---
name: tooluniverse-vaccine-design
description: "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."
disable-model-invocation: true
---
# 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** (A*02:01, A*02:06, A*68:02) — covers ~40% globally
- **A3 supertype** (A*03:01, A*11:01, A*31:01) — covers ~25%
- **B7 supertype** (B*07:02, B*35:01, B*51:01) — 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