About: Computational insights into tetracyclines as inhibitors against SARS-CoV-2 M(pro) via combinatorial molecular simulation calculations

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About: Computational insights into tetracyclines as inhibitors against SARS-CoV-2 M(pro) via combinatorial molecular simulation calculations Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Computational insights into tetracyclines as inhibitors against SARS-CoV-2 M(pro) via combinatorial molecular simulation calculations
Creator	Lee, Kyung Kang, Sang Bharadwaj, Shiv Dhar Dwivedi, Vivek
source	Elsevier; Medline; PMC; WHO
abstract	The COVID-19 pandemic raised by SARS-CoV-2 is a public health emergency. However, lack of antiviral drugs and vaccine against human coronaviruses demands a concerted approach to challenge the SARS-CoV-2 infection. Under limited resource and urgency, combinatorial computational approaches to identify the potential inhibitor from known drugs could be applied against risen COVID-19 pandemic. Thereof, this study attempted to purpose the potent inhibitors from the approved drug pool against SARS-CoV-2 main protease (M(pro)). To circumvent the issue of lead compound from available drugs as antivirals, antibiotics with broad spectrum of viral activity, i.e. doxycycline, tetracycline, demeclocycline, and minocycline were chosen for molecular simulation analysis against native ligand N3 inhibitor in SARS-CoV-2 M(pro) crystal structure. Molecular docking simulation predicted the docking score >−7 kcal/mol with significant intermolecular interaction at the catalytic dyad (His41 and Cys145) and other essential substrate binding residues of SARS-CoV-2 M(pro). The best ligand conformations were further studied for complex stability and intermolecular interaction profiling with respect to time under 100 ns classical molecular dynamics simulation, established the significant stability and interactions of selected antibiotics by comparison to N3 inhibitor. Based on combinatorial molecular simulation analysis, doxycycline and minocycline were selected as potent inhibitor against SARS-CoV-2 M(pro) which can used in combinational therapy against SARS-CoV-2 infection.
has issue date	2020-09-15(xsd:dateTime)
bibo:doi	10.1016/j.lfs.2020.118080
bibo:pmid	32653520
has license	no-cc
sha1sum (hex)	4aa70b125cb530b7455d1edcf48e4324028f916a
schema:url	https://doi.org/10.1016/j.lfs.2020.118080
resource representing a document's title	Computational insights into tetracyclines as inhibitors against SARS-CoV-2 M(pro) via combinatorial molecular simulation calculations
has PubMed Central identifier	PMC7347340
has PubMed identifier	32653520
schema:publication	Life Sci
resource representing a document's body	covid:4aa70b125cb530b7455d1edcf48e4324028f916a#body_text
is schema:about of	named entity 'antivirals' named entity 'protease' named entity 'score' named entity 'SARS-CoV-2' named entity 'DOCKING' named entity 'SIGNIFICANT' named entity 'INTERACTIONS' named entity 'THERAPY' named entity 'DEMECLOCYCLINE' named entity 'BASED' named entity 'CHALLENGE' named entity 'KNOWN' named entity 'DYAD' named entity 'ANTIBIOTICS' named entity 'LEAD COMPOUND' named entity 'ANTIVIRAL DRUGS' named entity 'TIME' named entity 'MOLECULAR' named entity '100' named entity 'COMPARISON' named entity 'SIMULATION ANALYSIS' named entity 'INHIBITOR' named entity 'RESPECT' named entity 'human coronaviruses' named entity 'intermolecular interaction' named entity 'potent' named entity 'SARS-CoV-2' named entity 'Under' named entity 'infection' named entity 'respect' named entity 'spectrum' named entity 'COVID-19' named entity 'complex' named entity 'vaccine' named entity 'tetracyclines' named entity 'lead compound' named entity 'SARS-CoV-2' named entity 'public health emergency' named entity 'minocycline' named entity 'SARS-CoV-2' named entity 'protease' named entity 'SARS-CoV-2' named entity 'infection' named entity 'antivirals' named entity 'molecular simulation' named entity 'molecular dynamics' named entity 'glutamic acid' named entity 'vaccine' named entity 'recombination process' named entity 'replication cycle' named entity 'protein structure' named entity 'minocycline' named entity 'SARS-CoV-2' named entity 'substrate-binding pocket' named entity 'doxycycline' named entity 'SARS-CoV-2' named entity 'drug repurposing' named entity 'ligand' named entity 'protein' named entity 'antiviral' named entity 'MERS-CoV' named entity 'binding affinity' named entity 'doxycycline' named entity 'dengue virus' named entity 'doxycycline' named entity 'protein' named entity 'SARS-CoV-2' named entity 'antiviral activity' named entity 'RMSF' named entity 'viral replication' named entity 'SARS-CoV-2' named entity 'ligands' named entity 'SARS-CoV' named entity 'protein' named entity 'α, β' named entity 'SARS-CoV-2' named entity 'crystal structure' named entity 'severe acute respiratory syndrome coronavirus'

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