A Strategy for the Identification of Canonical and Non‐canonical MHC I‐binding Epitopes Using an ANN‐based Epitope Prediction Algorithm

Abstract

Small peptides bound by Major Histocompatibility Complex (MHC) class I molecules and recognized in this context by the T-cell receptor of CD8 þ T cells are known as T-cell epitopes and are of extraordinary importance for the development of new vaccines against cancer and viral infections. Several algorithms predicting a peptides binding capability to a given MHC class I molecule are currently available and have been successfully applied in the identification of new T-cell epitopes within proteins. Most of these newly identified epitopes obey to the empirically determined anchor residue patterns that are specific for the different MHC I alleles. However, in recent studies an increasing number of weakly binding T-cell epitopes could be identified that do not fit to these canonical amino acid patterns. Therefore there is a need for new prediction algorithms improving the prediction accuracy for weakly binding epitopes that are biologically relevant as they are presented by, e.g. antigen presenting cells. Here we describe the development and application of an Artificial Neural Network (ANN)-based T-cell epitope prediction strategy for the MHC class I allele HLA-A*0201, leading to the identification of seven new melanoma-associated T-cell epitopes, that do not fit to the canonical HLA-A*0201 recognition motif. In this strategy the ANN-based epitope prediction is combined with a filter algorithm that incorporates knowledge of peptide chemistry and a ranking procedure that enables the selection of candidate epitopes according to protein family and phylogenetic conservation.