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Analysis of natural compounds against the activity of SARS-CoV-2 NSP15 protein towards an effective treatment against COVID-19: a theoretical and computational biology approach.
- Source :
-
Journal of molecular modeling [J Mol Model] 2021 May 08; Vol. 27 (6), pp. 160. Date of Electronic Publication: 2021 May 08. - Publication Year :
- 2021
-
Abstract
- Coronavirus infectious disease 2019 (COVID-19), a viral infection caused by a novel coronavirus (nCoV), continues to emerge as a serious threat to public health. This pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2) has infected globally with 1,550,000 plus deaths to date, representing a high risk to public health. No effective drug or vaccine is available to curb down this deadly virus. The expedition for searching for a potential drug or vaccine against COVID-19 is of massive potential and favour to the community. This study is focused on finding an effective natural compound that can be processed further into a potential inhibitor to check the activity of SARS-CoV-2 with minimal side effects targeting NSP15 protein, which belongs to the EndoU enzyme family. The natural screening suggested two efficient compounds (PubChem ID: 95372568 and 1776037) with dihydroxyphenyl region of the compound, found to be important in the interaction with the viral protein showing promising activity which may act as a potent lead inhibitory molecule against the virus. In combination with virtual screening, modelling, drug likeliness, molecular docking, and 500 ns cumulative molecular dynamics simulations (100 ns for each complex) along with the decomposition analysis to calculate and confirm the stability and fold, we propose 95372568 and 1776037 as novel compounds of natural origin capable of getting developed into potent lead molecules against SARS-CoV-2 target protein NSP15.
- Subjects :
- Antiviral Agents therapeutic use
Biological Products therapeutic use
Humans
Antiviral Agents chemistry
Biological Products chemistry
Computational Biology
Endoribonucleases chemistry
Molecular Docking Simulation
Molecular Dynamics Simulation
SARS-CoV-2 chemistry
Viral Nonstructural Proteins chemistry
COVID-19 Drug Treatment
Subjects
Details
- Language :
- English
- ISSN :
- 0948-5023
- Volume :
- 27
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- Journal of molecular modeling
- Publication Type :
- Academic Journal
- Accession number :
- 33963942
- Full Text :
- https://doi.org/10.1007/s00894-021-04750-z