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Exploration of SARS-CoV-2 Mpro Noncovalent Natural Inhibitors Using Structure-Based Approaches
[Image: see text] With the emergence of antibody-evasive omicron subvariants (BA.2.12.1, BA.4, and BA.5), which can compromise the efficacy of vaccination, it is of utmost importance to widen the finite therapeutic options for COVID-19. Although more than 600 co-crystal complexes of Mpro with inhibi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947982/ https://www.ncbi.nlm.nih.gov/pubmed/36844600 http://dx.doi.org/10.1021/acsomega.2c07259 |
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author | Duong, Cuong Quoc Nguyen, Phuong Thuy Viet |
author_facet | Duong, Cuong Quoc Nguyen, Phuong Thuy Viet |
author_sort | Duong, Cuong Quoc |
collection | PubMed |
description | [Image: see text] With the emergence of antibody-evasive omicron subvariants (BA.2.12.1, BA.4, and BA.5), which can compromise the efficacy of vaccination, it is of utmost importance to widen the finite therapeutic options for COVID-19. Although more than 600 co-crystal complexes of Mpro with inhibitors have been revealed, utilizing them to search for novel Mpro inhibitors remains limited. Although there were two major groups of Mpro inhibitors, covalent and noncovalent inhibitors, noncovalent inhibitors were our main focus due to the safety concerns with their covalent counterparts. Hence, this study aimed to explore Mpro noncovalent inhibition ability of phytochemicals extracted from Vietnamese herbals by combining multiple structure-based approaches. By closely inspecting 223 complexes of Mpro with noncovalent inhibitors, a 3D-pharmacophore model representing typical chemical features of Mpro noncovalent inhibitors was generated with good validation scores (sensitivity = 92.11%, specificity = 90.42%, accuracy = 90.65%, and goodness-of-hit score = 0.61). Afterward, the pharmacophore model was applied to explore the potential Mpro inhibitors from our in-house Vietnamese phytochemical database, revealing 18 substances, 5 of which were in vitro assayed. The remaining 13 substances were then examined by induced-fit molecular docking, revealing 12 suitable compounds. A machine-learning activity prediction model was developed to rank the hit, suggesting nigracin and calycosin-7-O-β-glucopyranoside as promising Mpro natural noncovalent inhibitors. |
format | Online Article Text |
id | pubmed-9947982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-99479822023-02-24 Exploration of SARS-CoV-2 Mpro Noncovalent Natural Inhibitors Using Structure-Based Approaches Duong, Cuong Quoc Nguyen, Phuong Thuy Viet ACS Omega [Image: see text] With the emergence of antibody-evasive omicron subvariants (BA.2.12.1, BA.4, and BA.5), which can compromise the efficacy of vaccination, it is of utmost importance to widen the finite therapeutic options for COVID-19. Although more than 600 co-crystal complexes of Mpro with inhibitors have been revealed, utilizing them to search for novel Mpro inhibitors remains limited. Although there were two major groups of Mpro inhibitors, covalent and noncovalent inhibitors, noncovalent inhibitors were our main focus due to the safety concerns with their covalent counterparts. Hence, this study aimed to explore Mpro noncovalent inhibition ability of phytochemicals extracted from Vietnamese herbals by combining multiple structure-based approaches. By closely inspecting 223 complexes of Mpro with noncovalent inhibitors, a 3D-pharmacophore model representing typical chemical features of Mpro noncovalent inhibitors was generated with good validation scores (sensitivity = 92.11%, specificity = 90.42%, accuracy = 90.65%, and goodness-of-hit score = 0.61). Afterward, the pharmacophore model was applied to explore the potential Mpro inhibitors from our in-house Vietnamese phytochemical database, revealing 18 substances, 5 of which were in vitro assayed. The remaining 13 substances were then examined by induced-fit molecular docking, revealing 12 suitable compounds. A machine-learning activity prediction model was developed to rank the hit, suggesting nigracin and calycosin-7-O-β-glucopyranoside as promising Mpro natural noncovalent inhibitors. American Chemical Society 2023-02-06 /pmc/articles/PMC9947982/ /pubmed/36844600 http://dx.doi.org/10.1021/acsomega.2c07259 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Duong, Cuong Quoc Nguyen, Phuong Thuy Viet Exploration of SARS-CoV-2 Mpro Noncovalent Natural Inhibitors Using Structure-Based Approaches |
title | Exploration of
SARS-CoV-2 Mpro Noncovalent
Natural Inhibitors Using Structure-Based Approaches |
title_full | Exploration of
SARS-CoV-2 Mpro Noncovalent
Natural Inhibitors Using Structure-Based Approaches |
title_fullStr | Exploration of
SARS-CoV-2 Mpro Noncovalent
Natural Inhibitors Using Structure-Based Approaches |
title_full_unstemmed | Exploration of
SARS-CoV-2 Mpro Noncovalent
Natural Inhibitors Using Structure-Based Approaches |
title_short | Exploration of
SARS-CoV-2 Mpro Noncovalent
Natural Inhibitors Using Structure-Based Approaches |
title_sort | exploration of
sars-cov-2 mpro noncovalent
natural inhibitors using structure-based approaches |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947982/ https://www.ncbi.nlm.nih.gov/pubmed/36844600 http://dx.doi.org/10.1021/acsomega.2c07259 |
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