An In-Silico Reverse Vaccinology Approach in Predicting Vaccine Candidate Against Mycobacterium tuberculosis CCDC5180

Abstract

A quarter of the world’s population is thought to be latently infected with Mycobacterium tuberculosis, creating a vast pool of possible future illnesses. Combined with the rising prevalence of multi-drug resistant Mycobacterium tuberculosis (Mtb) strains, TB poses one of the biggest challenges for world health, and the TB epidemic can only be eradicated with a new and more effective vaccine strategy. Since the Bacille Calmette-Guérin (BCG) vaccine is the only one used to treat tuberculosis (TB), Reverse vaccine technology aims to accelerate the development of subunit vaccines by identifying specific proteins in a pathogenic bacterial proteome that may be protective antigens. This approach was employed on four extracellular proteins namely Secreted fibronectin-binding protein C antigen 85-C, PE-PGRS family protein, Invasion protein RipA, and Invasion protein RipB. The shortlisted proteins were subjected to B-cell, CTL, and HTL epitope prediction. To further identify the most promising epitopes, specialized filtering techniques were employed to narrow down CTL epitopes that were non-allergenic, non-toxic, and antigenic as well as B-cell epitopes that produced antibodies. Similarly, HTL epitopes that generate IFN- γ but not IL-10 were selected. The analysis revealed invasion protein RipA as a potential candidate for Mycobacterium tuberculosis CCDC5180 immunization.