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

C. Garrett Rappazzo, Brooke D. Huisman and Michael E. Birnbaum ()
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C. Garrett Rappazzo: Koch Institute for Integrative Cancer Research
Brooke D. Huisman: Koch Institute for Integrative Cancer Research
Michael E. Birnbaum: Koch Institute for Integrative Cancer Research

Nature Communications, 2020, vol. 11, issue 1, 1-14

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-18204-2

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