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D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19

A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in...

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Autores principales: Shi, Yulong, Zhang, Xinben, Mu, Kaijie, Peng, Cheng, Zhu, Zhengdan, Wang, Xiaoyu, Yang, Yanqing, Xu, Zhijian, Zhu, Weiliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7169934/
https://www.ncbi.nlm.nih.gov/pubmed/32318328
http://dx.doi.org/10.1016/j.apsb.2020.04.006
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author Shi, Yulong
Zhang, Xinben
Mu, Kaijie
Peng, Cheng
Zhu, Zhengdan
Wang, Xiaoyu
Yang, Yanqing
Xu, Zhijian
Zhu, Weiliang
author_facet Shi, Yulong
Zhang, Xinben
Mu, Kaijie
Peng, Cheng
Zhu, Zhengdan
Wang, Xiaoyu
Yang, Yanqing
Xu, Zhijian
Zhu, Weiliang
author_sort Shi, Yulong
collection PubMed
description A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å(3) on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19.
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spelling pubmed-71699342020-04-21 D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19 Shi, Yulong Zhang, Xinben Mu, Kaijie Peng, Cheng Zhu, Zhengdan Wang, Xiaoyu Yang, Yanqing Xu, Zhijian Zhu, Weiliang Acta Pharm Sin B Original article A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å(3) on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19. Elsevier 2020-07 2020-04-20 /pmc/articles/PMC7169934/ /pubmed/32318328 http://dx.doi.org/10.1016/j.apsb.2020.04.006 Text en © 2020 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original article
Shi, Yulong
Zhang, Xinben
Mu, Kaijie
Peng, Cheng
Zhu, Zhengdan
Wang, Xiaoyu
Yang, Yanqing
Xu, Zhijian
Zhu, Weiliang
D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19
title D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19
title_full D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19
title_fullStr D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19
title_full_unstemmed D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19
title_short D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19
title_sort d3targets-2019-ncov: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against covid-19
topic Original article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7169934/
https://www.ncbi.nlm.nih.gov/pubmed/32318328
http://dx.doi.org/10.1016/j.apsb.2020.04.006
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