Structural T‐Cell Receptor Analysis in the Age of Machine Learning

Abstract

The development of highly accurate deep learning models for protein structure prediction has transformed the landscape of T‐cell receptor (TCR) structure data, which can now be accessed at repertoire scale. We provide a perspective on the growing field of structural TCR immunoinformatics, summarizing core principles of TCR structural biology and highlighting existing resources and tools. We outline computational methods for TCR structure prediction, and discuss outstanding challenges faced by current tools, as well as potential avenues to address these. We expand on the research enabled by the availability of predicted TCR structures, exploring the utility of TCR structure predictions for computationally inferring TCR specificity, as well as summarizing opportunities emerging from the adjacent field of antibody research. Finally, we provide a forward‐looking perspective on the advances in deep learning research which have recently enabled computational design of TCRs and TCR‐like binders.