An Immunocomputing Approach to the Development of Multiepitope Vaccine against Mycobacterium tuberculosis

Abstract

Tuberculosis (TB) is the second most deadly airborne infectious disease caused by Mycobacterium tuberculosis, posing a major global health risk       dw. Nearly one-third of the world’s population is infected, with low-income countries most affected due to limited access to diagnostics and treatments. Although the Bacillus Calmette–Guérin (BCG) vaccine offers protection in infancy, its reduced efficacy beyond 20 years limits long-term TB control. This study focuses on drafting a multi-epitope peptide-based vaccine against TB using immune-informatics approaches. Ten M. tuberculosis-specific antigenic proteins (PPE39, PPE68, Rv0310c, PE_PGRS35, PE_PGRS31, CFP10, Rv1975, lpqG, esxV, and espJ) were analyzed to identify potent immunogenic regions. Five cytotoxic T lymphocyte (CTL) epitopes, five helper T cell (HTL) epitopes, and several B-cell epitopes are proficient of causing strong immune responses, including Interferon-γ (IFN-γ) production, were selected. A total of 27 epitopes were linked using AAY, GPGPG, and KK linkers to construct the vaccine, which was assessed for physicochemical properties, antigenicity, allergenicity, and toxicity. The design demonstrated stability and strong immunogenic potential. Molecular docking with the TLR-4 monomer showed favorable binding affinity, indicating its potential as an effective vaccine candidate. By applying immune-informatics tools for precise epitope selection and vaccine design, this research provides a cost-effective strategy to enhance TB prevention. The study offers a strong framework for developing next-generation TB vaccines and supports global efforts to eliminate tuberculosis by 2035.