The use of binding-prediction models to identify M. bovis-specific antigenic peptides for screening assays in bovine tuberculosis.

The identification of MHC class II-restricted antigenic peptides for inclusion into vaccines and/or as diagnostic test reagents for mycobacterial infections remains a high research priority. To expedite discovery of such peptides, numerous bioinformatic tools have been developed to predict whether a given peptide is likely to form a stable binding interaction with MHC class II molecules. However, no prediction tool dedicated to the identification of bovine MHC (BoLA) class II-restricted peptides is currently available. Using experimental immunogenicity data derived from the stimulation of whole blood of Mycobacterium bovis-infected cattle with 105 individual M. bovis-derived peptides, we have compared the ability of a novel BoLA DRB3 structure-based prediction method (Hepitom) with the human MHC class II binding predictor model ProPred in predicting peptides that induce bovine T-cell activation. When a stringent cut off for considering peptide antigenicity was applied, the sensitivities of Hepitom and ProPred in detecting immunogenic peptides were 62% and 77%, respectively. In contrast, the Hepitom model showed greater specificity, with values of 66% and 34% for Hepitom and ProPred, respectively. Using all peptides, seven out of eleven M. bovis proteins were identified as being highly immunogenic. All but one of these antigens were also identified when just the Hepitom predicted peptides were used, while only four of the seven were identified using the ProPred predicted peptides. In conclusion, we demonstrate that the Hepitom model is a useful pre-screening tool to select peptides for further immunogenicity studies in cattle without major impact on the identification of antigenic M. bovis proteins.