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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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TCR representation learning with protein language models: a comprehensive review.

Kyohei Kinoshita¹, Tetsuya J Kobayashi^1,2,3

¹Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

International Immunology

|August 26, 2025

Summary

Protein language models (PLMs) offer a powerful new approach to analyze the T cell receptor (TCR) repertoire, improving immune profiling and disease diagnosis. These models overcome limitations of traditional methods, enabling better prediction of TCR function.

Keywords:

AIRR TCR repertoire antigen specificity prediction self-supervised learning transfer learning

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Published on: November 3, 2011

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

The T cell receptor (TCR) repertoire provides insights into immune status and infection history.
Analyzing the TCR repertoire is complex due to its diversity and heterogeneity.
Current clinical applications of TCR repertoire analysis are emerging.

Purpose of the Study:

To review advances in protein language models (PLMs) for TCR repertoire analysis.
To compare PLM-based approaches with conventional methods for TCR data representation and analysis.
To highlight the potential of PLMs in predicting TCR antigen specificity and enabling clinical applications.

Main Methods:

Review of protein language models (PLMs) applied to T cell receptor (TCR) sequences.
Description of methods for TCR data representation and PLM training.
Comparison of supervised deep learning models with PLM-based approaches for antigen specificity prediction.

Main Results:

PLMs capture context-dependent features from large TCR datasets.
PLMs demonstrate high generalization performance via transfer learning, even with limited labeled data.
PLM-based approaches show promise for antigen specificity prediction, outperforming conventional methods.

Conclusions:

PLMs offer significant advantages over traditional sequence representation methods for TCR repertoire analysis.
Challenges remain, including data scarcity, bias, and lack of paired-chain information.
Future work should focus on dataset optimization, PLM interpretability, and multimodal approaches for enhanced clinical applications.