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PubMed Central
Other literature type . 2022
Data sources: PubMed Central
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Biochemical and Biophysical Research Communications
Article . 2022 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Virtual screening and molecular dynamics simulation for identification of natural antiviral agents targeting SARS-CoV-2 NSP10

Authors: Huilin Zhao; Jin Liu; Lei He; Lichuan Zhang; Rilei Yu; Congmin Kang;

Virtual screening and molecular dynamics simulation for identification of natural antiviral agents targeting SARS-CoV-2 NSP10

Abstract

New variations of SARS-CoV-2 continue to emerge in the global pandemic, which may be resistant to at least some vaccines in COVID-19, indicating that drug and vaccine development must be continuously strengthened. NSP10 plays an essential role in SARS-CoV-2 viral life cycle. It stimulates the enzymatic activities of NSP14-ExoN and NSP16-O-MTase by the formation of NSP10/NSP14 and NSP10/NSP16 complexes. Inhibiting NSP10 can block the binding of NSP10 to NSP14 and NSP16. This study has identified potential natural NSP10 inhibitors from ZINC database. The protein druggable pocket was identified for screening candidates. Molecular docking of the selected compounds was performed and MM-GBSA binding energy was calculated. After ADMET assessment, 4 hits were obtained for favorable druggability. The analysis of site interactions suggested that the hits all had excellent binding. Molecular dynamics studies revealed that selected natural compounds stably bind to NSP10. These compounds were identified as potential leads against NSP10 for the development of strategies to combat SARS-CoV-2 replication and could serve as the basis for further studies.

Related Organizations
Keywords

Molecular Docking Simulation, SARS-CoV-2, Humans, Methyltransferases, Molecular Dynamics Simulation, Viral Nonstructural Proteins, Antiviral Agents, Article, COVID-19 Drug Treatment

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green