tooluniverse-antibody-engineering

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

.env.template

Audit 0.4.1 · source sha 43be0fac747b · 29 Oct 2026Report an issue or request a re-scan

• Publishermims-harvard GitHub organizationOwner of the repository this skill is read from
• Source foldermims-harvard/tooluniverse/skills/tooluniverse-antibody-engineeringThe exact folder we audited
• LicenceApache-2.0As the repository states it
• Commit pinned for this verdict43be0fac747bA new audit runs when the source changes
• Repository statusNot archivedLast push 1 day ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Therapeutic antibody engineering and optimization, lead-to-clinical-candidate. Covers sequence humanization (germline alignment, framework retention), affinity maturation, developability (aggregation, stability, PTMs),…

---
name: tooluniverse-antibody-engineering
description: Therapeutic antibody engineering and optimization, lead-to-clinical-candidate. Covers sequence humanization (germline alignment, framework retention), affinity maturation, developability (aggregation, stability, PTMs), structure modeling (AlphaFold/PDB CDR analysis), immunogenicity prediction, and manufacturing feasibility. Use for biologic-drug optimization, mAb design review, biosimilar engineering, and clinical-precedent comparison.
disable-model-invocation: true
---

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

See `REPORT_TEMPLATE.md` for the full report template with section formats.

### 2. Documentation Standards (MANDATORY)

Every optimization MUST include per-variant documentation with:
- Original and optimized sequences
- Humanization score (% human framework)
- CDR preservation confirmation
- Metrics table (humanness, aggregation risk, predicted KD, immunogenicity)
- Data source citations

---

## 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 |
| `ESMFold_predict_structure` | Fv structure modeling (VH-linker-VL scFv) | Structure |
| `alphafold_get_prediction` | Retrieve a precomputed AlphaFold DB model by UniProt accession (target antigen) | Structure |
| `iedb_search_epitopes` | Epitope identification | Immunogenicity |
| `iedb_search_bcell` | B-cell epitope prediction | Immunogenicity |
| `UniProt_get_entry_by_accession` | Target antigen information | Target |
| `STRING_get_interaction_partners` | Protein interaction network | Bispecifics |
| `PubMed_search_articles` | Literature precedents | Validation |

**CRITICAL**: SOAP tools (IMGT, SAbDab, TheraSAbDab) require an `operation` parameter. See `QUICK_START.md` for correct usage.

---

## 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
├── In silico mutation screening
├── CDR optimization strategies
├── Interface improvement
└── OUTPUT: Affinity variants
    ↓
Phase 5: Developability Assessment
├── Aggregation propensity
├── PTM site identification
├── Stability prediction
├── Expression prediction
└── OUTPUT: Developability score
    ↓
Phase 6: Immunogenicity Prediction
├── MHC-II epitope prediction (IEDB)
├── T-cell epitope risk
├── Aggregation-related immunogenicity
└── OUTPUT: Immunogenicity risk score
    ↓
Phase 7: Manufacturing Feasibility
├── Expression level prediction
├── Purification considerations
├── Formulation stability
└── OUTPUT: Manufacturing assessment
    ↓
Phase 8: Final Report & Recommendations
├── Ranked variant list
├── Experimental validation plan
├── Next steps
└── OUTPUT: Comprehensive report
```

---

## Phase 1: Input Analysis & Characterization

**Goal**: Annotate sequences, identify species/germline, find clinical precedents.

**Key steps**:
1. Annotate CDRs using an antibody-numbering tool and map labels back to sequence indices (IMGT CDR-H1: 27-38, CDR-H2: 56-65, CDR-H3: 105-117). These labels are not literal array offsets, especially with insertions or terminal extensions.
2. Identify closest human germline genes via `IMGT_search_genes`
3. Search clinical precedents via `TheraSAbDab_search_by_target`
4. Get target antigen

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-29
   ✓SAFE B 89 / 100

   first audit · source 43be0fac747b

Questions about tooluniverse-antibody-engineering

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