An Immunoinformatics-Based Multi-Epitope Vaccine Targeting Multiple Stages of Tuberculosis Pathogenesis

Abstract

Tuberculosis (TB), caused by Mycobacterium tuberculosis (M. tuberculosis), remains one of the leading causes of mortality from infectious diseases worldwide, underscoring the urgent need for more effective vaccine strategies. Advances in immunoinformatics have enabled rational, cost-effective vaccine design through systematic epitope identification and optimization. In this study, a multi-epitope vaccine candidate was computationally designed by integrating cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes derived from three M. tuberculosis antigens associated with distinct stages of infection: Ag85B, ESAT-6, and HspX. Selected epitopes were screened for antigenicity, immunogenicity, allergenicity, toxicity, and global population coverage, and assembled into a single construct using rational linker and adjuvant strategies. Structural modeling and refinement were performed, followed by molecular docking and molecular dynamics simulations to evaluate interactions with human Toll-like receptors (TLRs) TLR2, TLR4, and TLR9. The designed construct exhibited favorable antigenic and safety profiles, structural stability, and broad population coverage. Docking, molecular dynamics, and binding free-energy analyses suggested stable and energetically favorable interactions with all three TLRs, with the strongest binding observed for the TLR9 complex. In silico immune simulations predicted robust cellular and humoral immune responses, characterized by elevated interferon-γ (IFN-γ) and interleukin-2 (IL-2) levels and the formation of immunological memory. Overall, this study presents a stage-spanning, multi-epitope TB vaccine design and provides a comprehensive immunoinformatics framework that supports further experimental validation and optimization.