Machine learning optimization of peptides for presentation by class II MHCs.
basic_science · Level V
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- Record sourced from PubMed, PMID 33705522.
- Also identified by DOI 10.1093/bioinformatics/btab131 and PMC identifier 8504626.
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Abstract
T cells play a critical role in cellular immune responses to pathogens and cancer and can be activated and expanded by Major Histocompatibility Complex (MHC)-presented antigens contained in peptide vaccines. We present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying their anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization improves peptide binding. Supplementary data are available at Bioinformatics online.