Designing a multi-epitope vaccine to harness computational immunology for next-generation tuberculosis control

Mycobacterium tuberculosis (MTB) remains a global health challenge, necessitating innovative strategies to enhance control and prevention. The emergence of drug-resistant MTB strains underscores the need for advanced tuberculosis (TB) vaccines beyond the Bacillus Calmette-Guérin (BCG). This study used computational approaches to design a multi-epitope MTB vaccine. Conserved antigenic proteins (PE_PGRS, ESAT-6, and CFP-10) were identified via MTB genome analyses from the NCBI database. Cytotoxic T cell (CTL), helper T cell (HTL), and B cell epitopes were predicted using the Immune Epitope Database (IEDB) and assembled with adjuvants and linkers to optimize stability and immunogenicity. The vaccine construct underwent rigorous assessments of epitope mapping, allergenicity, antigenicity, and tertiary structural modeling. Immune simulation and docking with toll-like receptor 4 (TLR4) confirmed strong binding affinity and robust immunological properties. The final construct, consisting of 512 amino acids and a molecular weight of 48,937.61 Da, demonstrated favorable structural and immunological attributes, including 91% of residues in acceptable regions (Ramachandran analysis). Simulations predicted elevated levels of CD4 and CD8 T lymphocytes, B lymphocytes, plasma cells, and natural killer cells in secondary and tertiary immune responses. Additionally, the vaccine was successfully cloned into an Escherichia coli (strain K12) pET-28a (+) plasmid for expression analysis. This study highlights the promise of reverse vaccinology and in-silico methods for designing effective multi-epitope vaccines. While the construct showed high antigenicity and lacked allergenic or toxic properties, further in vivo evaluations are crucial for confirming human efficacy.