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**: 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 info via `UniProt_get_entry_by_accession` **Output**: Sequence information table, CDR annotation, target info, clinical precedent list. See `WORKFLOW_DETAILS.md` Phase 1 for code examples. --- ## Phase 2: Humanization Strategy **Goal**: Select human framework, design CDR grafting, identify backmutations. **Key steps**: 1. Search IMGT for IGHV/IGKV human germline genes 2. Score candidate frameworks by identity, CDR compatibility, and clinical use 3. Design CDR grafting onto selected framework 4. Identify Vernier zone residues that may need backmutation (positions 2, 27-30, 47-48, 67, 69, 71, 78, 93-94) 5. Generate at least 2 variants: full humanization and with key backmutations 6. Calculate humanization score (framework humanness, CDR preservation, T-cell epitopes, aggregation risk) **Output**: Framework selection rationale, grafting design, backmutation analysis, humanized sequences. See `WORKFLOW_DETAILS.md` Phase 2 for code examples. --- ## Phase 3: Structure Modeling & Analysis **Goal**: Predict structure, analyze CDR conformations, map epitope. **Key steps**: 1. Predict Fv structure via `ESMFold_predict_structure` (VH-linker-VL scFv; `alphafold_get_prediction` only retrieves precomputed models by UniProt accession) 2. Assess pLDDT scores by region (framework, CDRs, interface) 3. Classify CDR canonical structures and calculate RMSD 4. Search known epitopes via `iedb_search_epitopes` 5. Compare with clinical antibody structures via `SAbDab_search_structures` **Output**: Structure quality table, CDR conformation analysis, epitope mapping, structural comparison. See `WORKFLOW_DETAILS.md` Phase 3 for code examples. --- ## Phase 4: Affinity Optimization **Goal**: Design affinity-improving mutations via computational screening. **Key steps**: 1. Identify interface residues (distance cutoff 4.5 A) 2. Screen all amino acid substitutions at CDR interface positions 3. Rank by computed binding-energy change only when the method and calibration support it; state the sign convention and uncertainty. An illustrative cutoff is not a universal binding gate. 4. Design combination strategy: single -> double -> triple mutants 5. Consider CDR-H3 extension, tyrosine enrichment, salt bridge formation 6. Optional: specify stronger acidic binding or neutral binding with acidic release before considering histidine substitutions. Neither direction follows automatically from His pKa. Validate complete bound/free protonation assumptions with controls and matched assays; use [Protein Design Campaign](../tooluniverse-protein-design-campaign/SKILL.md) and its [pH reference](../tooluniverse-protein-design-campaign/references/ph-selectivity.md). **Output**: Ranked mutation hypotheses, combination strategy, computed metrics where supported, and explicit unverified affinity/conditional-binding claims. Do not invent KD ranges or fold improvements. See `WORKFLOW_DETAILS.md` Phase 4 for code examples. --- ## Phase 5: Developability Assessment **Goal**: Comprehensive developability scoring (0-100) across five dimensions. **Key steps**: 1. **Aggregation, pI, hydrophobic patches** (sequence-computable): run `scripts/developability.py --seq <VH/VL>`. It returns AGGRESCAN aggregation-prone regions (real per-residue propensity), isoelectric point, and Kyte-Doolittle hydrophobic patches. 2. **PTM liability**: Scan for deamidation (NG/NS), isomerization (DG/DS), oxidation (Met/Trp), N-glycosylation (N-X-S/T) 3. **Stability (Tm)** and **4. Expression titer**: NOT computable from sequence alone — use external predictors (sequence→Tm/titer ML tools; report only if actually run). Do not fabricate Tm/titer numbers. 5. **Solubility**: external solubility predictor (e.g. NetSolP) **Scoring**: Weighted average (aggregation 0.30, PTM 0.25, stability 0.20, expression 0.15, solubility 0.10). Tiers: T1 (>75), T2 (60-75), T3 (<60). **Output**: Component scores, overall score, tier classification, mitigation recommendations. See `WORKFLOW_DETAILS.md` Phase 5 and `CHECKLISTS.md` for scoring details. --- ## Phase 6: Immunogenicity Prediction **Goal**: Predict immunogenicity risk and design deimmunization strategy. **Key steps**: 1. Scan 9-mer peptides against IEDB for MHC-II binding epitopes 2. Count non-human residues in framework regions 3. Assess aggregation-related immunogenicity 4. Calculate total risk score (0-100, lower is better): Low <30, Medium 30-60, High >60 5. Propose deimmunization mutations (remove T-cell epitopes while preserving CDRs) 6. Compare with clinical precedent ADA rates **Output**: T-cell epitope list, risk score breakdown, deimmunization strategy, clinical comparison. See `WORKFLOW_DETAILS.md` Phase 6 for code examples. --- ## Phase 7: Manufacturing Feasibility **Goal**: Assess expression, purification, formulation, and CMC feasibility. **Key steps**: 1. Assess codon optimization for CHO, identify rare codons -- `CodonUsage_get_optimal_codons(taxid=<Cricetulus griseus NCBI taxid>)` gives the CHO-preferred codon per amino acid to compare