Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction

CD4 + helper T cells contribute important functions to the immune response during pathogen infection and tumor formation by recognizing antigenic peptides presented by class II major histocompatibility complexes (MHC-II). While many computational algorithms for predicting peptide binding to MHC-II proteins have been reported, their performance varies greatly. Here we present a yeast-display-based platform that allows the identification of over an order of magnitude more unique MHC-II binders than comparable approaches. These peptides contain previously identified motifs, but also reveal new motifs that are validated by in vitro binding assays. Training of prediction algorithms with yeast-display library data improves the prediction of peptide-binding affinity and the identification of pathogen-associated and tumor-associated peptides. In summary, our yeast-display-based platform yields high-quality MHC-II-binding peptide datasets that can be used to improve the accuracy of MHC-II binding prediction algorithms, and potentially enhance our understanding of CD4 + T cell recognition. Identifying peptides that can bind major histocompatibility complex II (MHC-II) is important for our understanding of T cell immunity and specificity. Here the authors present a yeast-display library screening approach that identifies more potential binders than various reported algorithms to help expand our understanding for antigen presentation.