Antibody Humanizer

Overview

Bioinformatics platform for converting murine antibodies into humanized variants by grafting complementarity-determining regions (CDRs) onto human framework templates while preserving antigen-binding affinity and reducing immunogenicity risk.

Key Capabilities:

• CDR Identification: Automatic CDR boundary detection (Kabat/Chothia/IMGT schemes)
• Framework Matching: Database search for optimal human germline templates
• Humanization Scoring: Multi-parameter immunogenicity risk assessment
• Back-Mutation Prediction: Identify critical framework residues for retention
• Batch Processing: Humanize multiple antibody candidates efficiently
• Immunogenicity Assessment: T-cell epitope and humanness scoring

When to Use

✅ Use this skill when:

• Converting murine hybridoma antibodies to therapeutic candidates
• Reducing immunogenicity risk of rodent-derived antibodies
• Selecting human framework templates for CDR grafting
• Identifying critical framework residues for antigen binding
• Comparing multiple humanization strategies for lead optimization
• Preparing antibody sequences for patent filings
• Teaching antibody engineering principles

❌ Do NOT use when:

• Fully human antibody generation from phage display → Use phage-display-library
• De novo antibody design → Use antibody-design-ai
• Affinity maturation → Use affinity-maturation-predictor
• ADCC/CDC optimization → Use fc-engineering-toolkit
• Final therapeutic candidate selection → Requires experimental validation

Integration:

• Upstream: antibody-sequencer (VH/VL sequence determination), cdr-grafting-validator (structural assessment)
• Downstream: protein-struct-viz (3D visualization), immunogenicity-predictor (T-cell epitope analysis)

Core Capabilities

1. CDR Region Identification

Parse antibody sequences and identify CDR boundaries:

python
from scripts.humanizer import AntibodyHumanizer

humanizer = AntibodyHumanizer()

# Analyze antibody sequence
analysis = humanizer.analyze_sequence(
    vh_sequence="QVQLQQSGPELVKPGASVKISCKASGYTFTDYYMHWVKQSHGKSLEWIGYINPSTGYTEYNQKFKDKATLTVDKSSSTAYMQLSSLTSEDSAVYYCAR...",
    vl_sequence="DIQMTQSPSSLSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLIYKASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSSYPYT...",
    scheme="chothia"  # Options: kabat, chothia, imgt
)

# Output CDR locations
print(analysis.cdr_regions)
# {
#   "VH_CDR1": {"start": 26, "end": 32, "seq": "GYTFTDY"},
#   "VH_CDR2": {"start": 52, "end": 58, "seq": "INPSTGY"},
#   ...
# }

Numbering Schemes: | Scheme | VH CDR1 | VH CDR2 | VH CDR3 | Best For | |--------|---------|---------|---------|----------| | Chothia | 26-32 | 52-56 | 95-102 | Structural analysis | | Kabat | 31-35 | 50-65 | 95-102 | Sequence-based work | | IMGT | 27-38 | 56-65 | 105-117 | Standardized analysis |

2. Human Framework Matching

Identify optimal human germline templates:

python
# Match against human germline database
matches = humanizer.find_human_frameworks(
    vh_framework=analysis.vh_frameworks,
    vl_framework=analysis.vl_frameworks,
    top_n=5,
    criteria=["homology", "canonical_structure", "vernier_similarity"]
)

# Evaluate each candidate
for match in matches:
    print(f"Template: {match.germline_genes}")
    print(f"Homology: {match.homology:.2%}")
    print(f"Vernier Score: {match.vernier_score:.1f}")
    print(f"Risk Level: {match.immunogenicity_risk}")

Matching Criteria:

• Sequence Homology: Percent identity to human germline
• Canonical Structure: Loop conformation compatibility
• Vernier Region: Framework residues contacting CDRs
• Interface Residues: Packing interactions with CDRs

3. Humanization Scoring

Assess immunogenicity risk of candidates:

python
# Score humanization candidates
scores = humanizer.score_candidates(
    murine_antibody=analysis,
    human_templates=matches,
    scoring_methods=["t20", "h_score", "germline_deviation", "paratope_diversity"]
)

