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In silico approach for identifying natural lead molecules against SARS-COV-2
The life challenging COVID-19 disease caused by the SARS-CoV-2 virus has greatly impacted smooth survival worldwide since its discovery in December 2019. Currently, it is one of the major threats to humanity. Moreover, any specific drug or vaccine unavailability against COVID-19 forces to discover a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042570/ https://www.ncbi.nlm.nih.gov/pubmed/33892297 http://dx.doi.org/10.1016/j.jmgm.2021.107916 |
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author | Gupta, Shiv Shankar Kumar, Ashwani Shankar, Ravi Sharma, Upendra |
author_facet | Gupta, Shiv Shankar Kumar, Ashwani Shankar, Ravi Sharma, Upendra |
author_sort | Gupta, Shiv Shankar |
collection | PubMed |
description | The life challenging COVID-19 disease caused by the SARS-CoV-2 virus has greatly impacted smooth survival worldwide since its discovery in December 2019. Currently, it is one of the major threats to humanity. Moreover, any specific drug or vaccine unavailability against COVID-19 forces to discover a new drug on an urgent basis. Viral cycle inhibition could be one possible way to prevent the further genesis of this viral disease, which can be contributed by drug repurposing techniques or screening of small bioactive natural molecules against already validated targets of COVID-19. The main protease (M(pro)) responsible for producing functional proteins from polyprotein is an important key step for SARS-CoV-2 virion replication. Natural product or herbal based formulations are an important platform for potential therapeutics and lead compounds in the drug discovery process. Therefore, here we have screened >53,500 bioactive natural molecules from six different natural product databases against M(pro) (PDB ID: 6LU7) of COVID-19 through computational study. Further, the top three molecules were subjected to pharmacokinetics evaluation, which is an important factor that reduces the drug failure rate. Moreover, the top three screened molecules (C00014803, C00006660, ANLT0001) were further validated by a molecular dynamics study under a condition similar to the physiological one. Relative binding energy analysis of three lead molecules indicated that C00014803 possess highest binding affinity among all three hits. These extensive studies can be a significant foundation for developing a therapeutic agent against COVID-19 through vet lab studies. |
format | Online Article Text |
id | pubmed-8042570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80425702021-04-13 In silico approach for identifying natural lead molecules against SARS-COV-2 Gupta, Shiv Shankar Kumar, Ashwani Shankar, Ravi Sharma, Upendra J Mol Graph Model Article The life challenging COVID-19 disease caused by the SARS-CoV-2 virus has greatly impacted smooth survival worldwide since its discovery in December 2019. Currently, it is one of the major threats to humanity. Moreover, any specific drug or vaccine unavailability against COVID-19 forces to discover a new drug on an urgent basis. Viral cycle inhibition could be one possible way to prevent the further genesis of this viral disease, which can be contributed by drug repurposing techniques or screening of small bioactive natural molecules against already validated targets of COVID-19. The main protease (M(pro)) responsible for producing functional proteins from polyprotein is an important key step for SARS-CoV-2 virion replication. Natural product or herbal based formulations are an important platform for potential therapeutics and lead compounds in the drug discovery process. Therefore, here we have screened >53,500 bioactive natural molecules from six different natural product databases against M(pro) (PDB ID: 6LU7) of COVID-19 through computational study. Further, the top three molecules were subjected to pharmacokinetics evaluation, which is an important factor that reduces the drug failure rate. Moreover, the top three screened molecules (C00014803, C00006660, ANLT0001) were further validated by a molecular dynamics study under a condition similar to the physiological one. Relative binding energy analysis of three lead molecules indicated that C00014803 possess highest binding affinity among all three hits. These extensive studies can be a significant foundation for developing a therapeutic agent against COVID-19 through vet lab studies. Elsevier Inc. 2021-07 2021-04-13 /pmc/articles/PMC8042570/ /pubmed/33892297 http://dx.doi.org/10.1016/j.jmgm.2021.107916 Text en © 2021 Elsevier Inc. All rights reserved. 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 Gupta, Shiv Shankar Kumar, Ashwani Shankar, Ravi Sharma, Upendra In silico approach for identifying natural lead molecules against SARS-COV-2 |
title | In silico approach for identifying natural lead molecules against SARS-COV-2 |
title_full | In silico approach for identifying natural lead molecules against SARS-COV-2 |
title_fullStr | In silico approach for identifying natural lead molecules against SARS-COV-2 |
title_full_unstemmed | In silico approach for identifying natural lead molecules against SARS-COV-2 |
title_short | In silico approach for identifying natural lead molecules against SARS-COV-2 |
title_sort | in silico approach for identifying natural lead molecules against sars-cov-2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042570/ https://www.ncbi.nlm.nih.gov/pubmed/33892297 http://dx.doi.org/10.1016/j.jmgm.2021.107916 |
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