001     857532
005     20240625095114.0
024 7 _ |a 10.1021/acschemneuro.8b00027
|2 doi
024 7 _ |a 2128/21119
|2 Handle
024 7 _ |a pmid:29506378
|2 pmid
024 7 _ |a WOS:000436211800022
|2 WOS
024 7 _ |a altmetric:34391965
|2 altmetric
037 _ _ |a FZJ-2018-06523
082 _ _ |a 540
100 1 _ |a Matthes, Frank
|0 P:(DE-Juel1)130822
|b 0
|u fzj
245 _ _ |a Reducing Mutant Huntingtin Protein Expression in Living Cells by a Newly Identified RNA CAG Binder
260 _ _ |a Washington, DC
|c 2018
|b ACS Publ.
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 1674541218_32481
|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 Expanded CAG trinucleotide repeats in Huntington’s disease (HD) are causative for neurotoxicity. The mutant CAG repeat RNA encodes neurotoxic polyglutamine proteins and can lead to a toxic gain of function by aberrantly recruiting RNA-binding proteins. One of these is the MID1 protein, which induces aberrant Huntingtin (HTT) protein translation upon binding. Here we have identified a set of CAG repeat binder candidates by in silico methods. One of those, furamidine, reduces the level of binding of HTT mRNA to MID1 and other target proteins in vitro. Metadynamics calculations, fairly consistent with experimental data measured here, provide hints about the binding mode of the ligand. Importantly, furamidine also decreases the protein level of HTT in a HD cell line model. This shows that small molecules masking RNA–MID1 interactions may be active against mutant HTT protein in living cells.
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
|0 G:(DE-HGF)POF3-574
|c POF3-574
|f POF III
|x 0
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
|0 G:(DE-HGF)POF3-511
|c POF3-511
|f POF III
|x 1
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Massari, Serena
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Bochicchio, Anna
|0 P:(DE-Juel1)169975
|b 2
700 1 _ |a Schorpp, Kenji
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Schilling, Judith
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Weber, Stephanie
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Offermann, Nina
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Desantis, Jenny
|0 P:(DE-HGF)0
|b 7
700 1 _ |a Wanker, Erich
|0 P:(DE-HGF)0
|b 8
700 1 _ |a Carloni, Paolo
|0 P:(DE-Juel1)145614
|b 9
|u fzj
700 1 _ |a Hadian, Kamyar
|0 P:(DE-HGF)0
|b 10
700 1 _ |a Tabarrini, Oriana
|0 P:(DE-HGF)0
|b 11
|e Corresponding author
700 1 _ |a Rossetti, Giulia
|0 P:(DE-Juel1)145921
|b 12
|e Corresponding author
700 1 _ |a Krauss, Sybille
|0 P:(DE-HGF)0
|b 13
|e Corresponding author
773 _ _ |a 10.1021/acschemneuro.8b00027
|g Vol. 9, no. 6, p. 1399 - 1408
|0 PERI:(DE-600)2528493-9
|n 6
|p 1399 - 1408
|t ACS chemical neuroscience
|v 9
|y 2018
|x 1948-7193
856 4 _ |u https://juser.fz-juelich.de/record/857532/files/acschemneuro.8b00027.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/857532/files/acschemneuro.8b00027.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:857532
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)130822
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)169975
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 9
|6 P:(DE-Juel1)145614
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 12
|6 P:(DE-Juel1)145921
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-574
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Theory, modelling and simulation
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Supercomputing & Big Data
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Computational Science and Mathematical Methods
|x 1
914 1 _ |y 2018
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
915 _ _ |a Free to read
|0 LIC:(DE-HGF)PublisherOA
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b ACS CHEM NEUROSCI : 2017
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
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-82)080012_20140620
|k JARA-HPC
|l JARA - HPC
|x 1
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 2
920 1 _ |0 I:(DE-Juel1)INM-9-20140121
|k INM-9
|l Computational Biomedicine
|x 3
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IAS-5-20120330
980 _ _ |a I:(DE-82)080012_20140620
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a I:(DE-Juel1)INM-9-20140121
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21