# Rank by overall score
ranked = scores.rank_by_composite_score(
    weights={"humanness": 0.4, "binding_retention": 0.4, "developability": 0.2}
)

Scoring Methods: | Method | Description | Target | |--------|-------------|--------| | T20 Score | 20-mer peptide humanization | >80% human | | H-Score | Hummerblind germline distance | <15 mutations | | Paratope Diversity | CDR germline gene diversity | Low diversity | | Developability | Aggregation/pH stability prediction | High score |

4. Back-Mutation Prediction

Identify critical residues to retain from murine framework:

python
# Predict back-mutations
back_mutations = humanizer.predict_back_mutations(
    murine_vh=analysis.vh_sequence,
    human_vh=matches[0].human_template,
    cdr_regions=analysis.cdr_regions,
    rationale_required=True
)

# Output shows position-specific recommendations
for mutation in back_mutations:
    print(f"Position {mutation.position}: {mutation.human_aa} → {mutation.murine_aa}")
    print(f"Rationale: {mutation.reason}")  # e.g., "Vernier region contact"
    print(f"Priority: {mutation.priority}")  # Critical/Important/Optional

Critical Residue Classes:

• Vernier Positions: Framework residues contacting CDRs (VH 24, 71, 94)
• Interface Packs: Residue packing between VH and VL
• Canonical Anchors: Cysteines and conserved framework positions
• Buried Positions: Core packing residues affecting stability

Common Patterns

Pattern 1: Standard Therapeutic Humanization

Scenario: Convert murine anti-tumor antibody to therapeutic candidate.

bash
# Humanize single antibody
python scripts/main.py \
  --vh "QVQLQQSGPELVKPGASVKISCKAS..." \
  --vl "DIQMTQSPSSLSASVGDRVTITCRAS..." \
  --name "Anti-HER2-Murine-1" \
  --scheme chothia \
  --top-n 3 \
  --output humanization_report.json

# Review top candidates
cat humanization_report.json | jq '.candidates[0]'

Workflow:

1. Input murine VH/VL sequences
2. Identify CDRs using Chothia scheme
3. Match to human germline database
4. Score top 3 candidates
5. Identify required back-mutations
6. Output humanized sequences

Pattern 2: Batch Humanization Screening

Scenario: Screen multiple murine clones from hybridoma campaign.

python
# Process multiple antibodies
antibodies = [
    {"name": "Clone-A", "vh": "...", "vl": "..."},
    {"name": "Clone-B", "vh": "...", "vl": "..."},
    {"name": "Clone-C", "vh": "...", "vl": "..."}
]

results = humanizer.batch_humanize(
    antibodies=antibodies,
    ranking_criteria="composite_score",
    min_humanness=0.85
)

# Rank by developability
ranked = results.rank_by(criteria=["humanness", "binding_retention", "stability"])

Selection Criteria:

• Highest humanness score (>85%)
• Fewest back-mutations required (<6)
• Low immunogenicity risk
• Good developability profile

Pattern 3: Framework Template Comparison

Scenario: Compare different humanization strategies for lead candidate.

python
# Test multiple framework combinations
strategies = [
    {"vh": "IGHV1-2*02", "vl": "IGKV1-12*01", "name": "Template-A"},
    {"vh": "IGHV3-23*01", "vl": "IGKV3-20*01", "name": "Template-B"},
    {"vh": "IGHV4-34*01", "vl": "IGKV1-5*01", "name": "Template-C"}
]

comparison = humanizer.compare_strategies(
    murine_antibody=analysis,
    strategies=strategies,
    metrics=["homology", "back_mutations", "immunogenicity", "paratope_structure"]
)

comparison.generate_report("framework_comparison.pdf")

Comparison Metrics:

• Sequence identity to human germline
• Number and location of back-mutations
• Predicted immunogenicity risk
• CDR conformation preservation

Pattern 4: Intellectual Property Analysis

Scenario: Assess humanization for patent landscape analysis.

bash
# Generate humanized variants
python scripts/main.py \
  --input murine_lead.json \
  --generate-variants 10 \
  --include-back-mutations \
  --output variants_for_ip.json

