In silico identification of novel SARS-COV-2 2′-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches
The novel coronavirus disease COVID-19, caused by the virus SARS CoV-2, has exerted a significant unprecedented economic and medical crisis, in addition to its impact on the daily life and health care systems all over the world. Regrettably, no vaccines or drugs are currently available for this new...
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doaj-98234b16b4e14c1eaae68e930447cbd72021-03-18T15:12:46ZengTaylor & Francis GroupJournal of Enzyme Inhibition and Medicinal Chemistry1475-63661475-63742021-01-0136172773610.1080/14756366.2021.18853961885396In silico identification of novel SARS-COV-2 2′-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approachesMahmoud A. El Hassab0Tamer M. Ibrahim1Sara T. Al-Rashood2Amal Alharbi3Razan O. Eskandrani4Wagdy M. Eldehna5Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Badr University in Cairo (BUC)Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh UniversityDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud UniversityDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud UniversityDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud UniversityDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh UniversityThe novel coronavirus disease COVID-19, caused by the virus SARS CoV-2, has exerted a significant unprecedented economic and medical crisis, in addition to its impact on the daily life and health care systems all over the world. Regrettably, no vaccines or drugs are currently available for this new critical emerging human disease. Joining the global fight against COVID-19, in this study we aim at identifying a potential novel inhibitor for SARS COV-2 2′-O-methyltransferase (nsp16) which is one of the most attractive targets in the virus life cycle, responsible for the viral RNA protection via a cap formation process. Firstly, nsp16 enzyme bound to Sinefungin was retrieved from the protein data bank (PDB ID: 6WKQ), then, a 3D pharmacophore model was constructed to be applied to screen 48 Million drug-like compounds of the Zinc database. This resulted in only 24 compounds which were subsequently docked into the enzyme. The best four score-ordered hits from the docking outcome exhibited better scores compared to Sinefungin. Finally, three molecular dynamics (MD) simulation experiments for 150 ns were carried out as a refinement step for our proposed approach. The MD and MM-PBSA outputs revealed compound 11 as the best potential nsp16 inhibitor herein identified, as it displayed a better stability and average binding free energy for the ligand-enzyme complex compared to Sinefungin.http://dx.doi.org/10.1080/14756366.2021.1885396sars cov-2 2′-o-methyltransferase (nsp16) inhibitor3d pharmacophoremolecular dynamicsmm-pbsa calculationscovid-19 therapies |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mahmoud A. El Hassab Tamer M. Ibrahim Sara T. Al-Rashood Amal Alharbi Razan O. Eskandrani Wagdy M. Eldehna |
spellingShingle |
Mahmoud A. El Hassab Tamer M. Ibrahim Sara T. Al-Rashood Amal Alharbi Razan O. Eskandrani Wagdy M. Eldehna In silico identification of novel SARS-COV-2 2′-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches Journal of Enzyme Inhibition and Medicinal Chemistry sars cov-2 2′-o-methyltransferase (nsp16) inhibitor 3d pharmacophore molecular dynamics mm-pbsa calculations covid-19 therapies |
author_facet |
Mahmoud A. El Hassab Tamer M. Ibrahim Sara T. Al-Rashood Amal Alharbi Razan O. Eskandrani Wagdy M. Eldehna |
author_sort |
Mahmoud A. El Hassab |
title |
In silico identification of novel SARS-COV-2 2′-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches |
title_short |
In silico identification of novel SARS-COV-2 2′-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches |
title_full |
In silico identification of novel SARS-COV-2 2′-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches |
title_fullStr |
In silico identification of novel SARS-COV-2 2′-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches |
title_full_unstemmed |
In silico identification of novel SARS-COV-2 2′-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches |
title_sort |
in silico identification of novel sars-cov-2 2′-o-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and mm-pbsa approaches |
publisher |
Taylor & Francis Group |
series |
Journal of Enzyme Inhibition and Medicinal Chemistry |
issn |
1475-6366 1475-6374 |
publishDate |
2021-01-01 |
description |
The novel coronavirus disease COVID-19, caused by the virus SARS CoV-2, has exerted a significant unprecedented economic and medical crisis, in addition to its impact on the daily life and health care systems all over the world. Regrettably, no vaccines or drugs are currently available for this new critical emerging human disease. Joining the global fight against COVID-19, in this study we aim at identifying a potential novel inhibitor for SARS COV-2 2′-O-methyltransferase (nsp16) which is one of the most attractive targets in the virus life cycle, responsible for the viral RNA protection via a cap formation process. Firstly, nsp16 enzyme bound to Sinefungin was retrieved from the protein data bank (PDB ID: 6WKQ), then, a 3D pharmacophore model was constructed to be applied to screen 48 Million drug-like compounds of the Zinc database. This resulted in only 24 compounds which were subsequently docked into the enzyme. The best four score-ordered hits from the docking outcome exhibited better scores compared to Sinefungin. Finally, three molecular dynamics (MD) simulation experiments for 150 ns were carried out as a refinement step for our proposed approach. The MD and MM-PBSA outputs revealed compound 11 as the best potential nsp16 inhibitor herein identified, as it displayed a better stability and average binding free energy for the ligand-enzyme complex compared to Sinefungin. |
topic |
sars cov-2 2′-o-methyltransferase (nsp16) inhibitor 3d pharmacophore molecular dynamics mm-pbsa calculations covid-19 therapies |
url |
http://dx.doi.org/10.1080/14756366.2021.1885396 |
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