Computational Prediction of B-cell Epitopes of Mycobacterium tuberculosis-Implications in Vaccine Design

Abstract

Introduction: Tuberculosis (TB) is a communicable disease caused by Mycobacterium tuberculosis. Bacillus Calmette-Guérin is the only vaccine available for TB. However, although the vaccine effectively protects children from TB, its efficacy in adults is still debatable. No effective vaccine is presently available to prevent TB. An effective vaccine should provoke humoral immunity to prevent the adhesion of M. tuberculosis to macrophages. In this context, B-cell epitopes may play an important role in vaccine development. Hence, this study aimed to identify B-cell epitopes using in silico tools. Materials and Methods: In this study, B-cell epitopes were predicted using two tools (ABCPred and BCPREDS), which consists of three methods (artificial neural networks, BCPred, and AAP). Further, the epitopes predicted by the three prediction methods were analyzed for overlapping, and the ToxinPred, VaxiJen and AllerTop servers were used for analysis. Results: A total of 2003 epitopes were predicted using all the prediction methods. Among these, 80 epitopes were predicted as overlapping epitopes, and 80, 57, and 29 epitopes were screened using the ToxinPred, VaxiJen, and AllerTop tools, respectively. Conclusion: The epitopes predicted in the current study needs to be further validated using in vitro and in vivo analyses for B-cell response toward infection by M. tuberculosis.

Keywords

• epitopes
• b-cell
• mycobacterium tuberculosis
• vaccine