antibody-engineering

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• SKILL.md
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• run.py · scripts/

SKILL.md (first lines)

---
name: tooluniverse-antibody-engineering
description: Comprehensive antibody engineering and optimization for therapeutic development. Covers humanization, affinity maturation, developability assessment, and immunogenicity prediction. Use when asked to optimize antibodies, humanize sequences, or engineer therapeutic antibodies from lead to clinical candidate.
---

# 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

---

## 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**:
   - File name: `antibody_optimization_report.md`
   - Initialize with section headers
   - Add placeholder: `[Analyzing...]`

2. **Progressively update** as analysis completes

3. **Output separate files**:
   - `optimized_sequences.fasta` - All optimized variants
   - `humanization_comparison.csv` - Before/after comparison
   - `developability_assessment.csv` - Detailed scores

### 2. Documentation Standards (MANDATORY)

Every optimization MUST include:

```markdown
### Optimized Variant: VH_Humanized_v1

**Original Sequence**: EVQLVESGGGLVQPGG... (mouse)
**Humanized Sequence**: EVQLVQSGAEVKKPGA... (human framework)
**Humanization Score**: 87% human framework
**CDR Preservation**: 100% (all CDR residues retained)

**Metrics**:
| Metric | Original | Optimized | Change |
|--------|----------|-----------|--------|
| Humanness | 62% | 87% | +25% |
| Aggregation risk | 0.58 | 0.32 | -45% |
| Predicted KD | 5.2 nM | 3.8 nM | +27% affinity |
| Immunogenicity | High | Low | -65% |

*Source: IMGT germline analysis, IEDB predictions*
```

---

## Phase 0: Tool Verification

### Required Tools

| Tool | Purpose | Category |
|------|---------|----------|
| `IMGT_search_genes` | Germline gene identification | Humanization |
| `IMGT_get_sequence` | Human framework sequences | Humanization |
| `SAbDab_search_structures` | Antibody structure precedents | Structure |
| `TheraSAbDab_search_by_target` | Clinical antibody benchmarks | Validation |
| `AlphaFold_get_prediction` | Structure modeling | Structure |
| `iedb_search_epitopes` | Epitope identification | Immunogenicity |
| `iedb_search_bcell` | B-cell epitope prediction | Immunogenicity |
| `UniProt_get_protein_by_accession` | Target antigen information | Target |
| `STRING_get_interactions` | Protein interaction network | Bispecifics |
| `PubMed_search` | Literature precedents | Validation |

---

## Workflow Overview

```
Phase 1: Input Analysis & Characterization
├── Sequence annotation (CDRs, framework)
├── Species identification
├── Target antigen identification
├── Clinical precedent search
└── OUTPUT: Input characterization
    ↓
Phase 2: Humanization Strategy
├── Germline gene alignment (IMGT)
├── Framework selection
├── CDR grafting design
├── Backmutation identification
└── OUTPUT: Humanization plan
    ↓
Phase 3: Structure Modeling & Analysis
├── AlphaFold prediction
├── CDR conformation analysis
├── Epitope mapping
├── Interface analysis
└── OUTPUT: Structural assessment
    ↓
Phase 4: Affinity Optimization

From lamm-mit/scienceclaw, Apache-2.0.

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