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1000 Titel
  • Rational approach toward COVID-19 main protease inhibitors via molecular docking, molecular dynamics simulation and free energy calculation
1000 Autor/in
  1. Keretsu, Seketoulie |
  2. Bhujbal, Swapnil P. |
  3. Cho, Seung Joo |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-10-19
1000 Erschienen in
1000 Quellenangabe
  • 10:17716
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1038/s41598-020-74468-0 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572583/ |
1000 Ergänzendes Material
  • https://www.nature.com/articles/s41598-020-74468-0#Sec17 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • In the rapidly evolving coronavirus disease (COVID-19) pandemic, repurposing existing drugs and evaluating commercially available inhibitors against druggable targets of the virus could be an effective strategy to accelerate the drug discovery process. The 3C-Like proteinase (3CLpro) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been identified as an important drug target due to its role in viral replication. The lack of a potent 3CLpro inhibitor and the availability of the X-ray crystal structure of 3CLpro (PDB-ID 6LU7) motivated us to perform computational studies to identify commercially available potential inhibitors. A combination of modeling studies was performed to identify potential 3CLpro inhibitors from the protease inhibitor database MEROPS (https://www.ebi.ac.uk/merops/index.shtml). Binding energy evaluation identified key residues for inhibitor design. We found 15 potential 3CLpro inhibitors with higher binding affinity than that of an α-ketoamide inhibitor determined via X-ray structure. Among them, saquinavir and three other investigational drugs aclarubicin, TMC-310911, and faldaprevir could be suggested as potential 3CLpro inhibitors. We recommend further experimental investigation of these compounds.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Computational models
lokal Virtual drug screening
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/S2VyZXRzdSwgU2VrZXRvdWxpZQ==|https://frl.publisso.de/adhoc/uri/Qmh1amJhbCwgU3dhcG5pbCBQLg==|https://frl.publisso.de/adhoc/uri/Q2hvLCBTZXVuZyBKb28=
1000 Label
1000 Förderer
  1. National Research Foundation of Korea |
1000 Fördernummer
  1. 2015-009070
1000 Förderprogramm
  1. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer National Research Foundation of Korea |
    1000 Förderprogramm -
    1000 Fördernummer 2015-009070
1000 Objektart article
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1000 @id frl:6425400.rdf
1000 Erstellt am 2021-01-28T09:04:34.758+0100
1000 Erstellt von 5
1000 beschreibt frl:6425400
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet Fri Feb 12 08:41:53 CET 2021
1000 Objekt bearb. Fri Feb 12 08:41:38 CET 2021
1000 Vgl. frl:6425400
1000 Oai Id
  1. oai:frl.publisso.de:frl:6425400 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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