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Computer-aided drug design for the pain-like protease (PL(pro)) inhibitors against SARS-CoV-2
A new coronavirus, known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a highly contagious virus and has caused a massive worldwide health crisis. While large-scale vaccination efforts are underway, the management of population health, economic impact and asof-yet unknown long-...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Masson SAS.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841087/ https://www.ncbi.nlm.nih.gov/pubmed/36689835 http://dx.doi.org/10.1016/j.biopha.2023.114247 |
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author | Gao, Hongwei Dai, Renhui Su, Ruiling |
author_facet | Gao, Hongwei Dai, Renhui Su, Ruiling |
author_sort | Gao, Hongwei |
collection | PubMed |
description | A new coronavirus, known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a highly contagious virus and has caused a massive worldwide health crisis. While large-scale vaccination efforts are underway, the management of population health, economic impact and asof-yet unknown long-term effects on physical and mental health will be a key challenge for the next decade. The papain-like protease (PL(pro)) of SARS-CoV-2 is a promising target for antiviral drugs. This report used pharmacophore-based drug design technology to identify potential compounds as PL(pro) inhibitors against SARS-CoV-2. The optimal pharmacophore model was fully validated using different strategies and then was employed to virtually screen out 10 compounds with inhibitory. Molecular docking and non-bonding interactions between the targeted protein PL(pro) and compounds showed that UKR1129266 was the best compound. These results provided a theoretical foundation for future studies of PL(pro) inhibitors against SARS-CoV-2. |
format | Online Article Text |
id | pubmed-9841087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Authors. Published by Elsevier Masson SAS. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98410872023-01-17 Computer-aided drug design for the pain-like protease (PL(pro)) inhibitors against SARS-CoV-2 Gao, Hongwei Dai, Renhui Su, Ruiling Biomed Pharmacother Article A new coronavirus, known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a highly contagious virus and has caused a massive worldwide health crisis. While large-scale vaccination efforts are underway, the management of population health, economic impact and asof-yet unknown long-term effects on physical and mental health will be a key challenge for the next decade. The papain-like protease (PL(pro)) of SARS-CoV-2 is a promising target for antiviral drugs. This report used pharmacophore-based drug design technology to identify potential compounds as PL(pro) inhibitors against SARS-CoV-2. The optimal pharmacophore model was fully validated using different strategies and then was employed to virtually screen out 10 compounds with inhibitory. Molecular docking and non-bonding interactions between the targeted protein PL(pro) and compounds showed that UKR1129266 was the best compound. These results provided a theoretical foundation for future studies of PL(pro) inhibitors against SARS-CoV-2. The Authors. Published by Elsevier Masson SAS. 2023-03 2023-01-16 /pmc/articles/PMC9841087/ /pubmed/36689835 http://dx.doi.org/10.1016/j.biopha.2023.114247 Text en © 2023 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Gao, Hongwei Dai, Renhui Su, Ruiling Computer-aided drug design for the pain-like protease (PL(pro)) inhibitors against SARS-CoV-2 |
title | Computer-aided drug design for the pain-like protease (PL(pro)) inhibitors against SARS-CoV-2 |
title_full | Computer-aided drug design for the pain-like protease (PL(pro)) inhibitors against SARS-CoV-2 |
title_fullStr | Computer-aided drug design for the pain-like protease (PL(pro)) inhibitors against SARS-CoV-2 |
title_full_unstemmed | Computer-aided drug design for the pain-like protease (PL(pro)) inhibitors against SARS-CoV-2 |
title_short | Computer-aided drug design for the pain-like protease (PL(pro)) inhibitors against SARS-CoV-2 |
title_sort | computer-aided drug design for the pain-like protease (pl(pro)) inhibitors against sars-cov-2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841087/ https://www.ncbi.nlm.nih.gov/pubmed/36689835 http://dx.doi.org/10.1016/j.biopha.2023.114247 |
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