The discovery of novel
antigens is an essential requirement in devising new diagnostics or
vaccines for use in control programmes against human
tuberculosis (TB) and
bovine tuberculosis (bTB). Identification of potential
epitopes recognised by CD4+ T cells requires prediction of
peptide binding to MHC class-II, an obligatory prerequisite for T cell recognition. To comprehensively prioritise potential MHC-II-binding
epitopes from Mycobacterium bovis, the agent of bTB and zoonotic TB in humans, we integrated three binding prediction methods with the M. bovisproteome using a subset of human HLA alleles to approximate the binding of
epitope-containing
peptides to the bovine
MHC class II molecule BoLA-DRB3. Two parallel strategies were then applied to filter the resulting set of binders: identification of the top-scoring binders or clusters of binders. Our approach was tested experimentally by assessing the capacity of predicted promiscuous
peptides to drive
interferon-γ secretion from T cells of M. bovis infected cattle. Thus, 376 20-mer
peptides, were synthesised (270 predicted
epitopes, 94 random
peptides with low predictive scores and 12 positive controls of known
epitopes). The results of this validation demonstrated significant enrichment (>24 %) of promiscuously recognised
peptides predicted in our selection strategies, compared with randomly selected
peptides with low prediction scores. Our strategy offers a general approach to the identification of promiscuous
epitopes tailored to target populations where there is limited knowledge of MHC allelic diversity.