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Data-Driven Discovery of Biophysical T Cell Receptor Cospecificity Rules

Andrew G. T. Pyo1, Yuta Nagano2,3, Martina Milighetti2,4, James Henderson2,5, Curtis G. Callan, Jr.1,6, Benny Chain2,7, Ned S. Wingreen8,9, and Andreas Tiffeau-Mayer2,5

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PRX Life 3, 033005 – Published 15 July, 2025

DOI: https://doi.org/10.1103/14j1-wrh5

Abstract

The biophysical interactions between the T cell receptor (TCR) and its ligands determine the specificity of the cellular immune response. However, the immense diversity of receptors and ligands has made it challenging to discover generalizable rules across the distinct binding affinity landscapes created by different ligands. Here, we present an optimization framework for discovering biophysical rules that predict whether TCRs share specificity to a ligand. Applying this framework to TCRs associated with a collection of SARS-CoV-2 peptides, we systematically characterize how cospecificity depends on the type and position of amino-acid differences between receptors. We also demonstrate that the inferred rules generalize to ligands highly dissimilar to any seen during training. Our analysis reveals that the matching of steric properties between substituted amino acids is more important for receptor cospecificity than the hydrophobic properties that prominently determine evolutionary substitutability. Our analysis also quantifies the substantial importance of positions not in direct contact with the peptide for specificity. These findings highlight the potential for data-driven approaches to uncover the molecular mechanisms underpinning the specificity of adaptive immune responses.

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PRX Life 3, 033005– Published 15 July, 2025
  • Received 19 December 2024
  • Accepted 18 June 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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