Cargando…

nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands

The rapid emergence of SARS-CoV-2 variants with multi-sites mutations is considered as a major obstacle for the development of drugs and vaccines. Although most of the functional proteins essential for SARS-CoV-2 have been determined, the understanding of the COVID-19 target-ligand interactions rema...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Kai, Lu, Xufeng, Shi, Hang, Xu, Xiaojun, Kong, Ren, Chang, Shan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320176/
https://www.ncbi.nlm.nih.gov/pubmed/37194703
http://dx.doi.org/10.1093/nar/gkad414
_version_ 1785068396389335040
author Liu, Kai
Lu, Xufeng
Shi, Hang
Xu, Xiaojun
Kong, Ren
Chang, Shan
author_facet Liu, Kai
Lu, Xufeng
Shi, Hang
Xu, Xiaojun
Kong, Ren
Chang, Shan
author_sort Liu, Kai
collection PubMed
description The rapid emergence of SARS-CoV-2 variants with multi-sites mutations is considered as a major obstacle for the development of drugs and vaccines. Although most of the functional proteins essential for SARS-CoV-2 have been determined, the understanding of the COVID-19 target-ligand interactions remains a key challenge. The old version of this COVID-19 docking server was built in 2020, and free and open to all users. Here, we present nCoVDock2, a new docking server to predict the binding modes for targets from SARS-CoV-2. First, the new server supports more targets. We replaced the modeled structures with newly resolved structures and added more potential targets of COVID-19, especially for the variants. Second, for small molecule docking, Autodock Vina was upgraded to the latest version 1.2.0, and a new scoring function was added for peptide or antibody docking. Third, the input interface and molecular visualization were updated for a better user experience. The web server, together with an extensive help and tutorial, are freely available at: https://ncovdock2.schanglab.org.cn.
format Online
Article
Text
id pubmed-10320176
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-103201762023-07-06 nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands Liu, Kai Lu, Xufeng Shi, Hang Xu, Xiaojun Kong, Ren Chang, Shan Nucleic Acids Res Web Server Issue The rapid emergence of SARS-CoV-2 variants with multi-sites mutations is considered as a major obstacle for the development of drugs and vaccines. Although most of the functional proteins essential for SARS-CoV-2 have been determined, the understanding of the COVID-19 target-ligand interactions remains a key challenge. The old version of this COVID-19 docking server was built in 2020, and free and open to all users. Here, we present nCoVDock2, a new docking server to predict the binding modes for targets from SARS-CoV-2. First, the new server supports more targets. We replaced the modeled structures with newly resolved structures and added more potential targets of COVID-19, especially for the variants. Second, for small molecule docking, Autodock Vina was upgraded to the latest version 1.2.0, and a new scoring function was added for peptide or antibody docking. Third, the input interface and molecular visualization were updated for a better user experience. The web server, together with an extensive help and tutorial, are freely available at: https://ncovdock2.schanglab.org.cn. Oxford University Press 2023-05-17 /pmc/articles/PMC10320176/ /pubmed/37194703 http://dx.doi.org/10.1093/nar/gkad414 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Liu, Kai
Lu, Xufeng
Shi, Hang
Xu, Xiaojun
Kong, Ren
Chang, Shan
nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands
title nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands
title_full nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands
title_fullStr nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands
title_full_unstemmed nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands
title_short nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands
title_sort ncovdock2: a docking server to predict the binding modes between covid-19 targets and its potential ligands
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320176/
https://www.ncbi.nlm.nih.gov/pubmed/37194703
http://dx.doi.org/10.1093/nar/gkad414
work_keys_str_mv AT liukai ncovdock2adockingservertopredictthebindingmodesbetweencovid19targetsanditspotentialligands
AT luxufeng ncovdock2adockingservertopredictthebindingmodesbetweencovid19targetsanditspotentialligands
AT shihang ncovdock2adockingservertopredictthebindingmodesbetweencovid19targetsanditspotentialligands
AT xuxiaojun ncovdock2adockingservertopredictthebindingmodesbetweencovid19targetsanditspotentialligands
AT kongren ncovdock2adockingservertopredictthebindingmodesbetweencovid19targetsanditspotentialligands
AT changshan ncovdock2adockingservertopredictthebindingmodesbetweencovid19targetsanditspotentialligands