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

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外文摘要：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.

外文关键词：

ImmunotherapyTCRAntigenNatural language processingTransfer learning

作者：

Peng Xiong、Anyi Liang、Xunhui Cai、Tian Xia

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作者单位：

School of Artificial Intelligence and Automation,Huazhong Uni-versity of Science and Technology,Wuhan 430074,China

Institute of Pathology,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,China

School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China

基金：

国家自然科学基金

项目编号：

61571202

出版年：

2024

DOI：

10.52601/bpr.2023.230037

生物物理学报告(英文)

ISSN：

年,卷(期)：2024.10(1)