Home > Publications database > Identification of Inhibitors of SARS-CoV-2 3CL-Pro Enzymatic Activity Using a Small Molecule in Vitro Repurposing Screen > print

001     904555
005     20240625095116.0
024 7 _ |a 10.1021/acsptsci.0c00216
|2 doi
024 7 _ |a 2128/30615
|2 Handle
024 7 _ |a altmetric:102097142
|2 altmetric
024 7 _ |a WOS:000662229400007
|2 WOS
037 _ _ |a FZJ-2021-06125
082 _ _ |a 610
100 1 _ |a Kuzikov, Maria
|0 0000-0001-8771-1865
|b 0
|e Corresponding author
245 _ _ |a Identification of Inhibitors of SARS-CoV-2 3CL-Pro Enzymatic Activity Using a Small Molecule in Vitro Repurposing Screen
260 _ _ |a Washington, DC
|c 2021
|b ACS Publications
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1714997743_28930
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Compound repurposing is an important strategy for the identification of effective treatment options against SARS-CoV-2 infection and COVID-19 disease. In this regard, SARS-CoV-2 main protease (3CL-Pro), also termed M-Pro, is an attractive drug target as it plays a central role in viral replication by processing the viral polyproteins pp1a and pp1ab at multiple distinct cleavage sites. We here report the results of a repurposing program involving 8.7 K compounds containing marketed drugs, clinical and preclinical candidates, and small molecules regarded as safe in humans. We confirmed previously reported inhibitors of 3CL-Pro and have identified 62 additional compounds with IC50 values below 1 μM and profiled their selectivity toward chymotrypsin and 3CL-Pro from the Middle East respiratory syndrome virus. A subset of eight inhibitors showed anticytopathic effect in a Vero-E6 cell line, and the compounds thioguanosine and MG-132 were analyzed for their predicted binding characteristics to SARS-CoV-2 3CL-Pro. The X-ray crystal structure of the complex of myricetin and SARS-Cov-2 3CL-Pro was solved at a resolution of 1.77 Å, showing that myricetin is covalently bound to the catalytic Cys145 and therefore inhibiting its enzymatic activity.
536 _ _ |a 5241 - Molecular Information Processing in Cellular Systems (POF4-524)
|0 G:(DE-HGF)POF4-5241
|c POF4-524
|f POF IV
|x 0
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5111
|c POF4-511
|f POF IV
|x 1
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Costanzi, Elisa
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Reinshagen, Jeanette
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Esposito, Francesca
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Vangeel, Laura
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Wolf, Markus
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Ellinger, Bernhard
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Claussen, Carsten
|0 P:(DE-HGF)0
|b 7
700 1 _ |a Geisslinger, Gerd
|0 P:(DE-HGF)0
|b 8
700 1 _ |a Corona, Angela
|0 P:(DE-HGF)0
|b 9
700 1 _ |a Iaconis, Daniela
|0 P:(DE-HGF)0
|b 10
700 1 _ |a Talarico, Carmine
|0 P:(DE-HGF)0
|b 11
700 1 _ |a Manelfi, Candida
|0 P:(DE-HGF)0
|b 12
700 1 _ |a Cannalire, Rolando
|0 P:(DE-HGF)0
|b 13
700 1 _ |a Rossetti, Giulia
|0 P:(DE-Juel1)145921
|b 14
700 1 _ |a Gossen, Jonas
|0 P:(DE-Juel1)172836
|b 15
|u fzj
700 1 _ |a Albani, Simone
|0 P:(DE-Juel1)181061
|b 16
|u fzj
700 1 _ |a Musiani, Francesco
|0 P:(DE-HGF)0
|b 17
700 1 _ |a Herzog, Katja
|0 P:(DE-HGF)0
|b 18
700 1 _ |a Ye, Yang
|0 P:(DE-HGF)0
|b 19
700 1 _ |a Giabbai, Barbara
|0 P:(DE-HGF)0
|b 20
700 1 _ |a Demitri, Nicola
|0 P:(DE-HGF)0
|b 21
700 1 _ |a Jochmans, Dirk
|0 P:(DE-HGF)0
|b 22
700 1 _ |a Jonghe, Steven De
|0 P:(DE-HGF)0
|b 23
700 1 _ |a Rymenants, Jasper
|0 P:(DE-HGF)0
|b 24
700 1 _ |a Summa, Vincenzo
|0 P:(DE-HGF)0
|b 25
700 1 _ |a Tramontano, Enzo
|0 P:(DE-HGF)0
|b 26
700 1 _ |a Beccari, Andrea R.
|0 P:(DE-HGF)0
|b 27
700 1 _ |a Leyssen, Pieter
|0 P:(DE-HGF)0
|b 28
700 1 _ |a Storici, Paola
|0 P:(DE-HGF)0
|b 29
700 1 _ |a Neyts, Johan
|0 P:(DE-HGF)0
|b 30
700 1 _ |a Gribbon, Philip
|0 P:(DE-HGF)0
|b 31
700 1 _ |a Zaliani, Andrea
|0 P:(DE-HGF)0
|b 32
773 _ _ |a 10.1021/acsptsci.0c00216
|g Vol. 4, no. 3, p. 1096 - 1110
|0 PERI:(DE-600)2934670-8
|n 3
|p 1096 - 1110
|t ACS pharmacology & translational science
|v 4
|y 2021
|x 2575-9108
856 4 _ |u https://juser.fz-juelich.de/record/904555/files/acsptsci.0c00216.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:904555
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 0
|6 0000-0001-8771-1865
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 1
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 2
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 3
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 4
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 5
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 6
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 7
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 8
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 10
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 11
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 12
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 13
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 14
|6 P:(DE-Juel1)145921
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 15
|6 P:(DE-Juel1)172836
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 16
|6 P:(DE-Juel1)181061
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 17
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 18
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 19
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 20
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 21
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 22
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 23
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 24
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 26
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 27
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 28
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 29
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 30
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 31
|6 P:(DE-HGF)0
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 32
|6 P:(DE-HGF)0
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-524
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Molecular and Cellular Information Processing
|9 G:(DE-HGF)POF4-5241
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5111
|x 1
914 1 _ |y 2021
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-08-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2020-08-31
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b ACS PHARMACOL TRANSL : 2022
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2023-10-27
915 _ _ |a WoS
|0 StatID:(DE-HGF)0112
|2 StatID
|b Emerging Sources Citation Index
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-10-27
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b ACS PHARMACOL TRANSL : 2022
|d 2023-10-27
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IAS-5-20120330
|k IAS-5
|l Computational Biomedicine
|x 0
920 1 _ |0 I:(DE-Juel1)INM-9-20140121
|k INM-9
|l Computational Biomedicine
|x 1
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 2
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IAS-5-20120330
980 _ _ |a I:(DE-Juel1)INM-9-20140121
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21