DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis.

Journal: Science advances
PMID: 37556545

Abstract

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson's correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.

Authors

  • Yu Zhao
    College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.
  • Bing He
    School of Business, Jiangsu Ocean University, Haizhou District, Lianyungang, Jiangsu, China.
  • Fan Xu
    Department of Public Health, Chengdu Medical College, Sichuan, China.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Zhimeng Xu
    AI Lab, Tencent, Shenzhen, China.
  • Xiaona Su
    AI Lab, Tencent, Shenzhen, China.
  • Haohuai He
    School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China.
  • Yueshan Huang
    AI Lab, Tencent, Shenzhen, China.
  • Jamie Rossjohn
    Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia.
  • Jiangning Song
    College of Information Engineering, Northwest A&F University, Yangling 712100, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia, National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China, Centre for Research in Intelligent Systems, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia College of Information Engineering, Northwest A&F University, Yangling 712100, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia, National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China, Centre for Research in Intelligent Systems, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia College of Information Engineering, Northwest A&F University, Yangling 712100, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia, National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China, Centre for Research in Intelligent Systems, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia.
  • Jianhua Yao

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