# Check novelty against patent databases
python scripts/patent_check.py \
  --sequences variants_for_ip.json \
  --databases [USPTO, EPO, WIPO] \
  --output novelty_report.pdf

IP Considerations:

• Human framework combinations may be patented
• CDR sequences determine antigen specificity
• Back-mutation positions may be prior art
• Document humanization rationale for filings

Complete Workflow Example

From murine hybridoma to therapeutic candidate:

bash
# Step 1: Sequence analysis and CDR identification
python scripts/main.py \
  --vh $VH_SEQUENCE \
  --vl $VL_SEQUENCE \
  --scheme chothia \
  --output step1_analysis.json

# Step 2: Find best human frameworks
python scripts/main.py \
  --input step1_analysis.json \
  --find-frameworks \
  --top-n 5 \
  --output step2_frameworks.json

# Step 3: Score and rank candidates
python scripts/main.py \
  --input step2_frameworks.json \
  --score-candidates \
  --include-immunogenicity \
  --output step3_scored.json

# Step 4: Predict back-mutations
python scripts/main.py \
  --input step3_scored.json \
  --predict-back-mutations \
  --rationale \
  --output step4_backmutations.json

# Step 5: Generate final humanized sequences
python scripts/main.py \
  --input step4_backmutations.json \
  --generate-sequences \
  --format fasta \
  --output humanized_antibody.fasta

Python API:

python
from scripts.humanizer import AntibodyHumanizer
from scripts.scoring import HumanizationScorer
from scripts.backmutation import BackMutationPredictor

# Initialize pipeline
humanizer = AntibodyHumanizer()
scorer = HumanizationScorer()
bm_predictor = BackMutationPredictor()

# Step 1: Parse and analyze
antibody = humanizer.analyze_sequence(
    vh_sequence=murine_vh,
    vl_sequence=murine_vl,
    scheme="chothia"
)

# Step 2: Find human frameworks
candidates = humanizer.find_human_frameworks(
    antibody,
    top_n=5
)

# Step 3: Score candidates
for candidate in candidates:
    scores = scorer.calculate_scores(
        murine=antibody,
        humanized=candidate
    )
    candidate.composite_score = scores.weighted_score()

# Step 4: Select best and predict back-mutations
best = max(candidates, key=lambda x: x.composite_score)
back_mutations = bm_predictor.predict(
    murine=antibody,
    human_template=best
)

# Step 5: Generate final sequence
final_sequence = humanizer.generate_humanized_sequence(
    template=best,
    back_mutations=back_mutations,
    cdrs=antibody.cdr_regions
)

print(f"Humanized antibody generated:")
print(f"- Humanness: {best.humanness:.1%}")
print(f"- Back-mutations: {len(back_mutations)}")
print(f"- Risk level: {best.immunogenicity_risk}")

Quality Checklist

Input Quality:

• ] VH and VL sequences complete (110-130 aa typical)
• ] No ambiguous residues (B, Z, X)
• ] Signal peptide removed
• ] Constant region removed (variable region only)

Humanization Assessment:

• ] CDR boundaries correctly identified
• ] Human framework homology >80%
• ] T20 score >75 (high humanness)
• ] Vernier positions analyzed for back-mutations
• ] Interface residues checked for packing

Output Validation:

• ] Humanized sequence valid (no stop codons)
• ] CDRs preserved exactly
• ] Framework length conserved
• ] Back-mutations documented with rationale
• ] CRITICAL: Immunogenicity risk assessed

Before Experimental Work:

• ] CRITICAL: Top 2-3 candidates selected for expression
• ] Binding affinity to be tested (ELISA/Biacore)
• ] Stability assessed (thermal/aggregation)
• ] Immunogenicity in vitro assays planned

Common Pitfalls

Sequence Issues:

• ❌ Incomplete sequences → Missing framework regions
• ✅ Ensure full VH/VL variable domains provided

• ❌ Wrong numbering scheme → CDR boundaries incorrect
• ✅ Verify scheme matches experimental data source

• ❌ Non-standard residues → Unusual amino acids
• ✅ Clean sequences; remove signal peptides

Design Issues:

• ❌ Over-humanization → Losing antigen binding
• ✅ Don't exceed 85-90% humanness; retain critical residues

