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<p>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<sup>+</sup> 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月4日

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  • 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,
  • Qinghua Jiang

摘要

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.