Computational prediction of MHC class I epitopes for most common viral diseases in cattle (Bos taurus).

Abstract

Viral diseases like foot-and-mouth disease (FMD), calf scour (CS), bovine viral diarrhea (BVD), infectious bovine rhinotracheitis (IBR) etc. affect the growth and milk production of cattle (Bos taurus) causing severe economic loss. Epitope-based vaccine designing have been evolved to provide a new strategy for therapeutic application of pathogen-specific immunity in animals. Therefore, identification of major histocompatibility complex (MHC) binding peptides as potential T-cell epitopes is widely applied in peptide vaccine designing and immunotherapy. In this study, MetaMHCI tool was used with seven different algorithms to predict the potential T-cell epitopes for FMD, BVD, IBR and CS in cattle. A total of 54 protein sequences were filtered out from a total set of 6351 sequences of the pathogens causing the said diseases using bioinformatics approaches. These selected protein sequences were used as the key inputs for MetaMHCI tool to predict the epitopes for the BoLA-A11 MHC class I allele of B. taurus. Further, the epitopes were ranked based on a proposed principal component analysis based epitope score (PbES). The best epitope for each disease based on its predictability through maximum number of predictors and low PbES was modeled in PEP-FOLD server and docked with the BoLA-A11 protein for understanding the MHC-epitope interaction. Finally, a total of 78 epitopes were predicted, out of which 27 were for FMD, 25 for BVD, 12 for CS and 14 for IBR. These epitopes could be artificially synthesized and recommended to vaccinate the cattle for the considered diseases. Besides, the methodology adapted here could also be used to predict and analyze the epitopes for other microbial diseases of important animal species.