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In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening
The novel coronavirus strain SARS-CoV-2 is the virus responsible for the recent global health crisis, as it causes the coronavirus disease-19 (COVID-19) in humans. Due to its high rate of spreading and significant fatality rates, the situation has escalated to a pandemic, which is the cause of immen...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer India
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741561/ https://www.ncbi.nlm.nih.gov/pubmed/35035034 http://dx.doi.org/10.1007/s40011-021-01292-5 |
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author | Mithilesh, Sai Raghunandan, Divya Suresh, P. K. |
author_facet | Mithilesh, Sai Raghunandan, Divya Suresh, P. K. |
author_sort | Mithilesh, Sai |
collection | PubMed |
description | The novel coronavirus strain SARS-CoV-2 is the virus responsible for the recent global health crisis, as it causes the coronavirus disease-19 (COVID-19) in humans. Due to its high rate of spreading and significant fatality rates, the situation has escalated to a pandemic, which is the cause of immense disruption in daily life. In this study, we have taken a docking-based virtual screening approach to select natural molecules (from plants) with possible therapeutic potential. For this purpose, AUTODOCK Vina-based determination of binding affinity values (blind and active-site oriented) was obtained to short-list molecules with possible inhibitory potential against the main Mpro in SARS-CoV-2 (PDB ID 6Y2F -the monomeric form). The 4 molecules selected were Chebuloside (−8.2; −8.2), Acetoside (−8.0; −8.0), Corilagin (−8.1; −7.7) and Arjunolic Acid (−8.0; −7.6) (blind and active-site oriented docking scores (Kcal/mol) in parenthesis, respectively). Further, a comparative search, with FDA-approved drugs, has shown that Ouabain was comparable to Chebuloside with a similarity score of 0.227. This in silico finding with respect to Ouabain is significant, since this polycyclic glycoside has been shown to treat COVID-19 positive patients with a cardiovascular disease. Hydrocortisone was similar to Arjunolic acid with a score of 0.539. Again, this likeness is worthy of mention, since hydrocortisone has been used earlier for the treatment of SARS-CoV1 and MERS. However, further experimentation and validation of the results, in suitable biological model systems, are necessary to gain more insight and relevance as well as provide corroborative evidence for our in-silico findings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40011-021-01292-5. |
format | Online Article Text |
id | pubmed-8741561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer India |
record_format | MEDLINE/PubMed |
spelling | pubmed-87415612022-01-10 In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening Mithilesh, Sai Raghunandan, Divya Suresh, P. K. Proc Natl Acad Sci India Sect B Biol Sci Research Article The novel coronavirus strain SARS-CoV-2 is the virus responsible for the recent global health crisis, as it causes the coronavirus disease-19 (COVID-19) in humans. Due to its high rate of spreading and significant fatality rates, the situation has escalated to a pandemic, which is the cause of immense disruption in daily life. In this study, we have taken a docking-based virtual screening approach to select natural molecules (from plants) with possible therapeutic potential. For this purpose, AUTODOCK Vina-based determination of binding affinity values (blind and active-site oriented) was obtained to short-list molecules with possible inhibitory potential against the main Mpro in SARS-CoV-2 (PDB ID 6Y2F -the monomeric form). The 4 molecules selected were Chebuloside (−8.2; −8.2), Acetoside (−8.0; −8.0), Corilagin (−8.1; −7.7) and Arjunolic Acid (−8.0; −7.6) (blind and active-site oriented docking scores (Kcal/mol) in parenthesis, respectively). Further, a comparative search, with FDA-approved drugs, has shown that Ouabain was comparable to Chebuloside with a similarity score of 0.227. This in silico finding with respect to Ouabain is significant, since this polycyclic glycoside has been shown to treat COVID-19 positive patients with a cardiovascular disease. Hydrocortisone was similar to Arjunolic acid with a score of 0.539. Again, this likeness is worthy of mention, since hydrocortisone has been used earlier for the treatment of SARS-CoV1 and MERS. However, further experimentation and validation of the results, in suitable biological model systems, are necessary to gain more insight and relevance as well as provide corroborative evidence for our in-silico findings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40011-021-01292-5. Springer India 2022-01-08 2022 /pmc/articles/PMC8741561/ /pubmed/35035034 http://dx.doi.org/10.1007/s40011-021-01292-5 Text en © The National Academy of Sciences, India 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Article Mithilesh, Sai Raghunandan, Divya Suresh, P. K. In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening |
title | In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening |
title_full | In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening |
title_fullStr | In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening |
title_full_unstemmed | In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening |
title_short | In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening |
title_sort | in-silico identification of natural compounds from traditional medicine as potential drug leads against sars-cov-2 through virtual screening |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741561/ https://www.ncbi.nlm.nih.gov/pubmed/35035034 http://dx.doi.org/10.1007/s40011-021-01292-5 |
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