Universität zu Köln

Halimi, Mohammad and Bararpour, Parvindokht (2022). Natural inhibitors of SARS-CoV-2 main protease: structure based pharmacophore modeling, molecular docking and molecular dynamic simulation studies. J. Mol. Model., 28 (9). NEW YORK: SPRINGER. ISSN 0948-5023

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Abstract

Main protease (M-pro) plays a key role in replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study was designed for finding natural inhibitors of SARS-CoV-2 M-pro by in silico methods. To this end, the co-crystal structure of M-pro with telaprevir was explored and receptor-ligand pharmacophore models were developed and validated using pharmit. The database of ZINC Natural Products was screened, and 288 compounds were filtered according to pharmacophore features. In the next step, Lipinski's rule of five was applied and absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the filtered compounds were calculated using in silico methods. The resulted 15 compounds were docked into the active site of M-pro and those with the highest binding scores and better interaction including ZINC61991204, ZINC67910260, ZINC61991203, and ZINC08790293 were selected. Further analysis by molecular dynamic simulation studies showed that ZINC61991203 and ZINC08790293 dissociated from M-pro active site, while ZINC426421106 and ZINC5481346 were stable. Root mean square deviation (RMSD), radius of gyration (Rg), number of hydrogen bonds between ligand and protein during the time of simulation, and root mean square fluctuations (RMSF) of protein and ligands were calculated, and components of binding free energy were calculated using the molecular mechanic/Poisson-Boltzmann surface area (MM/PBSA) method. The result of all the analysis indicated that ZINC61991204 and ZINC67910260 are drug-like and nontoxic and have a high potential for inhibiting M-pro.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Halimi, Mohammad UNSPECIFIED UNSPECIFIED UNSPECIFIED Bararpour, Parvindokht UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-665864
DOI:	10.1007/s00894-022-05286-6
Journal or Publication Title:	J. Mol. Model.
Volume:	28
Number:	9
Date:	2022
Publisher:	SPRINGER
Place of Publication:	NEW YORK
ISSN:	0948-5023
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language FORCE-FIELD; COVID-19; TELAPREVIR Multiple languages Biochemistry & Molecular Biology; Biophysics; Chemistry, Multidisciplinary; Computer Science, Interdisciplinary Applications Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/66586