Abstract
As Bacille Calmette-Guérin (BCG) vaccine's effectiveness is limited to only children, the development of new tuberculosis (TB) vaccines is being studied using several platforms, and a novel TB vaccine that overcomes this limitation is required. In this study, we designed an effective multi-epitope vaccine against Mycobacterium tuberculosis using immunoinformatic analysis. First, we selected 11 highly antigenic proteins based on previous research: Ag85A, Ag85B, Ag85C, ESAT-6, MPT64, Rv2660c, TB10.4, HspX, GlfT2, Fas, and IniB. Among these antigens, 10 linear B-cell epitopes, 9 helper T-cell epitopes, and 16 cytotoxic T-cell epitopes were predicted to design the multi-epitope vaccine. To improve the immunogenicity of the candidate vaccine, three different adjuvants, griselimycin, human beta-defensin 3 (HBD3), and 50s ribosomal protein (50sRP), were attached with linker sequences to the vaccine model. The immunogenic, antigenic, allergenic, and physicochemical properties of the resulting designed multi-epitope vaccines were predicted in silico. Moreover, 3D structural modeling, refinement, and validation were used to select a model for further evaluation. Molecular docking analysis revealed a consistent and significant binding affinity of the candidate vaccine for toll-like receptors (TLRs), TLR-2, -3, and -4. Immune simulation performed using C-ImmSim demonstrated that three rounds of immunization with multi-epitope vaccines induced a high production of cytokines and immunoglobulins related with both cellular and humoral immune response. Moreover, we constructed vaccine candidate composed of 50sRP and evaluated its immunogenicity in a mouse model. Consequently, this in silico-engineered multi-epitope structure can elicit adaptive immune responses and represents a promising novel candidate for TB vaccine development.