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Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT
Emergence of the SARS-CoV-2 Omicron variant of concern (VOC; B.1.1.529) resulted in a new peak of the COVID-19 pandemic, which called for development of effective therapeutics against the Omicron VOC. The receptor binding domain (RBD) of the spike protein, which is responsible for recognition and bi...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772825/ https://www.ncbi.nlm.nih.gov/pubmed/36569957 http://dx.doi.org/10.3389/fchem.2022.1063374 |
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author | Lü, Xudong Feng, Cuiyue Lü, Ruijie Wei, Xiyu Fan, Shuai Yan, Maocai Zhu, Xiandui Zhang, Zhifei Yang, Zhaoyong |
author_facet | Lü, Xudong Feng, Cuiyue Lü, Ruijie Wei, Xiyu Fan, Shuai Yan, Maocai Zhu, Xiandui Zhang, Zhifei Yang, Zhaoyong |
author_sort | Lü, Xudong |
collection | PubMed |
description | Emergence of the SARS-CoV-2 Omicron variant of concern (VOC; B.1.1.529) resulted in a new peak of the COVID-19 pandemic, which called for development of effective therapeutics against the Omicron VOC. The receptor binding domain (RBD) of the spike protein, which is responsible for recognition and binding of the human ACE2 receptor protein, is a potential drug target. Mutations in receptor binding domain of the S-protein have been postulated to enhance the binding strength of the Omicron VOC to host proteins. In this study, bioinformatic analyses were performed to screen for potential therapeutic compounds targeting the omicron VOC. A total of 92,699 compounds were screened from different libraries based on receptor binding domain of the S-protein via docking and binding free energy analysis, yielding the top 5 best hits. Dynamic simulation trajectory analysis and binding free energy decomposition were used to determine the inhibitory mechanism of candidate molecules by focusing on their interactions with recognized residues on receptor binding domain. The ADMET prediction and DFT calculations were conducted to determine the pharmacokinetic parameters and precise chemical properties of the identified molecules. The molecular properties of the identified molecules and their ability to interfere with recognition of the human ACE2 receptors by receptor binding domain suggest that they are potential therapeutic agents for SARS-CoV-2 Omicron VOC. |
format | Online Article Text |
id | pubmed-9772825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97728252022-12-23 Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT Lü, Xudong Feng, Cuiyue Lü, Ruijie Wei, Xiyu Fan, Shuai Yan, Maocai Zhu, Xiandui Zhang, Zhifei Yang, Zhaoyong Front Chem Chemistry Emergence of the SARS-CoV-2 Omicron variant of concern (VOC; B.1.1.529) resulted in a new peak of the COVID-19 pandemic, which called for development of effective therapeutics against the Omicron VOC. The receptor binding domain (RBD) of the spike protein, which is responsible for recognition and binding of the human ACE2 receptor protein, is a potential drug target. Mutations in receptor binding domain of the S-protein have been postulated to enhance the binding strength of the Omicron VOC to host proteins. In this study, bioinformatic analyses were performed to screen for potential therapeutic compounds targeting the omicron VOC. A total of 92,699 compounds were screened from different libraries based on receptor binding domain of the S-protein via docking and binding free energy analysis, yielding the top 5 best hits. Dynamic simulation trajectory analysis and binding free energy decomposition were used to determine the inhibitory mechanism of candidate molecules by focusing on their interactions with recognized residues on receptor binding domain. The ADMET prediction and DFT calculations were conducted to determine the pharmacokinetic parameters and precise chemical properties of the identified molecules. The molecular properties of the identified molecules and their ability to interfere with recognition of the human ACE2 receptors by receptor binding domain suggest that they are potential therapeutic agents for SARS-CoV-2 Omicron VOC. Frontiers Media S.A. 2022-12-08 /pmc/articles/PMC9772825/ /pubmed/36569957 http://dx.doi.org/10.3389/fchem.2022.1063374 Text en Copyright © 2022 Lü, Feng, Lü, Wei, Fan, Yan, Zhu, Zhang and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Chemistry Lü, Xudong Feng, Cuiyue Lü, Ruijie Wei, Xiyu Fan, Shuai Yan, Maocai Zhu, Xiandui Zhang, Zhifei Yang, Zhaoyong Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT |
title | Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT |
title_full | Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT |
title_fullStr | Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT |
title_full_unstemmed | Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT |
title_short | Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT |
title_sort | identification of potential inhibitors of omicron variant of sars-cov-2 rbd based virtual screening, md simulation, and dft |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772825/ https://www.ncbi.nlm.nih.gov/pubmed/36569957 http://dx.doi.org/10.3389/fchem.2022.1063374 |
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