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.

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.

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