tooluniverse-antibody-engineering
Antibody Engineering & Optimization
AI-guided antibody optimization pipeline from preclinical lead to clinical candidate. Covers sequence humanization, structure modeling, affinity optimization, developability assessment, immunogenicity prediction, and manufacturing feasibility.
KEY PRINCIPLES:
- Report-first approach - Create optimization report before analysis
- Evidence-graded humanization - Score based on germline alignment and framework retention
- Developability-focused - Assess aggregation, stability, PTMs, immunogenicity
- Structure-guided - Use AlphaFold/PDB structures for CDR analysis
- Clinical precedent - Reference approved antibodies for validation
- Quantitative scoring - Developability score (0-100) combining multiple factors
- English-first queries - Always use English terms in tool calls, even if user writes in another language. Respond in user's language
When to Use
Apply when user asks:
- "Humanize this mouse antibody sequence"
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