Predict MHC-I, MHC-II, and B-cell epitope risk from sequence.
Immunogenicity analysis predicts T-cell and B-cell epitope burden across the variable region of your variant. The output includes per-class risk scores (0–1), epitope lists with binding affinities, and deimmunization suggestions. The job takes 1–3 minutes and costs 2 credits.
result = job.resultsprint(result["risk_score"]) # composite immunogenicity risk, 0–1print(result["mhc1_risk"]) # MHC class I risk, 0–1print(result["mhc2_risk"]) # MHC class II risk, 0–1print(result["bcell_risk"]) # linear B-cell epitope risk, 0–1
Lower is better. A mhc2_risk above ~0.5 warrants deimmunization before advancing.
for epitope in result["mhc1_epitopes"] or []: print(epitope["peptide"], epitope["allele"], epitope["affinity_nm"])for epitope in result["mhc2_epitopes"] or []: print(epitope["peptide"], epitope["allele"], epitope["score"])for epitope in result["bcell_epitopes"] or []: print(epitope["start"], epitope["end"], epitope["sequence"])
for suggestion in result["deimmunization_suggestions"] or []: print(suggestion["position"], suggestion["wild_type"], suggestion["mutation"], suggestion["mhc1_risk_delta"], suggestion["mhc2_risk_delta"])
Each suggestion is a point mutation that reduces epitope burden. risk_delta values are negative when the substitution helps.
for job in client.immunogenicity_analyses.list(variant_id=variant_id): print(job.id, job.status, job["created_at"])
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