Skip to main content
tooluniverse-antibody-engineering 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.
Jump to install Skills Marketplace Discover and explore AI skills built by the community.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Show prompt details A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-antibody-engineeringThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Download Zip Downloading... More from this repository tooluniverse-drug-research Comprehensive drug profiling โ mechanism, primary/secondary targets, drug interactions, clinical-trial status, adverse events (FAERS), pharmacogenomics, and approval history. Use for full drug investigation reports, 'tell me about drug X' queries, and assembling drug profiles for clinicians, researchers, or regulatory work.
tooluniverse-gene-enrichment Gene-set enrichment analysis โ GO (Biological Process, Molecular Function, Cellular Component), KEGG, Reactome pathway enrichment via clusterProfiler, gseapy, ORA, GSEA. Use for interpreting DEG lists, screen hit lists, or any gene-list-to-pathways query. Includes simplify-cutoff handling and union-vs-total denominator conventions for percent-DE questions.
tooluniverse-multiomic-disease-characterization Comprehensive disease characterization across genomics, transcriptomics, proteomics, and pathways for systems-level understanding. Identifies therapeutic opportunities and biomarker candidates by integrating multi-layer molecular data. Use for full-omics disease deep-dive reports, mechanism mapping, and biomarker-and-target identification from multi-omics data.
REPORT_TEMPLATE.md 12.6 KB WORKFLOW_DETAILS.md 16.5 KB Related occupations SOC
Based on SOC occupation classification
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.
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 analysisEvidence-graded humanization - Score based on germline alignment and framework retentionDevelopability-focused - Assess aggregation, stability, PTMs, immunogenicityStructure-guided - Use AlphaFold/PDB structures for CDR analysisClinical precedent - Reference approved antibodies for validationQuantitative scoring - Developability score (0-100) combining multiple factorsEnglish-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)
Create the report file FIRST : antibody_optimization_report.mdProgressively update as analysis completesOutput separate files :
optimized_sequences.fasta - All optimized variantshumanization_comparison.csv - Before/after comparisondevelopability_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_genesGermline gene identification Humanization IMGT_get_sequenceHuman framework sequences Humanization SAbDab_search_structuresAntibody structure precedents Structure TheraSAbDab_search_by_targetClinical antibody benchmarks Validation alphafold_get_predictionStructure modeling Structure iedb_search_epitopesEpitope identification Immunogenicity iedb_search_bcellB-cell epitope prediction Immunogenicity UniProt_get_entry_by_accessionTarget antigen information Target STRING_get_interaction_partnersProtein interaction network Bispecifics PubMed_search_articlesLiterature 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
โโโ Sequence annotation (CDRs, framework)
โโโ Species identification
โโโ Target antigen identification
โโโ Clinical precedent search
โโโ OUTPUT: Input characterization
โ
Phase 2: Humanization Strategy
โโโ Germline gene alignment (IMGT)
โโโ Framework selection
โโโ CDR grafting design
โโโ Backmutation identification
โโโ OUTPUT: Humanization plan
โ
Phase 3: Structure Modeling & Analysis
โโโ AlphaFold prediction
โโโ CDR conformation analysis
โโโ Epitope mapping
โโโ Interface analysis
โโโ OUTPUT: Structural assessment
โ
Phase 4: Affinity Optimization
โโโ In silico mutation screening
โโโ CDR optimization strategies
โโโ Interface improvement
โโโ OUTPUT: Affinity variants
โ
Phase 5: Developability Assessment
โโโ Aggregation propensity
โโโ PTM site identification
โโโ Stability prediction
โโโ Expression prediction
โโโ OUTPUT: Developability score
โ
Phase 6: Immunogenicity Prediction
โโโ MHC-II epitope prediction (IEDB)
โโโ T-cell epitope risk
โโโ Aggregation-related immunogenicity
โโโ OUTPUT: Immunogenicity risk score
โ
Phase 7: Manufacturing Feasibility
โโโ Expression level prediction
โโโ Purification considerations
โโโ Formulation stability
โโโ OUTPUT: Manufacturing assessment
โ
Phase 8: Final Report & Recommendations
โโโ Ranked variant list
โโโ Experimental validation plan
โโโ Next steps
โโโ OUTPUT: Comprehensive report
Phase 1: Input Analysis & Characterization Goal : Annotate sequences, identify species/germline, find clinical precedents.
