001     904540
005     20240625095123.0
024 7 _ |a 10.3390/molecules26113384
|2 doi
024 7 _ |a 1420-3049
|2 ISSN
024 7 _ |a 2128/30619
|2 Handle
024 7 _ |a 34205049
|2 pmid
024 7 _ |a WOS:000660377100001
|2 WOS
037 _ _ |a FZJ-2021-06110
082 _ _ |a 540
100 1 _ |a Berdnikova, Daria V.
|0 0000-0002-0787-5753
|b 0
245 _ _ |a Role and Perspective of Molecular Simulation-Based Investigation of RNA–Ligand Interaction: From Small Molecules and Peptides to Photoswitchable RNA Binding
260 _ _ |a Basel
|c 2021
|b MDPI
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 1644824906_31777
|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 Aberrant RNA–protein complexes are formed in a variety of diseases. Identifying the ligands that interfere with their formation is a valuable therapeutic strategy. Molecular simulation, validated against experimental data, has recently emerged as a powerful tool to predict both the pose and energetics of such ligands. Thus, the use of molecular simulation may provide insight into aberrant molecular interactions in diseases and, from a drug design perspective, may allow for the employment of less wet lab resources than traditional in vitro compound screening approaches. With regard to basic research questions, molecular simulation can support the understanding of the exact molecular interaction and binding mode. Here, we focus on examples targeting RNA–protein complexes in neurodegenerative diseases and viral infections. These examples illustrate that the strategy is rather general and could be applied to different pharmacologically relevant approaches. We close this study by outlining one of these approaches, namely the light-controllable association of small molecules with RNA, as an emerging approach in RNA-targeting therapy.
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 Carloni, Paolo
|0 P:(DE-Juel1)145614
|b 1
700 1 _ |a Krauß, Sybille
|0 P:(DE-Juel1)145921
|b 2
|e Corresponding author
700 1 _ |a Rossetti, Giulia
|0 P:(DE-Juel1)145921
|b 3
|e Corresponding author
|u fzj
773 _ _ |a 10.3390/molecules26113384
|g Vol. 26, no. 11, p. 3384 -
|0 PERI:(DE-600)2008644-1
|n 11
|p 3384 -
|t Molecules
|v 26
|y 2021
|x 1420-3049
856 4 _ |u https://juser.fz-juelich.de/record/904540/files/molecules-26-03384.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:904540
|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 0000-0002-0787-5753
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)145614
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 2
|6 P:(DE-Juel1)145921
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)145921
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 DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2021-05-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-05-04
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2021-05-04
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b MOLECULES : 2019
|d 2021-05-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2021-05-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2021-05-04
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-05-04
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2021-05-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2021-05-04
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2021-05-04
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2021-05-04
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2021-05-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2021-05-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2021-05-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2021-05-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2021-05-04
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