tooluniverse-antibody-engineering

What it does

AI-guided antibody optimization pipeline from preclinical lead to clinical candidate. It covers sequence humanization, structure modeling, affinity optimization, developability assessment, immunogenicity prediction, and manufacturing feasibility.

How it works

• It follows a phased workflow from input analysis to final reporting. Phase 1 analyzes sequence annotation, species identification, target information, and clinical precedent. Phase 2 designs humanization strategies via framework selection and CDR grafting, including backmutation considerations and a humanization scoring system. Phase 3 models structure (AlphaFold) and assesses CDR conformations, followed by affinity optimization, developability scoring, and immunogenicity prediction. Phase 4 culminates in a comprehensive report and recommended variants, with explicit artifacts: antibody_optimization_report.md (initialized with [Analyzing...]), optimized_sequences.fasta, humanization_comparison.csv, developability_assessment.csv.
• It mandates a report-first approach and produces structured outputs for before/after comparisons and developability scores, as demonstrated in the provided workflow and sample sections.
• It relies on tools named in the workflow (e.g., IMGT_search_genes, IMGT_get_sequence, SAbDab_search_structures, TheraSAbDab_search_by_target, AlphaFold_get_prediction, iedb_search_epitopes, UniProt_get_protein_by_accession, STRING_get_interactions, PubMed_search).

When to use it

• 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", or "Analyze CDR sequences and suggest mutations".

What it can touch

• It lists and utilizes tools for germline identification, framework matching, structure modeling, and immunogenicity/epitope prediction as part of the workflow, enabling generation of multiple optimized variants and documentation artifacts.

Caveats

• The workflow emphasizes a report-first approach and structured optimization metrics (e.g., humanness, CDR preservation, aggregation risk, predicted KD, immunogenicity). It requires generating several output files and a standardized Markdown report for each optimization cycle. It does not guarantee experimental success; it specifies documentation and design steps and scoring methods.