TCR representation learning with protein language models: a comprehensive review

The T-cell receptor (TCR) repertoire is a valuable source of information that reflects an individual's immune status and infection history. However, due to the exceptional diversity and complexity of the TCR repertoire, predicting its functional properties remains a challenging task. This review summarizes recent advances in protein language models (PLMs), which apply natural language processing techniques to protein sequences, focusing specifically on TCR repertoire analysis. We begin by outlining the biological basis of the TCR repertoire and its current clinical applications. We then describe the methods used for representing TCR data and the training procedures of the corresponding PLMs. PLMs capture context-dependent features from large unlabeled TCR datasets and achieve high generalization performance even with limited labeled data through transfer learning. In this respect, PLMs offer significant advantages over conventional sequence representation methods. We highlight antigen specificity prediction as a key application, comparing supervised deep learning models with PLM-based approaches. While employment of PLMs is promising, TCR repertoire analysis still faces challenges such as data scarcity, bias, and lack of paired-chain information. Addressing these challenges requires rigorous dataset optimization, integration, and augmentation strategies. Future advances will require better interpretation of the representations learned by PLMs and the development of multimodal approaches that integrate structural information. These advances could enable several clinical applications, including disease diagnosis, vaccine development, and personalized immune profiling.