against; resolve the taxid first if not already known rather than assuming one. `CodonUsage_get_table` (same taxid) additionally gives per-codon usage frequency, needed to flag which codons in the actual sequence are rare in CHO specifically -- human codon-usage tables (e.g. from tooluniverse-sequence-analysis) are the wrong reference here, rare-in-human is not the same set as rare-in-CHO 2. Design signal peptide 3. Plan 3-step purification: Protein A capture -> cation exchange polishing -> viral nanofiltration 4. Recommend formulation (buffer, pH, stabilizer, tonicity) 5. Define analytical characterization panel (SEC-MALS, CEX, CE-SDS, SPR, DSF) 6. Estimate CMC timeline and costs (typically 18-24 months, $1.65-2.65M to IND) **Output**: Expression assessment, purification strategy, formulation recommendation, CMC timeline. See `MANUFACTURING.md` for detailed manufacturing content and `WORKFLOW_DETAILS.md` Phase 7 for code. --- ## Phase 8: Final Report & Recommendations **Goal**: Compile all findings into a ranked recommendation with validation plan. **Key outputs**: 1. **Top candidate** with key metrics (humanness, affinity, developability, immunogenicity, stability, expression) 2. **Key improvements** table comparing original vs. optimized 3. **Experimental validation plan**: In vitro (3-4 months) -> Lead optimization (2-3 months) -> Preclinical (6-12 months) 4. **Backup variants** with profiles and recommendations 5. **IP considerations**: FTO analysis, CDR novelty, patentability 6. **Next steps**: Immediate (month 1-3), short-term (4-6), long-term (7-24) See `REPORT_TEMPLATE.md` for the full report template. --- ## Tool Reference ### IMGT Tools - `IMGT_search_genes`: Search germline genes (IGHV, IGKV, etc.) - `IMGT_get_sequence`: Get germline sequences - `IMGT_get_gene_info`: Database information ### Antibody Databases - `SAbDab_search_structures`: Search antibody structures - `SAbDab_get_structure`: Get structure details - `TheraSAbDab_search_therapeutics`: Search by name - `TheraSAbDab_search_by_target`: Search by target antigen ### Immunogenicity - `iedb_search_epitopes`: Search epitopes - `iedb_search_bcell`: B-cell epitopes - `iedb_search_mhc`: MHC-II epitopes - `iedb_get_epitope_references`: Citations ### Structure & Target - `ESMFold_predict_structure`: Fv structure prediction from a sequence - `alphafold_get_prediction`: Retrieve a precomputed AlphaFold DB model by UniProt accession - `UniProt_get_entry_by_accession`: Target info - `RCSBData_get_entry`: Experimental structures ### Reagent Verification (Antibody Registry) - `AntibodyRegistry_search`: Find validated commercial antibodies for a target (by target gene/protein name, antibody name, or vendor keyword). Returns each hit's RRID, target (with UniProt/Entrez id), clonality, host organism, applications, vendor/catalog number, and defining citation. Useful during Phase 1 (target/precedent research) to see what antibody reagents already exist against a target antigen before designing a new one, or during Phase 8 (validation planning) to identify a benchmark reagent. - `AntibodyRegistry_get_by_rrid`: Resolve a specific RRID (e.g. `AB_2298772`, accepts the bare numeric id or `RRID:AB_...` form too) cited in a methods section to its full record — verify which exact antibody a paper used rather than assuming from the name alone. Live-verified: both tools return real records (e.g. `AntibodyRegistry_search(query="GFAP")` -> a real GFAP rabbit mAb with UniProt target P14136). - `SciCrunch_resolve_rrid`: General RRID resolver for everything that ISN'T an antibody — cell lines (`CVCL_` prefix), model organisms, software/tools (`SCR_` prefix), and plasmids. Use it when a clinical-precedent or validation paper's methods section also cites the cell line or software used alongside an antibody. It will still resolve an `AB_` RRID, but only returns generic fields (name, description, categories, proper_citation) — no target/clonality/host — so prefer `AntibodyRegistry_get_by_rrid` for antibody RRIDs specifically. Live-verified: `SciCrunch_resolve_rrid(rrid="CVCL_0045")` returns the real HEK293 cell-line record (12 synonyms, `proper_citation: "NCBI_Iran Cat# C497, RRID:CVCL_0045"`); on the same `AB_2298772` antibody RRID above it returns only `name`/`description`/`categories` with no target info, confirming the richer-antibody-fields distinction. ### Systems Biology (for Bispecifics) - `STRING_get_interaction_partners`: Protein interactions - `STRING_get_enrichment`: Pathway analysis --- ## Reference Files | File | Contents | |------|----------| | `QUICK_START.md` | Getting started guide, SOAP tool parameters, Python SDK and MCP usage | | `WORKFLOW_DETAILS.md` | Code examples for all 8 phases | | `REPORT_TEMPLATE.md` | Full report template with section formats and example tables | | `MANUFACTURING.md` | Detailed manufacturing content (expression, purification, formulation, CMC) | | `EXAMPLES.md` | Complete clinical scenario examples (humanization, affinity, bispecific) | | `CHECKLISTS.md` | Evidence grading, completeness checklists, scoring details, special considerations |