Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction
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s41467-020-18204-2.pdf
Description
Published version
Size
1.91 MB
Format
Adobe PDF
Checksum (MD5)
abf76d8c59f8b74781ec5f600dffd5b7
Author(s)
Rappazzo, C Garrett
Huisman, Brooke D
Birnbaum, Michael E
Date Issued
2020
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2020, The Author(s). 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.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
Massachusetts Institute of Technology. Department of Biological Engineering
Ragon Institute of MGH, MIT and Harvard
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41467-020-18204-2
