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

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.