Bioinformatics tools have the potential to accelerate research into the design of
vaccines and diagnostic tests by exploiting genome sequences. The aim of this study was to assess whether in silico analysis could be combined with in vitro screening methods to rapidly identify
peptides that are immunogenic during Mycobacterium bovis
infection of cattle. In the first instance the M. bovis-derived
protein ESAT-6 was used as a model
antigen to describe
peptides containing
T-cell epitopes that were frequently recognized across mammalian species, including natural hosts for
tuberculosis (humans and cattle) and small-animal models of
tuberculosis (mice and guinea pigs). Having demonstrated that some
peptides could be recognized by T cells from a number of M. bovis-infected hosts, we tested whether a virtual-matrix-based human prediction program (ProPred) could identify
peptides that were recognized by T cells from M. bovis-infected cattle. In this study, 73% of the experimentally defined
peptides from 10 M. bovis
antigens that were recognized by bovine T cells contained motifs predicted by ProPred. Finally, in validating this observation, we showed that three of five
peptides from the mycobacterial
antigen Rv3019c that were predicted to contain
HLA-DR-restricted
epitopes were recognized by T cells from M. bovis-infected cattle. The results obtained in this study support the approach of using bioinformatics to increase the efficiency of
epitope screening and selection.