Antibody Engineering & Optimization

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:

1. Report-first approach - Create optimization report before analysis
2. Evidence-graded humanization - Score based on germline alignment and framework retention
3. Developability-focused - Assess aggregation, stability, PTMs, immunogenicity
4. Structure-guided - Use AlphaFold/PDB structures for CDR analysis
5. Clinical precedent - Reference approved antibodies for validation
6. Quantitative scoring - Developability score (0-100) combining multiple factors
7. English-first queries - Always use English terms in tool calls, even if user writes in another language. Respond in user's language

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

When to Use

Apply when user asks:

• "Humanize this mouse antibody sequence"
• "Optimize antibody affinity for [target]"
• "Assess developability of this antibody"
• "Predict immunogenicity risk for [sequence]"
• "Engineer bispecific antibody against [targets]"
• "Reduce aggregation in antibody formulation"
• "Design pH-dependent binding antibody"
• "Analyze CDR sequences and suggest mutations"

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

1. Create the report file FIRST: antibody_optimization_report.md
2. Progressively update as analysis completes