中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 上海药物研究所
  3. 中国科学院上海药物研究所
  4. 新药研究国家重点实验室
Highly accurate carbohydrate-binding site prediction with DeepGlycanSite

文献类型：期刊论文

作者He, Xinheng7,8,9; Zhao, Lifen8,9; Tian, Yinping8,9; Li, Rui6,8,9; Chu, Qinyu5; Gu, Zhiyong5; Zheng, Mingyue5,7,8,9; Wang, Yusong3,4; Li, Shaoning2; Jiang, Hualiang1,5,7,8,9
刊名NATURE COMMUNICATIONS
出版日期2024-06-17
卷号15期号:1页码:13
DOI10.1038/s41467-024-49516-2
通讯作者Wen, Liuqing(lwen@simm.ac.cn) ; Wang, Dingyan(wangdy@lglab.ac.cn) ; Cheng, Xi(xicheng@simm.ac.cn)
英文摘要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. Carbohydrates are essential for regulating various biological processes. Here, the authors developed DeepGlycanSite, a deep learning model that accurately predicts carbohydrate-binding sites on proteins, offering insights into carbohydrate regulation of therapeutically important proteins.
WOS关键词MOLECULAR-DYNAMICS ; PROTEIN-STRUCTURE ; CD22 LIGANDS ; INFLUENZA ; SIALIDASE ; DOCKING ; SYSTEM ; LEADS
资助项目Shanghai Municipal Science and Technology Major Project ; National Key Research and Development Program of China[2021YFA1301900] ; Youth Innovation Promotion Association[2022077] ; Lingang Laboratory[LG202102-01-01]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001249940500024
出版者NATURE PORTFOLIO
源URL[http://119.78.100.183/handle/2S10ELR8/311890]  
专题新药研究国家重点实验室
通讯作者Wen, Liuqing; Wang, Dingyan; Cheng, Xi
作者单位1.Lingang Lab, Shanghai, Peoples R China
2.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China
3.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian, Peoples R China
4.Xi An Jiao Tong Univ, Natl Engn Res Ctr Visual Informat & Applicat, Natl Key Lab Human Machine Hybrid Augmented Intell, Xian, Peoples R China
5.Hangzhou Inst Adv Study, Sch Pharmaceut Sci & Technol, Hangzhou, Peoples R China
6.China Pharmaceut Univ, Sch Pharm, Nanjing, Peoples R China
7.Univ Chinese Acad Sci, Beijing, Peoples R China
8.Chinese Acad Sci, Shanghai Inst Mat Med, Carbohydrate Based Drug Res Ctr, State Key Lab Chem Biol, Shanghai, Peoples R China
9.Chinese Acad Sci, State Key Lab Drug Res, Shanghai, Peoples R China
推荐引用方式
GB/T 7714		 He, Xinheng,Zhao, Lifen,Tian, Yinping,et al. Highly accurate carbohydrate-binding site prediction with DeepGlycanSite[J]. NATURE COMMUNICATIONS,2024,15(1):13.
APA He, Xinheng.,Zhao, Lifen.,Tian, Yinping.,Li, Rui.,Chu, Qinyu.,...&Cheng, Xi.(2024).Highly accurate carbohydrate-binding site prediction with DeepGlycanSite.NATURE COMMUNICATIONS,15(1),13.
MLA He, Xinheng,et al."Highly accurate carbohydrate-binding site prediction with DeepGlycanSite".NATURE COMMUNICATIONS 15.1(2024):13.

入库方式： OAI收割

来源：上海药物研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。