Annotate CDRs using IMGT numbering (CDR-H1: 27-38, CDR-H2: 56-65, CDR-H3: 105-117)
Identify closest human germline genes via IMGT_search_genes
Search clinical precedents via TheraSAbDab_search_by_target
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.
Search IMGT for IGHV/IGKV human germline genes
Score candidate frameworks by identity, CDR compatibility, and clinical use
Design CDR grafting onto selected framework
Identify Vernier zone residues that may need backmutation (positions 2, 27-30, 47-48, 67, 69, 71, 78, 93-94)
Generate at least 2 variants: full humanization and with key backmutations
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.
Predict Fv structure via alphafold_get_prediction (VH:VL)
Assess pLDDT scores by region (framework, CDRs, interface)
Classify CDR canonical structures and calculate RMSD
Search known epitopes via iedb_search_epitopes
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.
Identify interface residues (distance cutoff 4.5 A)
Screen all amino acid substitutions at CDR interface positions
Rank by predicted binding energy change (ddG < -0.5 kcal/mol = favorable)
Design combination strategy: single -> double -> triple mutants
Consider CDR-H3 extension, tyrosine enrichment, salt bridge formation
Optional: pH-dependent binding via histidine substitutions
Output : Ranked mutation list, combination strategy, expected affinity improvements.
See WORKFLOW_DETAILS.md Phase 4 for code examples.
Phase 5: Developability Assessment Goal : Comprehensive developability scoring (0-100) across five dimensions.
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.PTM liability : Scan for deamidation (NG/NS), isomerization (DG/DS), oxidation (Met/Trp), N-glycosylation (N-X-S/T)Stability (Tm) and 4. Expression titer : NOT computable from sequence alone โ use external predictors (sequenceโTm/titer ML tools; report only if actually run). Do not fabricate Tm/titer numbers.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.
Scan 9-mer peptides against IEDB for MHC-II binding epitopes
Count non-human residues in framework regions
Assess aggregation-related immunogenicity
Calculate total risk score (0-100, lower is better): Low <30, Medium 30-60, High >60
Propose deimmunization mutations (remove T-cell epitopes while preserving CDRs)
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.
Assess codon optimization for CHO, identify rare codons
Design signal peptide
Plan 3-step purification: Protein A capture -> cation exchange polishing -> viral nanofiltration
Recommend formulation (buffer, pH, stabilizer, tonicity)
Define analytical characterization panel (SEC-MALS, CEX, CE-SDS, SPR, DSF)
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.
Top candidate with key metrics (humanness, affinity, developability, immunogenicity, stability, expression)Key improvements table comparing original vs. optimizedExperimental validation plan : In vitro (3-4 months) -> Lead optimization (2-3 months) -> Preclinical (6-12 months)Backup variants with profiles and recommendationsIP considerations : FTO analysis, CDR novelty, patentabilityNext 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 sequencesIMGT_get_gene_info: Database information
Antibody Databases
SAbDab_search_structures: Search antibody structuresSAbDab_get_structure: Get structure detailsTheraSAbDab_search_therapeutics: Search by nameTheraSAbDab_search_by_target: Search by target antigen
Immunogenicity
iedb_search_epitopes: Search epitopesiedb_search_bcell: B-cell epitopesiedb_search_mhc: MHC-II epitopesiedb_get_epitope_references: Citations
Structure & Target
alphafold_get_prediction: Structure predictionUniProt_get_entry_by_accession: Target infoRCSBData_get_entry: Experimental structures
Systems Biology (for Bispecifics)
STRING_get_interaction_partners: Protein interactionsSTRING_get_enrichment: Pathway analysis
Reference Files File Contents QUICK_START.mdGetting started guide, SOAP tool parameters, Python SDK and MCP usage WORKFLOW_DETAILS.mdCode examples for all 8 phases REPORT_TEMPLATE.mdFull report template with section formats and example tables MANUFACTURING.mdDetailed manufacturing content (expression, purification, formulation, CMC) EXAMPLES.mdComplete clinical scenario examples (humanization, affinity, bispecific) CHECKLISTS.mdEvidence grading, completeness checklists, scoring details, special considerations
