Analysis of the performance of peptide predicting methods using MHC class I peptide sequencing in the C57BL/6 mouse (78.11)

Abstract

Abstract MHC class I peptide-binding motifs can defined by sequencing of peptides naturally bound to MHC class I molecules. More recently MHC class I-peptide binding motifs have be defined on the basis of quantitative MHC-peptide binding assays in vitro using peptide libraries. The information on peptide binding motifs obtained using binding assays have been useful to develop numerous bioinformatics-based tools to predict the binding of peptides to MHC class I molecules. To date few studies have analyzed the performance of these bioinformatics tools to predict peptides determined by sequencing of peptides eluted directly from MHC class I molecules. In this study we performed sequencing of endogenous peptides eluted from H2Kb and Db molecules expressed in the C57BL/6 mouse. Using data from 280 H2-class I-bound peptides we identified novel preferred anchor residues located in H2Kb and H2Db-associated peptides. When comparing the predicting affinity performance of 3 bioinformatics tools we found that between 60 to 95% of peptides eluted from H2Kb and H2Db molecules were correctly classified as binders by these 3 predicting algorithms, suggesting that these bioinformatics tools are reliable and efficient for prediction of naturally processed MHC class I ligands. These results expand our knowledge about H2 class I binding specificities and provide the identity of positions that can be used for better characterization of H2 class I peptide-binding motifs. Similar studies using sensitive methods for sequencing large numbers of peptides from other MHC molecules are needed to establish more precise motif information that can be used for the training of bioinformatics tool employed in the prediction of CD8 T-cell epitopes