Machine learning optimization of peptides for presentation by class II MHCs
Name
btab131.pdf
Description
Published version
Size
1.72 MB
Format
Adobe PDF
Checksum (MD5)
fb00f900a386c4bf9a56790dd7c76bbe
Author(s)
Dai, Zheng
Huisman, Brooke D
Zeng, Haoyang
Carter, Brandon
Jain, Siddhartha
Birnbaum, Michael E
Gifford, David K
Date Issued
2021
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Version
Final published version
Abstract
Abstract
Summary 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 information Supplementary data are available at Bioinformatics online.
Summary 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 information Supplementary data are available at Bioinformatics online.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Biological Engineering
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1093/BIOINFORMATICS/BTAB131
