Prediction of the Receptorome for the Human-Infecting Virome

Zheng Zhang; Sifan Ye; Aiping Wu; Taijiao Jiang; Yousong Peng

doi:10.1007/s12250-020-00259-6

February 2021

Zheng Zhang, Sifan Ye, Aiping Wu, Taijiao Jiang and Yousong Peng. Prediction of the Receptorome for the Human-Infecting Virome[J]. Virologica Sinica, 2021, 36(1): 133-140. doi: 10.1007/s12250-020-00259-6

Citation: Zheng Zhang, Sifan Ye, Aiping Wu, Taijiao Jiang, Yousong Peng. Prediction of the Receptorome for the Human-Infecting Virome .VIROLOGICA SINICA, 2021, 36(1) : 133-140. http://dx.doi.org/10.1007/s12250-020-00259-6

基于生物信息学预测人类病毒受体组

张征 ^1, ,
叶思帆 ^1, ,
吴爱平 ^2,3 ,
蒋太交 ^2,3,4 ,
彭友松 ^1,,

1.
湖南大学生物学院

通讯作者： 彭友松, pys2013@hnu.edu.cn, ORCID: 0000-0002-5482-9506
收稿日期： 2020-04-13
录用日期： 2020-06-01
出版日期： 2020-07-28

摘要

病毒受体是病毒感染宿主细胞的关键。目前，病毒受体的鉴定非常困难。在先前的研究中，我们发现人类病毒受体蛋白具有较高的N-糖基化水平、较多的蛋白相互作用以及较高的表达量。本研究基于人类病毒受体的独有特征和蛋白序列，建立了一个从人类细胞膜蛋白中预测人类病毒受体蛋白的随机森林模型，并得到1424个人类细胞膜蛋白可能作为人类病毒的潜在受体。通过整合先前研究中人类与病毒的蛋白相互作用预测结果，进一步预测了693个人类病毒（如肠道病毒、诺如病毒和西尼罗河病毒等）的受体。最后，我们预测了新型冠状病毒的潜在受体。本研究是对人类病毒受体组预测的首次尝试，将极大的促进病毒受体的鉴定。
- 受体
- , 人类病毒
- , 病毒组
- , 生物信息学
- , 新发病毒

Prediction of the Receptorome for the Human-Infecting Virome

Zheng Zhang ^1, ,
Sifan Ye ^1, ,
Aiping Wu ^2,3 ,
Taijiao Jiang ^2,3,4 ,
Yousong Peng ^1,,

1.
Bioinformatics Center of College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
2.
Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
3.
Suzhou Institute of Systems Medicine, Suzhou 215123, China
4.
Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou 510005, China

Corresponding author: Yousong Peng, pys2013@hnu.edu.cn
ORCID: 0000-0002-5482-9506
Received Date: 13 April 2020
Accepted Date: 01 June 2020
Published Date: 28 July 2020

Abstract

The virus receptors are key for the viral infection of host cells. Identification of the virus receptors is still challenging at present. Our previous study has shown that human virus receptor proteins have some unique features including high N-glycosylation level, high number of interaction partners and high expression level. Here, a random-forest model was built to identify human virus receptorome from human cell membrane proteins with an accepted accuracy based on the combination of the unique features of human virus receptors and protein sequences. A total of 1424 human cell membrane proteins were predicted to constitute the receptorome of the human-infecting virome. In addition, the combination of the random-forest model with protein-protein interactions between human and viruses predicted in previous studies enabled further prediction of the receptors for 693 human-infecting viruses, such as the enterovirus, norovirus and West Nile virus. Finally, the candidate alternative receptors of the SARS-CoV-2 were also predicted in this study. As far as we know, this study is the first attempt to predict the receptorome for the human-infecting virome and would greatly facilitate the identification of the receptors for viruses.
- Receptor
- , Human-infecting virus
- , Virome
- , Bioinformatics
- , Emerging virus

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