# Integrated reiterative pipeline for rapid epitope-based pan-alphavirus vaccines

## TL;DR

A new pipeline for designing vaccines against multiple alphaviruses uses machine learning and experimental validation to identify effective T cell epitopes.

## Contribution

The novel pipeline integrates epitope prediction, protein modeling, and experimental validation to rapidly design multitarget vaccines.

## Key findings

- T cell epitopes were validated using peptide microarrays and molecular dynamics simulations.
- Murine and human T cells showed robust activation and cytokine secretion in response to prioritized epitopes.
- Composite immunogenicity scores enabled the selection of final vaccine candidates.

## Abstract

The vast diversity of the virosphere underscores the need for rapid, adaptable vaccine development infrastructures. Arthropod-borne zoonotic alphaviruses, in particular, continue to pose substantial threats to human and animal health. We present a fast, multitarget vaccine design pipeline integrating machine learning–based epitope prediction, protein modeling, and docking to prioritize viral peptides by immunogenicity, allele coverage, solubility, and stability. T cell epitopes were validated using peptide microarrays and molecular dynamics simulations, confirming receptor binding accuracy. Flow cytometry of murine and human peripheral blood mononuclear cells demonstrated robust T cell activation and cytokine secretion (IFN-γ, TNF-α, or IL-2), dependent on species and HLA allele. Final candidates were selected by composite immunogenicity scores. While this study primarily validates the T cell–specific arm of our predictive pipeline, complementary B cell epitope analyses are ongoing. Our findings support the development of broadly protective pan-alphaviral vaccines and the establishment of efficient, tunable processes for global vaccine development.

A computational, in vitro, and ex vivo pipeline enabling sustainable efficiency from viral proteome to vaccine payload.