A combined immuno-informatics and structure-based modeling approach for prediction of T cell epitopes of secretory proteins of Mycobacterium tuberculosis

Abstract

The role of secretory proteins of Mycobacterium tuberculosis in pathogenesis and stimulation of specific host responses is well documented. They are also shown to activate different cell types, which subsequently present mycobacterial antigens to T cells. Therefore identification of T cell epitopes from this set of proteins may serve to define candidate antigens with vaccine potential. Fifty-two secretory proteins of M. tuberculosis H37Rv were analyzed computationally for the presence of HLA class I binding nonameric peptides. All possible overlapping nonameric peptide sequences from 52 secretory proteins were generated in silico and analyzed for their ability to bind to 33 alleles belonging to A, B and C loci of HLA class I. Fifteen percent of generated peptides are predicted to bind to HLA with halftime of dissociation T_½≥100 min and 73% of the peptides predicted to bind are mono-allelic in their binding. The structural basis for recognition of nonamers by different HLA molecules was studied employing structural modeling of HLA class I-peptide complexes and there exists a good correlation between structural analysis and binding prediction. Pathogen peptides that could behave as self- or partially self-peptides in the host were eliminated using a comparative study with the human proteome, thus reducing the number of peptides for analysis. The implications of the finding for vaccine development are discussed vis-à-vis the limitations of the use of subunit vaccine and DNA vaccine.