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:

1. Epitope-driven — vaccines work by presenting epitopes to T/B cells; start with epitope prediction
2. Population coverage matters — HLA diversity means no single epitope covers everyone; design for breadth
3. Multi-epitope is better — combine CD8+ (MHC-I) and CD4+ (MHC-II) epitopes for robust immunity
4. Conservation = broad protection — conserved epitopes across strains provide cross-protective immunity
5. Evidence grading — T1: clinical trial data, T2: in-vivo immunogenicity, T3: in-vitro binding, T4: computational prediction only

When to Use

• "Design a vaccine against pathogen]"
• "Predict T-cell epitopes for protein]"
• "What MHC-I epitopes does protein] have?"
• "Assess population coverage of these epitopes"
• "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) | | iedb_search_mhc | Search validated MHC binding assay data | | IEDB_predict_mhci_binding | Predict MHC-I binding (NetMHCpan EL; rank < 0.5% = strong binder) | | IEDB_predict_mhcii_binding | Predict MHC-II binding (NetMHCIIpan EL; CD4+ helper epitopes) | | UniProt_get_entry_by_accession | Get antigen protein sequence | | UniProt_search | Find pathogen protein sequences | | BVBRC_search_genome_features | Search pathogen proteomes | | alphafold_get_prediction | Get/predict antigen 3D structure | | EnsemblVEP_annotate_hgvs | Check epitope conservation across variants | | PubMed_search_articles | Find published vaccine studies | | search_clinical_trials | Find ongoing vaccine clinical trials |

Workflow

Phase 0: Antigen Selection
  Pathogen → essential surface proteins → sequence retrieval
    |
Phase 1: T-Cell Epitope Prediction
  MHC-I (CD8+ CTL) and MHC-II (CD4+ helper) binding prediction
    |
Phase 2: B-Cell Epitope Prediction
  Linear and conformational B-cell epitopes for antibody response
    |
Phase 3: Population Coverage
  HLA allele frequencies → design for target population
    |
Phase 4: Conservation Analysis
  Cross-strain epitope conservation → broad protection
    |
Phase 5: Candidate Assembly & Report
  Multi-epitope construct design → immunogenicity assessment

Phase 0: Antigen Selection

Best antigens for vaccines: Surface-exposed, essential for pathogen function, conserved across strains.

python
# 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):

python
# 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):

python
iedb_search_mhc(
    mhc_class="II",
    qualitative_measure="Positive",
    filters={"source_organism_iri": "eq.NCBITaxon:2697049"},
    limit=50
)

Binding affinity interpretation:

| IC50 (nM) | Classification | Vaccine Relevance | |-----------|---------------|-------------------| | < 50 | Strong binder | Include — high presentation probability | | 50-500 | Moderate binder | Consider — may contribute to response | | 500-5000 | Weak binder | Exclude — unlikely to be presented | | > 5000 | Non-binder | Exclude |

HLA supertype strategy: For broad coverage, predict against HLA supertypes:

• A2 supertype (A02:01, A02:06, A68:02) — covers ~40% globally
• A3 supertype (A03:01, A11:01, A31:01) — covers ~25%
• B7 supertype (B07:02, B35:01, B51:01) — covers ~25%
• A2 + A3 + B7 + B44 combined — covers >90% of most populations

Phase 2: B-Cell Epitope Prediction

B-cell epitopes trigger antibody production. Look for:

• Linear epitopes: Continuous peptide sequences (easier to synthesize)
• Conformational epitopes: 3D surface patches (requires structural data)

python
# 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:

bash
# 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:

python
# 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:

1. Include 3-5 MHC-I epitopes (CD8+ response)
2. Include 2-3 MHC-II epitopes (CD4+ helper response)
3. Include 1-2 B-cell epitopes (antibody response)
4. Connect with appropriate linkers (AAY for MHC-I, GPGPG for MHC-II)
5. Add adjuvant sequence if needed (e.g., flagellin domain for TLR5)

Report structure:

1. Antigen Selection — rationale, conservation, essentiality
2. Epitope Map — all predicted epitopes with binding affinities and HLA restrictions
3. Top Epitopes — ranked by binding strength × conservation × population coverage
4. Population Coverage — % coverage per major world population
5. Conservation Analysis — strain coverage, escape risk assessment
6. Construct Design — multi-epitope sequence with linkers
7. Clinical Precedent — existing vaccines/trials for related antigens
8. 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
• Pathogen evasion — rapidly evolving pathogens (HIV, influenza) may escape epitope-based vaccines