Highly accurate carbohydrate-binding site prediction with DeepGlycanSite

Xinheng He, Lifen Zhao, Yinping Tian, Rui Li, Qinyu Chu, Zhiyong Gu, Mingyue Zheng, Yusong Wang, Shaoning Li, Hualiang Jiang, Yi Jiang, Liuqing Wen (), Dingyan Wang () and Xi Cheng ()
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Xinheng He: Chinese Academy of Sciences
Lifen Zhao: Chinese Academy of Sciences
Yinping Tian: Chinese Academy of Sciences
Rui Li: Chinese Academy of Sciences
Qinyu Chu: Hangzhou Institute of Advanced Study
Zhiyong Gu: Hangzhou Institute of Advanced Study
Mingyue Zheng: Chinese Academy of Sciences
Yusong Wang: Xi’an Jiaotong University
Shaoning Li: The Chinese University of Hong Kong
Hualiang Jiang: Chinese Academy of Sciences
Yi Jiang: Lingang Laboratory
Liuqing Wen: Chinese Academy of Sciences
Dingyan Wang: Lingang Laboratory
Xi Cheng: Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5’-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.

Date: 2024
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DOI: 10.1038/s41467-024-49516-2

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