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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 templatesHumanization Scoring : Multi-parameter immunogenicity risk assessmentBack-Mutation Prediction : Identify critical framework residues for retentionBatch Processing : Humanize multiple antibody candidates efficientlyImmunogenicity 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:
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"},
# ...
# }
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:
# 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}")
Sequence Homology : Percent identity to human germlineCanonical Structure : Loop conformation compatibilityVernier Region : Framework residues contacting CDRsInterface Residues : Packing interactions with CDRs
3. Humanization Scoring Assess immunogenicity risk of candidates:
# 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}
)
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:
# 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 VLCanonical 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.
# 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]'
Input murine VH/VL sequences
Identify CDRs using Chothia scheme
Match to human germline database
Score top 3 candidates
Identify required back-mutations
Output humanized sequences
Pattern 2: Batch Humanization Screening Scenario : Screen multiple murine clones from hybridoma campaign.
# 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"])
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.
# 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")
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.
# 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
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:
# 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
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 Before Experimental Work:
Common Pitfalls
❌ 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
❌ 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
References Available in references/ directory:
imgt_germline_database.md - Human germline gene reference sequencescdr_numbering_schemes.md - Kabat, Chothia, IMGT comparisonhumanization_case_studies.md - Successful therapeutic examplesvernier_positions_guide.md - Critical framework residuesimmunogenicity_assessment.md - T-cell epitope prediction methodspatent_landscape.md - Humanization IP considerations
Scripts Located in scripts/ directory:
main.py - CLI interface for humanizationhumanizer.py - Core humanization enginecdr_parser.py - CDR identification and numberingframework_matcher.py - Human germline database searchscoring.py - Humanization quality assessmentbackmutation.py - Critical residue predictionbatch_processor.py - Multiple antibody screeningstructure_predictor.py - CDR conformation analysis
Limitations
Binding Prediction : Cannot accurately predict impact on antigen affinityDevelopability : Limited prediction of aggregation or stability issuesImmunogenicity : In silico T-cell epitope prediction has false positivesNon-Standard Antibodies : May not handle camelid, shark, or engineered scaffoldsExperimental Validation Required : All predictions must be confirmed in vitro/vivoIntellectual Property : Does not check for existing patent claims on sequences
Parameters Parameter Type Default Required Description --vhstring - No Murine VH sequence (amino acids) --vlstring - No Murine VL sequence (amino acids) --input, -istring - No Input JSON file path --name, -nstring "" No Antibody name --output, -ostring - No Output file path --format, -fstring json No Output format (json, fasta, csv) --scheme, -sstring chothia No Numbering scheme (kabat, chothia, imgt) --top-nint 3 No Number of best candidates to return
Usage
Basic Usage # 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 {
"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
Prerequisites # Python 3.7+
# No external packages required (uses standard library)
Evaluation Criteria
Success Metrics
Test Cases
Basic Functionality : Humanize valid VH/VL sequences → Returns candidatesEdge Case : Invalid sequence characters → Graceful error messageFile Input : Process JSON input → Correctly parses and outputs
Lifecycle Status
Current Stage : DraftNext Review Date : 2026-03-06Known Issues : NonePlanned 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.
