Computational vaccine candidate design: peptide/subunit vaccines via MHC-I/MHC-II epitope prediction (IEDB), population HLA coverage optimization, B-cell epitope identification, and cross-strain conservation analysis. Use for vaccine epitope prediction, HLA allele coverage, multi-epitope construct design, and immunogenicity assessment. Combines predicted MHC binding with experimentally validated IEDB epitopes for higher-confidence designs.
Vaccine Design
Computational pipeline for designing peptide/subunit vaccine candidates through epitope prediction, population coverage optimization, and immunogenicity assessment.
Reasoning Strategy
Vaccine design requires presenting the right epitopes to elicit protective immunity — not just any immune response, but one that is neutralizing, durable, and broadly applicable. For T-cell vaccines, the core tool is MHC binding prediction (IEDB tools): predict peptide-MHC affinity across multiple HLA alleles, then select epitopes with broad coverage of the target population. For antibody vaccines, prioritize surface-exposed conserved regions — a deeply buried or hypervariable region makes a poor antibody target. MHC binding does not equal immunogenicity; many good binders are not immunogenic in vivo due to tolerance, poor processing, or lack of T-cell help. A multi-epitope strategy (combining MHC-I for CD8+ CTL response, MHC-II for CD4+ helper response, and B-cell epitopes for antibody induction) is more robust than any single epitope. Conservation across pathogen strains is critical — an epitope that mutates under immune pressure (like HIV envelope hypervariable regions) is a poor vaccine target.
LOOK UP DON'T GUESS: Do not predict MHC binding or population coverage from memory — use IEDB_predict_mhci_binding / IEDB_predict_mhcii_binding for T-cell predictions, IEDB_predict_bcell_epitopes for antibody (B-cell) epitope prediction, and iedb_search_epitopes for validated experimental data. Do not assume what's on the pathogen surface; retrieve annotated sequences from UniProt or BVBRC.
Key principles:
Epitope-driven — vaccines work by presenting epitopes to T/B cells; start with epitope prediction
Population coverage matters — HLA diversity means no single epitope covers everyone; design for breadth
Multi-epitope is better — combine CD8+ (MHC-I) and CD4+ (MHC-II) epitopes for robust immunity
Conservation = broad protection — conserved epitopes across strains provide cross-protective immunity
"Find conserved epitopes across [pathogen] strains"
Not this skill: For HLA typing or allele frequency only, use tooluniverse-hla-immunogenomics. For antibody engineering, use tooluniverse-antibody-engineering.
Core Tools
Tool
Use For
IEDB_predict_bcell_epitopes
De-novo predict linear B-cell (antibody) epitopes from sequence (BepiPred/Emini/…)
iedb_search_epitopes
Search experimentally validated epitopes
iedb_get_epitope_mhc
Get detailed epitope data (assay results, MHC restriction)
Best antigens for vaccines: Surface-exposed, essential for pathogen function, conserved across strains.
# Find pathogen surface proteins
UniProt_search(query="[organism] AND locations:(location:cell surface) AND reviewed:true")
# Or search BVBRC for annotated pathogen proteomes
BVBRC_search_genome_features(keyword="surface protein", genome_id="[taxon_id]")
Antigen prioritization: prefer surface-exposed (secreted/outer membrane) over cytoplasmic; >95% conserved across strains; essential for pathogen viability; known immunogen in natural infection. Use UniProt subcellular location annotations and PubMed to verify these properties.
Phase 1: T-Cell Epitope Prediction
MHC-I epitopes (CD8+ cytotoxic T cells — kill infected cells):
# Option A: Search for KNOWN validated epitopes from IEDB
iedb_search_mhc(
mhc_class="I",
qualitative_measure="Positive",
filters={"source_organism_iri": "eq.NCBITaxon:2697049"}, # SARS-CoV-2
select=["linear_sequence", "mhc_restriction", "qualitative_measure"],
limit=50
)
# Option B: PREDICT novel peptide binding (recommended for new proteins)
IEDB_predict_mhci_binding(
sequence="YOUR_PROTEIN_SEQUENCE", # full protein or peptide
allele="HLA-A*02:01", # or H-2-Kd for mouse
method="netmhcpan_el", # EL = eluted ligand (recommended)
length=9# 8-11 for MHC-I
)
# Returns peptides ranked by percentile_rank:# < 0.5% = strong binder (include in vaccine)# 0.5-2% = moderate binder (consider)# > 2% = weak/non-binder (exclude)
MHC-II epitopes (CD4+ helper T cells — activate B cells and CD8+ T cells):
Linear epitopes: Continuous peptide sequences (easier to synthesize)
Conformational epitopes: 3D surface patches (requires structural data)
# De-novo predict LINEAR B-cell epitopes along the antigen sequence (BepiPred).# Returns contiguous predicted epitope regions + per-residue scores.
