Data-driven Discovery of Biophysical T Cell Receptor Co-specificity Rules

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 co-specificity 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 matching of steric properties between substituted amino acids is more important for receptor co-specificity 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.

18 pages, 12 figures (accepted in PRX Life)

References in corpus (6)

• Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models
• Learning from positive and unlabeled data: a survey
• Statistical inference of the generation probability of T-cell receptors from sequence repertoires
• Quantifying selection in immune receptor repertoires
• Population variability in the generation and thymic selection of T-cell repertoires
• Limits on Inferring T-cell Specificity from Partial Information