HLA gene-family analysis and MHC-peptide binding for transplant compatibility, vaccine epitope coverage, and cancer immunotherapy. Uses IMGT (HLA polymorphism), IEDB (epitope-MHC binding), UniProt (annotation), DGIdb (druggability). Use for HLA typing/imputation review, vaccine HLA coverage, and immunotherapy prediction biomarkers (HLA-LOH, neoantigen presentation).
HLA & Immunogenomics Analysis
Pipeline for exploring HLA gene families, MHC-peptide binding, epitope associations, and their clinical implications in transplantation, vaccine development, and cancer immunotherapy. Bridges immunogenetic databases (IMGT, IEDB) with functional annotation (UniProt) and druggability data (DGIdb).
Reasoning Strategy
HLA analysis is fundamentally about peptide presentation: the polymorphism of HLA molecules determines which peptides are displayed to T cells, which in turn governs disease susceptibility, transplant rejection, drug hypersensitivity, and vaccine immunogenicity. HLA type affects disease susceptibility for autoimmune conditions (HLA-B27 and ankylosing spondylitis), transplant rejection (HLA mismatch drives alloresponse), drug hypersensitivity (abacavir causes severe hypersensitivity reactions only in HLA-B*57:01 carriers), and vaccine design (epitopes must be presented by the recipient's HLA alleles to elicit a T-cell response). Class I and Class II HLA molecules have fundamentally different binding grooves, peptide lengths, and T-cell partners — never conflate them. The absence of an epitope from IEDB means it has not been tested, not that it cannot bind.
LOOK UP DON'T GUESS: Never assume an allele's binding properties or population frequency — query IEDB for experimental binding data and IMGT for allele annotation. Do not guess which HLA alleles are common in a population; look up published frequency data via PubMed.
Guiding principles:
HLA nomenclature precision -- HLA allele names follow strict conventions (e.g., HLA-A*02:01); get the resolution level right
MHC class awareness -- Class I (A, B, C) and Class II (DR, DQ, DP) have different binding properties and clinical roles
Species context -- most queries target human HLA, but MHC exists across vertebrates; confirm species early
Evidence layering -- combine binding data (IEDB) with gene annotation (IMGT) and structural context (UniProt)
Clinical translation -- connect molecular findings to transplant matching, vaccine targets, or immunotherapy response
English-first queries -- use English terms in all tool calls; respond in the user's language
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
When to Use
Typical triggers:
"Look up HLA-A*02:01 binding peptides"
"What epitopes are presented by MHC class I for [pathogen]?"
"Find HLA gene information for [allele]"
"What MHC molecules bind [peptide/antigen]?"
"Assess HLA associations for [disease]"
"Find immunogenic epitopes for [virus/protein]"
"What drugs target HLA-related pathways?"
Not this skill: For full neoantigen prediction pipelines, use tooluniverse-immunotherapy-response-prediction. For general gene function lookup, use tooluniverse-drug-target-validation.
Core Databases
Database
Scope
Best For
IMGT
International ImMunoGeneTics; HLA/MHC gene nomenclature and sequences
Authoritative HLA gene info, allele nomenclature, sequence data
IEDB
Immune Epitope Database; experimentally validated epitope-MHC data
Retrieve full gene details including functional status and sequence
Note the number of known alleles (HLA-A has >7,000; HLA-B has >8,000)
Identify whether the allele is commonly studied or rare
If allele not found: Check nomenclature -- older names may have been reassigned. Try searching by the gene name alone (e.g., "HLA-A") and filtering results.
Phase 2: MHC Binding & Restriction
Objective: Find what peptides bind to a specific MHC molecule, or what MHC molecules present a given peptide.
Tools:
iedb_search_mhc -- search for MHC molecules in IEDB
Important: IEDB epitopes are experimentally validated, not predicted. The absence of an epitope does not mean it won't bind -- it may simply be untested.
Population coverage for vaccine design: When selecting epitopes for a vaccine, check how common the restricting HLA allele is in the target population. An epitope restricted to HLA-A*02:01 covers ~50% of Europeans but <15% of some African populations. For broad population coverage, select epitopes across multiple HLA supertypes (A2, A3, B7, B44 cover >95% of most populations).
Phase 4: Functional Annotation
Objective: Get protein-level features for HLA molecules and related proteins.
Protein Features -- structural domains, polymorphic sites
Clinical Relevance -- transplant implications, drug associations, disease links
Evidence Summary -- graded by source (IEDB experimental > computational prediction > literature mention)
Edge Cases & Fallbacks
Ambiguous allele name: Ask user for resolution level. "HLA-A2" could mean HLA-A02:01 or the broader A02 group
No IEDB data for allele: Common for rare alleles. Note the gap; suggest computational prediction tools
Cross-species MHC: IMGT covers multiple species. Confirm species context for non-human queries (e.g., H-2 for mouse)
BVBRC empty results: Try broader organism name or use IEDB as primary source
Limitations
No binding prediction: This skill queries experimental databases, not prediction algorithms (NetMHCpan, MHCflurry). It tells you what has been measured, not what might bind
Population frequency gaps: HLA allele frequencies vary dramatically by ethnicity; databases may not cover all populations equally
Class II complexity: Class II molecules are heterodimers (alpha + beta chains); binding prediction and data are less mature than for Class I
Epitope completeness: IEDB coverage is biased toward well-studied pathogens (HIV, influenza, SARS-CoV-2) and common HLA alleles
