Multi-epitope vaccine construct against bovine tuberculosis: insights from immunoinformatics and molecular dynamics simulations

Bovine tuberculosis is a chronic zoonotic disease that continues to threaten the livestock industry worldwide, particularly in developing countries. Despite extensive control efforts, there is still no licensed vaccine available for effective prevention of bTB. This study aimed to design a multi-epitope vaccine candidate against Mycobacterium bovis using comprehensive immunoinformatics and molecular dynamics approaches. Eighteen high-confidence epitopes were predicted from antigenic bovine tuberculosis proteins through machine learning–based algorithms and subsequently assembled into a single vaccine construct using GPGPG linkers and an HSP70 adjuvant to enhance immunogenicity. The designed multi-epitope vaccine was predicted to be non-toxic and non-allergenic, as validated using CSM-Toxin and AllerTOP v2.0 servers. The tertiary structure of the vaccine was modeled and docked with Toll-like receptor 4 to assess molecular interactions and stability. Molecular dynamics simulations for 100 ns at 300 K revealed a stable vaccine–receptor complex with consistent hydrogen bonding and low RMSD fluctuations in a solvated environment. Overall, this study provides theoretical evidence supporting the rational design of a safe, stable, and potentially immunogenic multi-epitope vaccine candidate for bovine tuberculosis, warranting further in vitro and in vivo validation.