Computational approaches in epitope design using DNA binding proteins as vaccine candidate in Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) is a successful pathogen in the history of mankind. A high rate of mortality and morbidity raises the need for vaccine development. Mechanism of pathogenesis, survival strategy and virulence determinant are needed to be explored well for this pathogen. The involvement of DNA binding proteins in the regulation of virulence genes, transcription, DNA replication, repair make them more significant. In present work, we have identified 1453 DNA binding proteins (DBPs) in the 4173 genes of Mtb through the DNABIND tool and they were subjected for further screening by incorporating different bioinformatics tools. The eighteen DBPs were selected for the B-cell epitope prediction by using ABCpred server. Moreover, the B-cell epitope bearing the antigenic and non- allergenic property were selected for T-cell epitope prediction using ProPredI, and ProPred server. Finally, DGIGSAVSV (Rv1088), IRALPSSRH (Rv3923c), LTISPIANS (Rv3235), VQPSGKGGL (Rv2871) VPRPGPRPG (Rv2731) and VGQKINPHG (Rv0707) were identified as T-cell epitopes. The structural modelling of these epitopes and DBPs was performed to ensure the localization of these epitopes on the respective proteins. The interaction studies of these epitopes with human HLA confirmed their validation to be used as potential vaccine candidates. Collectively, these results revealed that the DBPs- Rv2731, Rv3235, Rv1088, Rv0707, Rv3923c and Rv2871 are the most appropriate vaccine candidates. In our knowledge, it is the first report of using the DBPs of Mtb for epitope prediction. Significantly, this study also provides evidence to be useful for designing a peptide-based vaccine against tuberculosis.