Deciphering small sequence differences in T cell receptor-antigen pairing

T cells have important functions in development and disease processes through T cell receptor (TCR)-dependent activities. Many tools were developed to predict the binding between TCRs and antigens. However, one of the uncertainties is whether such tools can decipher how small changes in the TCRs or antigenic peptides contribute to binding. We develop a deep learning model, pMTnet-omni, which not only predicts the binding vs. non-binding of TCRs towards pMHCs, but also distinguishes the stronger vs. weaker binding of TCRs similar in sequence. We leverage this capability to interpret the biological rules that govern TCR-antigen pairing. This also enables pMTnet-omni to accurately predict variant TCRs with desired stronger or weaker binding to the antigen, in conjunction with a Lab-in-the-Loop (LiL) mechanism. We show that pMTnet-omni can also predict binding of TCRs towards similar pMHCs. Overall, we provide a flexible toolkit for research and translational applications involving antigens and TCRs. Computational prediction of peptide-MHC (pMHC) antigen binding to T cell receptors (TCR) is useful for T cell-based therapeutic development and predicting responses to immunotherapies. Here the authors use a deep learning model pMTnet-omni to predict the binding versus non-binding of TCRs to pMHC antigens and then use this model to tune the affinity of TCRs against antigens.

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Publication Details

Journal
Nature Communications
Published
2026-06-13
DOI
https://doi.org/10.1038/s41467-026-73396-3
Primary Topic
T-cell and B-cell Immunology
Type
article
Field-Weighted Citation Impact
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article

Deciphering small sequence differences in T cell receptor-antigen pairing

Fangjiang Wu, Don L. Gibbons, Donghan M. Yang, Cassian Yee et al.
Nature Communications
T-cell and B-cell Immunology
article

Deciphering small sequence differences in T cell receptor-antigen pairing

Fangjiang Wu, Don L. Gibbons, Donghan M. Yang, Cassian Yee, Sawsan Rashdan, Minying Zhang, Ke Pan, Indu Raman, Yanhua Tian, Yafang Deng, John Victor Heymach, Yaming Xue, Shailbala Singh, David E. Gerber, Yifei Hu, Yuqiu Yang, Junzhou Huang, David Hsiehchen, Jade Homsi, Mitchell S. von Itzstein, Chao Cheng, Yvonne Gloria-McCutchen, Olivia Xiao, Mary Gwin, Alexandre Reuben, Chengsong Zhu, Jonathan E. Dowell, Casey Bermack, Jianjun Zhang, Jialiang Liu, Tao Wang, Chaoying Liang, Xiongbin Kang, Farjana J. Fattah, Peixin Jiang, Yi Han, James Zhu, Shengjie Yang
article en

Abstract

T cells have important functions in development and disease processes through T cell receptor (TCR)-dependent activities. Many tools were developed to predict the binding between TCRs and antigens. However, one of the uncertainties is whether such tools can decipher how small changes in the TCRs or antigenic peptides contribute to binding. We develop a deep learning model, pMTnet-omni, which not only predicts the binding vs. non-binding of TCRs towards pMHCs, but also distinguishes the stronger vs. weaker binding of TCRs similar in sequence. We leverage this capability to interpret the biological rules that govern TCR-antigen pairing. This also enables pMTnet-omni to accurately predict variant TCRs with desired stronger or weaker binding to the antigen, in conjunction with a Lab-in-the-Loop (LiL) mechanism. We show that pMTnet-omni can also predict binding of TCRs towards similar pMHCs. Overall, we provide a flexible toolkit for research and translational applications involving antigens and TCRs. Computational prediction of peptide-MHC (pMHC) antigen binding to T cell receptors (TCR) is useful for T cell-based therapeutic development and predicting responses to immunotherapies. Here the authors use a deep learning model pMTnet-omni to predict the binding versus non-binding of TCRs to pMHC antigens and then use this model to tune the affinity of TCRs against antigens.

Nature Communications
The University of Texas MD Anderson Cancer Center (US), Baylor College of Medicine (US), The University of Texas at Arlington (US), Southwestern Medical Center (US), Houston Institute for Clinical Research (US), Southwestern Medical Center (US), The University of Texas Southwestern Medical Center (US)
Openalex Percentile: Top 9%
T-cell and B-cell Immunology
DOIOpen Access PDFOpenAlex
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