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

2024-12-18

arXiv.org

AI Summary

This study addresses the challenge of predicting TCR cross-reactivity toward the same antigenic peptide by uncovering generalizable biophysical rules governing TCR–ligand interactions. We developed a data-driven, structure-aware modeling framework trained on SARS-CoV-2-specific TCR sequences, integrating biophysical features—including stereochemical compatibility (at both peptide-contact and non-contact positions) and hydrophobicity. Contrary to prevailing assumptions, our quantitative analysis reveals that TCR co-specificity is predominantly governed by amino acid stereochemical complementarity rather than hydrophobic preference, with non-contact residues contributing substantially to recognition. The model demonstrates robust generalization across structurally divergent ligands, accurately predicting cross-reactivity toward novel, highly heterogeneous peptides. These findings establish an interpretable, physics-based foundation for rational TCR engineering and therapeutic design.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 red 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.

Problem

• Discover biophysical rules predicting TCR-ligand co-specificity
• Characterize amino-acid differences affecting TCR co-specificity
• Reveal steric properties' dominance over hydrophobic in co-specificity

Innovation

• Optimization framework for TCR co-specificity rules
• Data-driven analysis of amino-acid steric properties
• Generalizable rules for dissimilar ligands