Identifying T cell antigen at the atomic level with graph convolutional network

Jinhao Que, Guangfu Xue, Tao Wang, Xiyun Jin, Zuxiang Wang, Yideng Cai, Wenyi Yang, Meng Luo, Qian Ding, Jinwei Zhang, Yilin Wang, Yuexin Yang, Fenglan Pang, Yi Hui, Zheng Wei, Jun Xiong, Shouping Xu, Yi Lin, Haoxiu Sun (), Pingping Wang (), Zhaochun Xu () and Qinghua Jiang ()
Additional contact information
Jinhao Que: Harbin Institute of Technology
Guangfu Xue: Harbin Institute of Technology
Tao Wang: Northwestern Polytechnical University
Xiyun Jin: Harbin Medical University
Zuxiang Wang: Harbin Medical University
Yideng Cai: Harbin Institute of Technology
Wenyi Yang: Harbin Institute of Technology
Meng Luo: Harbin Institute of Technology
Qian Ding: Harbin Institute of Technology
Jinwei Zhang: Harbin Institute of Technology
Yilin Wang: Harbin Institute of Technology
Yuexin Yang: Harbin Institute of Technology
Fenglan Pang: Harbin Institute of Technology
Yi Hui: Harbin Institute of Technology
Zheng Wei: Harbin Institute of Technology
Jun Xiong: Harbin Institute of Technology
Shouping Xu: Harbin Medical University Cancer Hospital
Yi Lin: Harbin Medical University
Haoxiu Sun: Harbin Medical University
Pingping Wang: Harbin Medical University
Zhaochun Xu: Harbin Medical University
Qinghua Jiang: Harbin Institute of Technology

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract Precise identification of T cell antigens in silico is crucial for the development of cancer mRNA vaccines. However, current computational methods only utilize sequence-level rather than atomic level features to identify T cell antigens, which results in poor representation of those that activate immune responses. Here we propose deepAntigen, a graph convolutional network-based framework, to identify T cell antigens at the atomic level. deepAntigen achieves excellent performance both in the prediction of antigen-human leukocyte antigen (HLA) binding and antigen-T cell receptor (TCR) interactions, which can provide comprehensive guidance for identification of T cell antigens. The tumor neoantigens predicted by deepAntigen in lung, breast and pancreatic cancer patients are experimentally validated through ELISPOT assays, which detect successful activation of CD8+ T cells to release IFN-γ. Overall, deepAntigen can accurately identify T cell antigens at the atomic level, which could accelerate the development of personalized neoantigen targeted immunotherapies for cancer patients.

Date: 2025
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DOI: 10.1038/s41467-025-60461-6

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