Volume 10 Issue 1
Feb.  2024
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Peng Xiong, Anyi Liang, Xunhui Cai, Tian Xia. APTAnet: an atom-level peptide-TCR interaction affinity prediction model. Biophysics Reports, 2024, 10(1): 1-14. doi: 10.52601/bpr.2023.230037
Citation: Peng Xiong, Anyi Liang, Xunhui Cai, Tian Xia. APTAnet: an atom-level peptide-TCR interaction affinity prediction model. Biophysics Reports, 2024, 10(1): 1-14. doi: 10.52601/bpr.2023.230037

APTAnet: an atom-level peptide-TCR interaction affinity prediction model

doi: 10.52601/bpr.2023.230037
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  • Corresponding author: tianxia@hust.edu.cn (T. Xia)
  • Received Date: 14 November 2023
  • Accepted Date: 26 January 2024
  • Available Online: 09 April 2024
  • Publish Date: 29 February 2024
  • The prediction of affinity between TCRs and peptides is crucial for the further development of TIL (Tumor-Infiltrating Lymphocytes) immunotherapy. Inspired by the broader research of drug-protein interaction (DPI), we propose an atom-level peptide-TCR interaction (PTI) affinity prediction model APTAnet using natural language processing methods. APTAnet model achieved an average ROC-AUC and PR-AUC of 0.893 and 0.877, respectively, in ten-fold cross-validation on 25,675 pairs of PTI data. Furthermore, experimental results on an independent test set from the McPAS database showed that APTAnet outperformed the current mainstream models. Finally, through the validation on 11 cases of real tumor patient data, we found that the APTAnet model can effectively identify tumor peptides and screen tumor-specific TCRs.

  • Peng Xiong, Anyi Liang, Xunhui Cai, and Tian Xia declare that they have no conflict of interest.
    This article does not contain any studies with human or animal subjects performed by any of the authors.
    Peng Xiong and Anyi Liang contributed equally to this work.

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