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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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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 |
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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 |
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