Learning the principles of T cell antigen discernment

TL;DR

This paper reviews theoretical models and recent experimental advances in understanding how T cell receptors discriminate antigens, highlighting complex recognition mechanisms and the potential for improved immunotherapy design.

Contribution

It synthesizes current theoretical frameworks with modern data and computational methods, emphasizing adaptive kinetic proofreading and machine learning in T cell antigen recognition.

Findings

Antigen potency exhibits nonlinear effects in complex mixtures.

High-throughput data enables model refinement through machine learning.

Theoretical models are advancing understanding of immune decision-making.

Abstract

T cells are central to the adaptive immune response, capable of detecting pathogenic antigens while ignoring healthy tissues with remarkable specificity and sensitivity. Quantitatively understanding how T cell receptors (TCRs) discriminate among antigens requires biophysical models and theoretical analysis of signaling networks. Here, we review current theoretical frameworks of antigen recognition in the context of modern experimental and computational advances. Antigen potency spans a continuum and exhibits nonlinear effects within complex mixtures, challenging discrete classification and simple threshold-based models. This complexity motivates the development of models such as adaptive kinetic proofreading, which integrate both activating and inhibitory signals. Advances in high-throughput technologies now generate large-scale, quantitative datasets, enabling the refinement of such models through statistical and machine learning approaches. This convergence of theory, data, and computation promises deeper insights into immune decision-making and opens new avenues for rational immunotherapy design.