A computational resource for the prediction of peptide binding to Indian rhesus macaque MHC class I molecules

Non-human primates, in general, and Indian rhesus macaques, specifically, play an important role in the development and testing of vaccines and diagnostics destined for human use. To date, several frequently expressed macaque MHC molecules have been identified and their binding specificities characterized in detail. Here, we report the development of computational algorithms to predict peptide binding and potential T cell epitopes for the common MHC class I alleles Mamu-A*01, -A*02, -A*11, -B*01 and -B*17, which cover approximately two thirds of the captive Indian rhesus macaque populations. We validated this method utilizing an SIV derived data set encompassing 59 antigenic peptides. Of all peptides contained in the SIV proteome, the 2.4% scoring highest in the prediction contained 80% of the antigenic peptides. The method was implemented in a freely accessible and user friendly website at . Thus, we anticipate that our approach can be utilized to rapidly and efficiently identify CD8+ T cell epitopes recognized by rhesus macaques and derived from any pathogen of interest.