Scrutinizing Mycobacterium tuberculosis membrane and secretory proteins to formulate multiepitope subunit vaccine against pulmonary tuberculosis by utilizing immunoinformatic approaches.

Tuberculosis is a menacing disease caused eminently to the people inhabiting the tropical and sub-tropical nations. A holistic approach is required to generate T and B memory cells to effectuate a long-term exemption from the pulmonary tuberculosis. In this study, immunoinformatic approaches were used to design a multi-epitope-based subunit vaccine for pulmonary tuberculosis which may improve human immune system. The various B-cell, TH cell and TC cell binding epitopes were predicted for selected 2 membrane and 12 secretory proteins of Mycobacterium tuberculosis. The final vaccine construct was assembled by merging the predicted epitope sequences and an adjuvant at the N-terminal of the construct. Furthermore, the physiochemical characterization was done to check the molecular weight, aliphatic index, theoretical PI, hydropathicity and thermostable nature of the designed vaccine. The construct was a potential antigen while wasn't allergenic in nature. Tertiary modeling was performed, by filtering them a refined model was chosen and was docked with TLR-4 (immune receptor). Molecular docking and dynamic simulation was performed and the microscopic interaction between the vaccine construct (ligand) and TLR-4 receptor complex was verified. In silico cloning was used to fortify the expression and translation efficiency of the vaccine within an expression vector.