Antibody Humanizer

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"},
#   ...
# }

# 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}")

# 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}
)

# 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

# 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]'

# 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"])

# 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")

# 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

# 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}")