Discovery of a multi-epitope tuberculosis vaccine targeting phospholipase C virulence factors: an insilico approach

Phospholipase C enzymes (plcA, plcB, plcC) represent critical virulence determination in mycobacterium tuberculosis pathogenesis. These enzymes play pivotal roles in disrupting phagosomal maturation, inducing macrophage necrosis, and facilitating immune evasion mechanisms. Protein sequences of phospholipase C gene plcA, plcB,plcC were collected and screened against human to exclude homologous matches and minimize cross-reactivity. Linear B-cell epitopes and T cell epitopes were identified and evaluated for the ability to produce strong antigenicity, solubility, toxicity and allergenicity. Suitable segments were linked using EAAK for adjuvant fusion, GPGPG between T-cell epitopes, and AAY between cytotoxin T-cell epitopes, with the L7/L12 ribosomal proteins at N-terminus as immunostimulatory adjuvant, The full vaccine structures were modelled in 3-D and improved for accuracy, and interaction of tuberculosis related proteins were analysed. Immunological potent epitopes for all 3 phospholipase C enzymes with favourable physiochemical properties and structural stability were identified. Immune simulation predicted effective stimulation of both humoral and cell-mediated responses. Molecular docking revealed promising interactions with key targets, characterized by favourable binding energies and stable complex formation dominated by hydrogen bonds and electrostatic interactions, suggesting potential functional efficacy of the designed vaccine. The study presents a strong potential of rationally designed multi-epitope vaccine candidate targeting phospholipase C virulence factor of M. tuberculosis. The computational workflow established a rigorous selection process for immunologically relevant epitopes assembled into chimeric construct with predicted vaccine potential. Further experimental validation through invitro antigenicity assay and in-vivo immunization studies is needed to assess the translational potential of this computationally designed vaccine.