Rapid assessment of T-cell receptor specificity of the immune repertoire
Author(s)
Lin, Xingcheng
George, Jason T.
Schafer, Nicholas P.
Ng Chau, Kevin
Birnbaum, Michael E
Clementi, Cecilia
Onuchic, José N.
Levine, Herbert
Date Issued
May 2021
Journal
Nature Computational Science
Publisher
Springer Science and Business Media LLC
Citation
Lin, Xingcheng et al. "Rapid assessment of T-cell receptor specificity of the immune repertoire." Nature Computational Science 1, 5 (May 2021): 362–373. © 2021 The Author(s)
Version
Author's final manuscript
Abstract
Accurate assessment of T-cell-receptor (TCR)–antigen specificity across the whole immune repertoire lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR–peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR–peptide systems. Here, we introduce RACER, a pairwise energy model capable of rapid assessment of TCR–peptide affinity for entire immune repertoires. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR–peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each TCR–peptide system. When applied to simulate thymic selection of a major-histocompatibility-complex (MHC)-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the computational challenge of reliably identifying properties of tumor antigen-specific T-cells at the level of an individual patient’s immune repertoire.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Koch Institute for Integrative Cancer Research at MIT
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1038/s43588-021-00076-1
