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A computational study of cooperative binding to multiple SARS-CoV-2 proteins
Structure-based drug design targeting the SARS-CoV-2 virus has been greatly facilitated by available virus-related protein structures. However, there is an urgent need for effective, safe small-molecule drugs to control the spread of the virus and variants. While many efforts are devoted to searchin...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358031/ https://www.ncbi.nlm.nih.gov/pubmed/34381116 http://dx.doi.org/10.1038/s41598-021-95826-6 |
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author | Li, Jianing McKay, Kyle T. Remington, Jacob M. Schneebeli, Severin T. |
author_facet | Li, Jianing McKay, Kyle T. Remington, Jacob M. Schneebeli, Severin T. |
author_sort | Li, Jianing |
collection | PubMed |
description | Structure-based drug design targeting the SARS-CoV-2 virus has been greatly facilitated by available virus-related protein structures. However, there is an urgent need for effective, safe small-molecule drugs to control the spread of the virus and variants. While many efforts are devoted to searching for compounds that selectively target individual proteins, we investigated the potential interactions between eight proteins related to SARS-CoV-2 and more than 600 compounds from a traditional Chinese medicine which has proven effective at treating the viral infection. Our original ensemble docking and cooperative docking approaches, followed by a total of over 16-micorsecond molecular simulations, have identified at least 9 compounds that may generally bind to key SARS-CoV-2 proteins. Further, we found evidence that some of these compounds can simultaneously bind to the same target, potentially leading to cooperative inhibition to SARS-CoV-2 proteins like the Spike protein and the RNA-dependent RNA polymerase. These results not only present a useful computational methodology to systematically assess the anti-viral potential of small molecules, but also point out a new avenue to seek cooperative compounds toward cocktail therapeutics to target more SARS-CoV-2-related proteins. |
format | Online Article Text |
id | pubmed-8358031 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83580312021-08-13 A computational study of cooperative binding to multiple SARS-CoV-2 proteins Li, Jianing McKay, Kyle T. Remington, Jacob M. Schneebeli, Severin T. Sci Rep Article Structure-based drug design targeting the SARS-CoV-2 virus has been greatly facilitated by available virus-related protein structures. However, there is an urgent need for effective, safe small-molecule drugs to control the spread of the virus and variants. While many efforts are devoted to searching for compounds that selectively target individual proteins, we investigated the potential interactions between eight proteins related to SARS-CoV-2 and more than 600 compounds from a traditional Chinese medicine which has proven effective at treating the viral infection. Our original ensemble docking and cooperative docking approaches, followed by a total of over 16-micorsecond molecular simulations, have identified at least 9 compounds that may generally bind to key SARS-CoV-2 proteins. Further, we found evidence that some of these compounds can simultaneously bind to the same target, potentially leading to cooperative inhibition to SARS-CoV-2 proteins like the Spike protein and the RNA-dependent RNA polymerase. These results not only present a useful computational methodology to systematically assess the anti-viral potential of small molecules, but also point out a new avenue to seek cooperative compounds toward cocktail therapeutics to target more SARS-CoV-2-related proteins. Nature Publishing Group UK 2021-08-11 /pmc/articles/PMC8358031/ /pubmed/34381116 http://dx.doi.org/10.1038/s41598-021-95826-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Li, Jianing McKay, Kyle T. Remington, Jacob M. Schneebeli, Severin T. A computational study of cooperative binding to multiple SARS-CoV-2 proteins |
title | A computational study of cooperative binding to multiple SARS-CoV-2 proteins |
title_full | A computational study of cooperative binding to multiple SARS-CoV-2 proteins |
title_fullStr | A computational study of cooperative binding to multiple SARS-CoV-2 proteins |
title_full_unstemmed | A computational study of cooperative binding to multiple SARS-CoV-2 proteins |
title_short | A computational study of cooperative binding to multiple SARS-CoV-2 proteins |
title_sort | computational study of cooperative binding to multiple sars-cov-2 proteins |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358031/ https://www.ncbi.nlm.nih.gov/pubmed/34381116 http://dx.doi.org/10.1038/s41598-021-95826-6 |
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