• ❌ Ignoring back-mutations → Assuming 100% human framework works
• ✅ Always predict and test back-mutations

• ❌ Single candidate only → No backup options
• ✅ Generate 2-3 candidates with different frameworks

Experimental Issues:

• ❌ Skipping binding validation → Assuming in silico = in vivo
• ✅ Always confirm antigen binding experimentally

• ❌ Ignoring developability → Aggregation or instability
• ✅ Check for problematic residues (unpaired cysteines, hydrophobic patches)

References

Available in references/ directory:

• imgt_germline_database.md - Human germline gene reference sequences
• cdr_numbering_schemes.md - Kabat, Chothia, IMGT comparison
• humanization_case_studies.md - Successful therapeutic examples
• vernier_positions_guide.md - Critical framework residues
• immunogenicity_assessment.md - T-cell epitope prediction methods
• patent_landscape.md - Humanization IP considerations

Scripts

Located in scripts/ directory:

• main.py - CLI interface for humanization
• humanizer.py - Core humanization engine
• cdr_parser.py - CDR identification and numbering
• framework_matcher.py - Human germline database search
• scoring.py - Humanization quality assessment
• backmutation.py - Critical residue prediction
• batch_processor.py - Multiple antibody screening
• structure_predictor.py - CDR conformation analysis

Limitations

• Binding Prediction: Cannot accurately predict impact on antigen affinity
• Developability: Limited prediction of aggregation or stability issues
• Immunogenicity: In silico T-cell epitope prediction has false positives
• Non-Standard Antibodies: May not handle camelid, shark, or engineered scaffolds
• Experimental Validation Required: All predictions must be confirmed in vitro/vivo
• Intellectual Property: Does not check for existing patent claims on sequences

Parameters

| Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | --vh | string | - | No | Murine VH sequence (amino acids) | | --vl | string | - | No | Murine VL sequence (amino acids) | | --input, -i | string | - | No | Input JSON file path | | --name, -n | string | "" | No | Antibody name | | --output, -o | string | - | No | Output file path | | --format, -f | string | json | No | Output format (json, fasta, csv) | | --scheme, -s | string | chothia | No | Numbering scheme (kabat, chothia, imgt) | | --top-n | int | 3 | No | Number of best candidates to return |

Usage

Basic Usage

bash
# Humanize with direct sequence input
python scripts/main.py --vh "QVQLQQSGPELVKPGASVKMSCKAS..." --vl "DIQMTQSPSSLSASVGDRVTITC..." --name "MyAntibody"

# Use JSON input file
python scripts/main.py --input antibody.json --output results.json

# Use IMGT numbering scheme
python scripts/main.py --vh "SEQUENCE" --vl "SEQUENCE" --scheme imgt

Input JSON Format

json
{
  "vh_sequence": "QVQLQQSGPELVKPGASVKMSCKAS...",
  "vl_sequence": "DIQMTQSPSSLSASVGDRVTITC...",
  "name": "MyAntibody",
  "scheme": "chothia"
}

Risk Assessment

| Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python script executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Low | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output may contain proprietary sequences | Medium |

Security Checklist

• x] No hardcoded credentials or API keys
• x] No unauthorized file system access (../)
• x] Input validation for sequences
• x] Prompt injection protections in place
• x] Error messages sanitized
• x] Output directory restricted to workspace
• x] Script execution in sandboxed environment

Prerequisites

bash
# Python 3.7+
# No external packages required (uses standard library)

Evaluation Criteria

Success Metrics

• x] Successfully parses antibody sequences
• x] Identifies CDR regions correctly
• x] Matches human germline frameworks
• x] Predicts back-mutations
• x] Generates valid humanized sequences

Test Cases

1. Basic Functionality: Humanize valid VH/VL sequences → Returns candidates
2. Edge Case: Invalid sequence characters → Graceful error message
3. File Input: Process JSON input → Correctly parses and outputs

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
• Add T20 score database integration
• Support for camelid and shark antibodies
• Structure-based CDR prediction

🔬 Critical Note: Computational humanization is a design tool, not a substitute for experimental validation. Always express and test humanized candidates for binding affinity, specificity, stability, and immunogenicity before therapeutic development.