In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology

Tuberculosis (TB) kills more individuals in the world than any other disease, and a threat made direr by the coverage of drug-resistant strains of Mycobacterium tuberculosis (Mtb). Bacillus Calmette-Guérin (BCG) is the single TB vaccine licensed for use in human beings and effectively protects infants and children against severe military and meningeal TB. We applied advanced computational techniques to develop a universal TB vaccine. In the current study, we select the very conserved, experimentally confirmed Mtb antigens, including Rv2608, Rv2684, Rv3804c (Ag85A), and Rv0125 (Mtb32A) to design a novel multi-epitope subunit vaccine. By using the Immune Epitopes Database (IEDB), we predicted different B-cell and T-cell epitopes. An adjuvant (Griselimycin) was also added to vaccine construct to improve its immunogenicity. Bioinformatics tools were used to predict, refined, and validate the 3D structure and then docked with toll-like-receptor (TLR-3) using different servers. The constructed vaccine was used for further processing based on allergenicity, antigenicity, solubility, different physiochemical properties, and molecular docking scores. The in silico immune simulation results showed significant response for immune cells. For successful expression of the vaccine in E. coli, in-silico cloning and codon optimization were performed. This research also sets out a good signal for the design of a peptide-based tuberculosis vaccine. In conclusion, our findings show that the known multi-epitope vaccine may activate humoral and cellular immune responses and maybe a possible tuberculosis vaccine candidate. Therefore, more experimental validations should be exposed to it.