Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire.

Autor: Lin X; Center for Theoretical Biological Physics, Rice University, Houston, TX.; Department of Physics and Astronomy, Rice University, Houston, TX.; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA., George JT; Center for Theoretical Biological Physics, Rice University, Houston, TX.; Medical Scientist Training Program, Baylor College of Medicine, Houston, TX., Schafer NP; Center for Theoretical Biological Physics, Rice University, Houston, TX.; Departments of Chemistry, Rice University, Houston, TX., Chau KN; Department of Physics, Northeastern University, Boston, MA., Birnbaum ME; Koch Institute for Integrative Cancer Research, Cambridge, MA.; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA.; Ragon Institute of MIT, MGH, and Harvard, Cambridge, MA., Clementi C; Center for Theoretical Biological Physics, Rice University, Houston, TX.; Departments of Chemistry, Rice University, Houston, TX.; Department of Physics, Freie Universität, Berlin, Germany., Onuchic JN; Center for Theoretical Biological Physics, Rice University, Houston, TX.; Department of Physics and Astronomy, Rice University, Houston, TX.; Departments of Chemistry, Rice University, Houston, TX.; Department of Biosciences, Rice University, Houston, TX., Levine H; Center for Theoretical Biological Physics, Rice University, Houston, TX.; Department of Physics, Northeastern University, Boston, MA.
Jazyk: angličtina
Zdroj: Nature computational science [Nat Comput Sci] 2021 May; Vol. 1 (5), pp. 362-373. Date of Electronic Publication: 2021 May 24.
DOI: 10.1038/s43588-021-00076-1
Abstrakt: Accurate assessment of TCR-antigen specificity at the whole immune repertoire level 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-p-MHC systems. Here, we introduce a pairwise energy model, RACER, for rapid assessment of TCR-peptide affinity at the immune repertoire level. 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 specific TCR-p-MHC system. When applied to simulate thymic selection of an 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 large computational challenge of reliably identifying the properties of tumor antigen-specific T-cells at the level of an individual patient's immune repertoire.
Databáze: MEDLINE
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