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题名

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov

作者
通讯作者Wei, Yanjie
发表日期
2020-06
DOI
发表期刊
ISSN
1913-2751
EISSN
1867-1462
卷号12期号:3页码:368-376
摘要
A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, d-Sorbitol, d-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.
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英语
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其他
资助项目
National Key Research and Development Program of China[2018YFB0204403][2016YFB0201305] ; National Science Foundation of China[U1813203][61433012] ; National Natural Youth Science Foundation of China[31601028] ; Shenzhen Basic Research Fund[JCYJ20180507182818013][GGFW2017073114031767][JCYJ20170413093358429] ; China Postdoctoral Science Foundation[2019M653132] ; CAS Key Lab[2011DP173015]
WOS研究方向
Mathematical & Computational Biology
WOS类目
Mathematical & Computational Biology
WOS记录号
WOS:000537395500001
出版者
来源库
Web of Science
引用统计
被引频次[WOS]:119
成果类型期刊论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/139972
专题南方科技大学第二附属医院
南方科技大学第一附属医院
作者单位
1.Chinese Acad Sci, Ctr High Performance Comp, Joint Engn Res Ctr Hlth Big Data Intelligent Anal, Shenzhen Inst Adv Technol, Shenzhen 518055, Guangdong, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen Peoples Hosp 3, Hosp 2,State Key Discipline Infect Dis, Shenzhen Key Lab Pathogen & Immun,Guangdong Key L, Shenzhen 518112, Peoples R China
3.Univ City Shenzhen, Chinese Acad Sci, Inst Biomed & Biotechnol, Shenzhen Lab Human Antibody Engn,Shenzhen Inst Ad, 1068 Xueyuan Blvd, Shenzhen 518055, Peoples R China
4.Shenzhen Ctr Dis Control & Prevent, Inst Toxicol, 8 Longyuan Rd, Shenzhen 518055, Peoples R China
5.Georgia State Univ, Dept Comp Sci, Atlanta, GA 30302 USA
6.Univ Chinese Acad Sci, 19 A Yuquan Rd, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Zhang, Haiping,Saravanan, Konda Mani,Yang, Yang,et al. Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov[J]. Interdisciplinary Sciences-Computational Life Sciences,2020,12(3):368-376.
APA
Zhang, Haiping.,Saravanan, Konda Mani.,Yang, Yang.,Hossain, Md Tofazzal.,Li, Junxin.,...&Wei, Yanjie.(2020).Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov.Interdisciplinary Sciences-Computational Life Sciences,12(3),368-376.
MLA
Zhang, Haiping,et al."Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov".Interdisciplinary Sciences-Computational Life Sciences 12.3(2020):368-376.
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