Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction
Year: 2020
Type: article
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.
Source: Nature Communications
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,380
Funders: Schmidt Futures, David and Lucile Packard Foundation, U.S. Department of Health and Human Services, V Foundation for Cancer Research, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Monash University, National Cancer Institute, National Institute of General Medical Sciences
OpenAlex ID: W3083537918