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?:abstract
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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 (M(pro)) 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 (ΔG(binding)) > −48.0 kcal/mol. Interestingly, among the identified NLPs, four bis([1,3]dioxolo)pyran-5-carboxamide derivatives showed ΔG(binding) > −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-M(pro) 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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?:doi
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10.1080/07391102.2020.1790037
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?:doi
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?:journal
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Journal_of_biomolecular_structure_&_dynamics
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?:license
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document_parses/pdf_json/502fb25f8ead2fe54c3da12d1fc7501fcd87c6ec.json
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document_parses/pmc_json/PMC7443551.xml.json
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?:title
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Natural-like products as potential SARS-CoV-2 M(pro) inhibitors: in-silico drug discovery
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