Leveraging immunoinformatic techniques for the development of a multi-epitope subunit vaccine targeting virulence-associated proteins of Mycobacterium tuberculosis
Among infectious diseases, tuberculosis is the biggest cause of death worldwide. Thus, controlling TB, a global epidemic, is a major public health concern on a worldwide scale. According to epidemiological modelling, a new vaccination that can prevent TB, especially in adults and adolescents, is necessary to control the disease’s spread, even while medication therapies for the disease are still improving. The development of novel vaccines with broader applicability and improved, long-lasting effectiveness becomes increasingly crucial as strains resistant to various drugs emerge. This work designed a multi-epitope subunit vaccine (MESV) candidateusing immunoinformatic approaches that targets five virulence-associated proteins, Rv0227c, Rv0584, Lipoprotein LprA, Phospholipase C, and Antigen 85 C. Antigenicity, non-allergenicity, and non-toxicity were the criteria used to identify and filter potential CTL, HTL, and B-cell epitopes. High-affinity epitopes connected with suitable spacers and an adjuvant for improved immunogenicity were included in the carried-out vaccine formulation. Validation and structural modelling verified the MESV’s quality and stability. TLR2 and TLR4 receptors have strong interactions, which were further demonstrated through molecular docking, and molecular dynamics simulations showed low fluctuations and good structural stability. Further, the MMPBSA analysis was introduced to determine the binding affinity of the vaccine construct, and a more pronounced binding affinity was established by the designed vaccine with TLR4. Increased cytokine release, memory cell development, and the developed MESV candidateare viable options warranting further experimental validation, as it may elicit potent humoral and cellular immune responses against M. tuberculosis.