydrophobic_patches': hydrophobic_patches,
        'charge_patches': charge_patches,
        'tango_score': tango_score,
        'aggrescan_score': aggrescan_score,
        'pi': pi,
        'overall_risk': categorize_risk(tango_score, aggrescan_score, len(aprs))
    }

def identify_ptm_sites(sequence):
    """Identify post-translational modification liability sites."""

    ptm_sites = {
        'deamidation': [],
        'isomerization': [],
        'oxidation': [],
        'glycosylation': []
    }

    # Deamidation: Asn followed by Gly or Ser (NG, NS motifs)
    for i, aa in enumerate(sequence[:-1]):
        if aa == 'N' and sequence[i+1] in ['G', 'S']:
            ptm_sites['deamidation'].append({
                'position': i,
                'motif': sequence[i:i+2],
                'risk': 'High' if sequence[i+1] == 'G' else 'Medium',
                'region': identify_region(i)
            })

    # Isomerization: Asp followed by Gly or Ser (DG, DS motifs)
    for i, aa in enumerate(sequence[:-1]):
        if aa == 'D' and sequence[i+1] in ['G', 'S']:
            ptm_sites['isomerization'].append({
                'position': i,
                'motif': sequence[i:i+2],
                'risk': 'High',
                'region': identify_region(i)
            })

    # Oxidation: Met and Trp residues
    for i, aa in enumerate(sequence):
        if aa in ['M', 'W']:
            ptm_sites['oxidation'].append({
                'position': i,
                'residue': aa,
                'risk': 'Medium',
                'region': identify_region(i)
            })

    # N-glycosylation: N-X-S/T motif (X != P)
    for i in range(len(sequence)-2):
        if sequence[i] == 'N' and sequence[i+1] != 'P' and sequence[i+2] in ['S', 'T']:
            ptm_sites['glycosylation'].append({
                'position': i,
                'motif': sequence[i:i+3],
                'region': identify_region(i)
            })

    return ptm_sites