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Machine learning optimization of peptides for presentation by class II MHCs.

ABSTRACT: T cells play a critical role in cellular immune responses to pathogens and cancer and can be activated and expanded by 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 information: Supplementary data are available at Bioinformatics online.

SUBMITTER: Dai Z

PROVIDER: S-EPMC8504626 | biostudies-literature |

REPOSITORIES: biostudies-literature

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