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In silico identification of drug candidates against COVID-19
The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 proteas...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7574721/ https://www.ncbi.nlm.nih.gov/pubmed/33102688 http://dx.doi.org/10.1016/j.imu.2020.100461 |
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author | Wu, Yifei Chang, Kuan Y. Lou, Lei Edwards, Lorette G. Doma, Bly K. Xie, Zhong-Ru |
author_facet | Wu, Yifei Chang, Kuan Y. Lou, Lei Edwards, Lorette G. Doma, Bly K. Xie, Zhong-Ru |
author_sort | Wu, Yifei |
collection | PubMed |
description | The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M(pro)), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M(pro). Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M(pro). Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M(pro). Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design. |
format | Online Article Text |
id | pubmed-7574721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Author(s). Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75747212020-10-21 In silico identification of drug candidates against COVID-19 Wu, Yifei Chang, Kuan Y. Lou, Lei Edwards, Lorette G. Doma, Bly K. Xie, Zhong-Ru Inform Med Unlocked Article The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M(pro)), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M(pro). Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M(pro). Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M(pro). Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design. The Author(s). Published by Elsevier Ltd. 2020 2020-10-20 /pmc/articles/PMC7574721/ /pubmed/33102688 http://dx.doi.org/10.1016/j.imu.2020.100461 Text en © 2020 The Author(s) 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 Wu, Yifei Chang, Kuan Y. Lou, Lei Edwards, Lorette G. Doma, Bly K. Xie, Zhong-Ru In silico identification of drug candidates against COVID-19 |
title | In silico identification of drug candidates against COVID-19 |
title_full | In silico identification of drug candidates against COVID-19 |
title_fullStr | In silico identification of drug candidates against COVID-19 |
title_full_unstemmed | In silico identification of drug candidates against COVID-19 |
title_short | In silico identification of drug candidates against COVID-19 |
title_sort | in silico identification of drug candidates against covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7574721/ https://www.ncbi.nlm.nih.gov/pubmed/33102688 http://dx.doi.org/10.1016/j.imu.2020.100461 |
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