Abstract
Bovine tuberculosis (bTB), caused by Mycobacterium bovis, poses significant zoonotic and economic challenges globally. The current prevention and treatment options are limited and increasingly complicated by the emergence of multidrug-resistant strains. This study employs reverse vaccinology and immunoinformatics to design a multi-epitope subunit vaccine targeting the MPB83, ArfA, DnaK, GrpE, and LpqH proteins of M. bovis. The T-cell and B-cell epitopes of the candidate vaccine were predicted and evaluated for antigenicity, allergenicity, and toxicity. The promising epitopes were then assembled into three vaccine constructs (bTBV1, bTBV2, and bTBV3) using appropriate adjuvants, pan HLA DR-binding epitope (PADRE), and linkers. The constructs were analyzed for physicochemical properties, 3D structure, cytokines induction and stability, followed by molecular docking with bovine CD molecules and toll-like receptor, TLR-9. Among the candidates, bTBV3 was chosen as one of the most promising vaccine candidates due to its high aliphatic index (67.60), lowest instability score (27.26), and a strong binding affinity. Molecular dynamics simulations and the results of interactions between the vaccine-receptor complexes (eigenvalue 2.318704e-06) show that the vaccine construct bTBV3 is stable. In silico immune simulation findings, such as elevated IgM levels and increased Th cell populations, suggest that the designed multi-epitope vaccine candidate bTBV3 elicits robust humoral and cellular immune responses, confirming the vaccine's potential efficacy. Additionally, codon optimization (CAI: 0.997 and GC: 54.687%) and in silico cloning facilitated efficient expression in E. coli. This study highlights the potential of bioinformatics-driven approaches in developing effective subunit vaccines against bTB, providing a foundation for experimental validation and future applications in combating this pervasive zoonotic disease, bovine tuberculosis.