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Natural-like products as potential SARS-CoV-2 Mpro inhibitors: in-silico drug discovery.
Ibrahim, Mahmoud A A; Abdeljawaad, Khlood A A; Abdelrahman, Alaa H M; Hegazy, Mohamed-Elamir F.
  • Ibrahim MAA; Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia, Egypt.
  • Abdeljawaad KAA; Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia, Egypt.
  • Abdelrahman AHM; Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia, Egypt.
  • Hegazy MF; Chemistry of Medicinal Plants Department, National Research Centre, El-Tahrir St, Dokki, Giza, Egypt.
J Biomol Struct Dyn ; 39(15): 5722-5734, 2021 09.
Article in English | MEDLINE | ID: covidwho-1390286
ABSTRACT
In December 2019, a COVID-19 epidemic was discovered in Wuhan, China, and since has disseminated around the world impacting human health for millions. Herein, in-silico drug discovery approaches have been utilized to identify potential natural products (NPs) as Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. The MolPort database that contains over 100,000 NPs was screened and filtered using molecular docking techniques. Based on calculated docking scores, the top 5,000 NPs/natural-like products (NLPs) were selected and subjected to molecular dynamics (MD) simulations followed by molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations. Combined 50 ns MD simulations and MM-GBSA calculations revealed nine potent NLPs with binding affinities (ΔGbinding) > -48.0 kcal/mol. Interestingly, among the identified NLPs, four bis([1,3]dioxolo)pyran-5-carboxamide derivatives showed ΔGbinding > -56.0 kcal/mol, forming essential short hydrogen bonds with HIS163 and GLY143 amino acids via dioxolane oxygen atoms. Structural and energetic analyses over 50 ns MD simulation demonstrated NLP-Mpro complex stability. Drug-likeness predictions revealed the prospects of the identified NLPs as potential drug candidates. The findings are expected to provide a novel contribution to the field of COVID-19 drug discovery.Communicated by Ramaswamy H. Sarma.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Journal: J Biomol Struct Dyn Year: 2021 Document Type: Article Affiliation country: 07391102.2020.1790037

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Journal: J Biomol Struct Dyn Year: 2021 Document Type: Article Affiliation country: 07391102.2020.1790037