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

Zhao, Yu, He, Bing, Xu, Fan, Li, Chen, Xu, Zhimeng, Su, Xiaona, He, Haohuai, Huang, Yueshan, Rossjohn, Jamie ORCID: https://orcid.org/0000-0002-2020-7522, Song, Jiangning and Yao, Jianhua 2023. DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis. Science Advances 9 (32) 10.1126/sciadv.abo5128

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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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Medicine
Publisher:	American Association for the Advancement of Science
ISSN:	2375-2548
Date of First Compliant Deposit:	5 October 2023
Date of Acceptance:	6 July 2023
Last Modified:	07 Oct 2023 03:29
URI:	https://orca.cardiff.ac.uk/id/eprint/162981

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