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In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing
BACKGROUND: In November 2021, variant B.1.1.529 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified by the World Health Organization (WHO) and designated Omicron. Omicron is characterized by a high number of mutations, thirty-two in total, making it more transmissible than...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939377/ https://www.ncbi.nlm.nih.gov/pubmed/36808314 http://dx.doi.org/10.1007/s00894-023-05457-z |
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author | Mohamed, Eslam A. R. Abdel-Rahman, Islam M. Zaki, Magdi E. A. Al-Khdhairawi, Ahmad Abdelhamid, Mahmoud M. Alqaisi, Ahmad M. Rahim, Lyana binti Abd Abu-Hussein, Bilal El-Sheikh, Azza A. K. Abdelwahab, Sayed F. Hassan, Heba Ali |
author_facet | Mohamed, Eslam A. R. Abdel-Rahman, Islam M. Zaki, Magdi E. A. Al-Khdhairawi, Ahmad Abdelhamid, Mahmoud M. Alqaisi, Ahmad M. Rahim, Lyana binti Abd Abu-Hussein, Bilal El-Sheikh, Azza A. K. Abdelwahab, Sayed F. Hassan, Heba Ali |
author_sort | Mohamed, Eslam A. R. |
collection | PubMed |
description | BACKGROUND: In November 2021, variant B.1.1.529 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified by the World Health Organization (WHO) and designated Omicron. Omicron is characterized by a high number of mutations, thirty-two in total, making it more transmissible than the original virus. More than half of those mutations were found in the receptor-binding domain (RBD) that directly interacts with human angiotensin-converting enzyme 2 (ACE2). This study aimed to discover potent drugs against Omicron, which were previously repurposed for coronavirus disease 2019 (COVID-19). All repurposed anti-COVID-19 drugs were compiled from previous studies and tested against the RBD of SARS-CoV-2 Omicron. METHODS: As a preliminary step, a molecular docking study was performed to investigate the potency of seventy-one compounds from four classes of inhibitors. The molecular characteristics of the best-performing five compounds were predicted by estimating the drug-likeness and drug score. Molecular dynamics simulations (MD) over 100 ns were performed to inspect the relative stability of the best compound within the Omicron receptor-binding site. RESULTS: The current findings point out the crucial roles of Q493R, G496S, Q498R, N501Y, and Y505H in the RBD region of SARS-CoV-2 Omicron. Raltegravir, hesperidin, pyronaridine, and difloxacin achieved the highest drug scores compared with the other compounds in the four classes, with values of 81%, 57%, 18%, and 71%, respectively. The calculated results showed that raltegravir and hesperidin had high binding affinities and stabilities to Omicron with ΔG(binding) of − 75.7304 ± 0.98324 and − 42.693536 ± 0.979056 kJ/mol, respectively. Further clinical studies should be performed for the two best compounds from this study. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00894-023-05457-z. |
format | Online Article Text |
id | pubmed-9939377 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-99393772023-02-21 In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing Mohamed, Eslam A. R. Abdel-Rahman, Islam M. Zaki, Magdi E. A. Al-Khdhairawi, Ahmad Abdelhamid, Mahmoud M. Alqaisi, Ahmad M. Rahim, Lyana binti Abd Abu-Hussein, Bilal El-Sheikh, Azza A. K. Abdelwahab, Sayed F. Hassan, Heba Ali J Mol Model Original Paper BACKGROUND: In November 2021, variant B.1.1.529 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified by the World Health Organization (WHO) and designated Omicron. Omicron is characterized by a high number of mutations, thirty-two in total, making it more transmissible than the original virus. More than half of those mutations were found in the receptor-binding domain (RBD) that directly interacts with human angiotensin-converting enzyme 2 (ACE2). This study aimed to discover potent drugs against Omicron, which were previously repurposed for coronavirus disease 2019 (COVID-19). All repurposed anti-COVID-19 drugs were compiled from previous studies and tested against the RBD of SARS-CoV-2 Omicron. METHODS: As a preliminary step, a molecular docking study was performed to investigate the potency of seventy-one compounds from four classes of inhibitors. The molecular characteristics of the best-performing five compounds were predicted by estimating the drug-likeness and drug score. Molecular dynamics simulations (MD) over 100 ns were performed to inspect the relative stability of the best compound within the Omicron receptor-binding site. RESULTS: The current findings point out the crucial roles of Q493R, G496S, Q498R, N501Y, and Y505H in the RBD region of SARS-CoV-2 Omicron. Raltegravir, hesperidin, pyronaridine, and difloxacin achieved the highest drug scores compared with the other compounds in the four classes, with values of 81%, 57%, 18%, and 71%, respectively. The calculated results showed that raltegravir and hesperidin had high binding affinities and stabilities to Omicron with ΔG(binding) of − 75.7304 ± 0.98324 and − 42.693536 ± 0.979056 kJ/mol, respectively. Further clinical studies should be performed for the two best compounds from this study. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00894-023-05457-z. Springer Berlin Heidelberg 2023-02-20 2023 /pmc/articles/PMC9939377/ /pubmed/36808314 http://dx.doi.org/10.1007/s00894-023-05457-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Original Paper Mohamed, Eslam A. R. Abdel-Rahman, Islam M. Zaki, Magdi E. A. Al-Khdhairawi, Ahmad Abdelhamid, Mahmoud M. Alqaisi, Ahmad M. Rahim, Lyana binti Abd Abu-Hussein, Bilal El-Sheikh, Azza A. K. Abdelwahab, Sayed F. Hassan, Heba Ali In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing |
title | In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing |
title_full | In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing |
title_fullStr | In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing |
title_full_unstemmed | In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing |
title_short | In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing |
title_sort | in silico prediction of potential inhibitors for sars-cov-2 omicron variant using molecular docking and dynamics simulation-based drug repurposing |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939377/ https://www.ncbi.nlm.nih.gov/pubmed/36808314 http://dx.doi.org/10.1007/s00894-023-05457-z |
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