tooluniverse-vaccine-design

What this skill does

# 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