IEDB_predict_bcell_epitopes(sequence="[antigen_aa_sequence]", method="Bepipred")
# Cross-check against KNOWN experimentally validated epitopes
iedb_search_epitopes(query="[protein_name]", epitope_type="B cell")
# Get structure for conformational epitope prediction
alphafold_get_prediction(uniprot_id="[accession]")
B-cell epitope criteria: Surface-exposed loops, hydrophilic regions, flexible regions (high B-factor). Prefer IEDB_predict_bcell_epitopes regions that also fall on surface-exposed loops in the structure; the method arg also supports Emini (surface accessibility), Kolaskar-Tongaonkar (antigenicity), and Parker (hydrophilicity).
Phase 3: Population Coverage
No HLA-frequency tool exists in ToolUniverse, but the coverage math is packaged in
scripts/population_coverage.py. Pass the HLA alleles your selected epitopes bind
(from the IEDB_predict_mhci/mhcii_binding results) and get the % of the
population covered:
# Broad first-pass estimate (bundled average frequencies):
python scripts/population_coverage.py --alleles "HLA-A*02:01,HLA-A*01:01,HLA-A*03:01,HLA-A*24:02,HLA-B*07:02,HLA-B*08:01,HLA-B*44:02"# -> {"overall_coverage": 73.6, "per_locus_coverage_pct": {"A": 62.9, "B": 28.9}, ...}# Population-SPECIFIC: supply real allele frequencies (ALLELE<TAB>FREQ) for the# target ethnicity from the Allele Frequency Net Database (allelefrequencies.net)# or the IEDB population-coverage tool (tools.iedb.org/population):
python scripts/population_coverage.py --alleles-file covered.txt --freq-file afnd_han_chinese.tsv
The bundled default is an approximate broad average — do NOT report it as coverage
for a specific ethnicity; use --freq-file with AFND/IEDB data for that. Also use
the HLA supertype strategy to ensure your epitope set spans the common supertypes.
Population coverage targets:
Coverage Level
Interpretation
Action
>90%
Excellent — vaccine will work in most individuals
Proceed to development
70-90%
Good — most people covered; some populations underserved
Add more epitopes for uncovered HLA types
50-70%
Moderate — significant gaps
Redesign with broader HLA coverage
<50%
Poor — vaccine will miss too many people
Fundamental redesign needed
Phase 4: Conservation Analysis
Check if epitopes are conserved across pathogen strains/variants:
# Search for protein variants across strains
PubMed_search_articles(query="[pathogen] [protein] sequence variation strains")
# Check specific mutations in epitope regions
EnsemblVEP_annotate_hgvs(hgvs_notation="[variant_in_epitope]")
Conservation interpretation:
100% conserved across all known strains → ideal vaccine target
>95% conserved → good target; monitor emerging variants
80-95% conserved → may need strain-specific variants in construct
<80% conserved → avoid; pathogen evolves to escape this epitope
Phase 5: Candidate Assembly & Report
Multi-epitope construct design principles:
Include 3-5 MHC-I epitopes (CD8+ response)
Include 2-3 MHC-II epitopes (CD4+ helper response)
Include 1-2 B-cell epitopes (antibody response)
Connect with appropriate linkers (AAY for MHC-I, GPGPG for MHC-II)
Add adjuvant sequence if needed (e.g., flagellin domain for TLR5)
Construct Design — multi-epitope sequence with linkers
Clinical Precedent — existing vaccines/trials for related antigens
Limitations — predicted only (T4 evidence); needs experimental validation
Limitations
All predictions are computational (T4 evidence) — experimental validation (binding assays, immunogenicity studies) is required before any clinical development
No immunogenicity guarantee — MHC binding ≠ immunogenicity; many good binders are not immunogenic in vivo
B-cell epitope prediction is less reliable than T-cell; conformational epitopes require accurate structures
No adjuvant optimization — adjuvant selection requires empirical testing
