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tooluniverse-vaccine-design

Designs multi-epitope vaccine candidates via MHC binding prediction and population coverage analysis.

1.7k|254|Updated Mar 3, 2025
Search Tags:#immunoinformatics#vaccine design#epitope prediction#mhc binding#iedb#b-cell epitopes#hla coverage
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-vaccine-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-vaccine-design
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-vaccine-design
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-vaccine-design

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Designing peptide and subunit vaccines requires predicting which epitopes will bind MHC molecules, cover diverse HLA alleles in target populations, and remain conserved across pathogen strains — a workflow that normally demands manual querying of IEDB, UniProt, and population genetics databases.

Core Features & Use Cases

  • T-Cell Epitope Prediction: Predict MHC-I and MHC-II binding with NetMHCpan/NetMHCIIpan via IEDB tools, and cross-check against experimentally validated epitopes.
  • B-Cell Epitope Prediction: Identify linear antibody epitopes with BepiPred, Emini, and related methods, combined with AlphaFold structural data.
  • Population Coverage & Conservation: Compute HLA population coverage with the bundled script and assess cross-strain epitope conservation for broad protection.
  • Use Case: Given a SARS-CoV-2 surface protein, retrieve its sequence from UniProt, predict strong MHC-I binders across HLA supertypes, estimate >90% population coverage, and assemble a multi-epitope construct with linkers.

Quick Start

Design a vaccine candidate against a pathogen by predicting T-cell and B-cell epitopes for its surface proteins and estimating population HLA coverage.

Frequently Asked Questions about tooluniverse-vaccine-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I predict MHC-I T-cell epitopes for a protein?▼

Use IEDB_predict_mhci_binding with the protein sequence, an HLA allele such as HLA-A*02:01, and the netmhcpan_el method. Peptides with percentile rank below 0.5% are strong binders suitable for vaccine inclusion.

How to estimate HLA population coverage for vaccine epitopes?▼

Run the bundled scripts/population_coverage.py with the HLA alleles your epitopes bind. For population-specific estimates, supply a frequency file from the Allele Frequency Net Database rather than the approximate bundled defaults.

What is the difference between MHC-I and MHC-II epitope prediction?▼

MHC-I epitopes (8-11mers, predicted with NetMHCpan) drive CD8+ cytotoxic T-cell responses, while MHC-II epitopes (predicted with NetMHCIIpan) drive CD4+ helper responses. A robust vaccine construct includes both.

Does MHC binding prediction guarantee immunogenicity?▼

No. MHC binding does not equal immunogenicity; many strong binders fail in vivo due to tolerance or poor processing. All predictions are T4 computational evidence requiring experimental validation before clinical development.

When should I not use this vaccine design skill?▼

Use tooluniverse-hla-immunogenomics for HLA typing or allele frequency analysis alone, and tooluniverse-antibody-engineering for antibody design. This skill focuses on epitope-based vaccine candidate design.

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