000858888 001__ 858888
000858888 005__ 20240625095120.0
000858888 0247_ $$2doi$$a10.1371/journal.pcbi.1006642
000858888 0247_ $$2ISSN$$a1553-734X
000858888 0247_ $$2ISSN$$a1553-7358
000858888 0247_ $$2Handle$$a2128/21501
000858888 0247_ $$2pmid$$apmid:30521520
000858888 0247_ $$2WOS$$aWOS:000454835100039
000858888 0247_ $$2altmetric$$aaltmetric:52319100
000858888 037__ $$aFZJ-2018-07724
000858888 082__ $$a610
000858888 1001_ $$0P:(DE-Juel1)169975$$aBochicchio, Anna$$b0$$eCorresponding author$$ufzj
000858888 245__ $$aMolecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study
000858888 260__ $$aSan Francisco, Calif.$$bPublic Library of Science$$c2018
000858888 3367_ $$2DRIVER$$aarticle
000858888 3367_ $$2DataCite$$aOutput Types/Journal article
000858888 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1674540630_28768
000858888 3367_ $$2BibTeX$$aARTICLE
000858888 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000858888 3367_ $$00$$2EndNote$$aJournal Article
000858888 520__ $$aThe RNA recognition motif (RRM) is the most common RNA binding domain across eukaryotic proteins. It is therefore of great value to engineer its specificity to target RNAs of arbitrary sequence. This was recently achieved for the RRM in Rbfox protein, where four mutations R118D, E147R, N151S, and E152T were designed to target the precursor to the oncogenic miRNA 21. Here, we used a variety of molecular dynamics-based approaches to predict specific interactions at the binding interface. Overall, we have run approximately 50 microseconds of enhanced sampling and plain molecular dynamics simulations on the engineered complex as well as on the wild-type Rbfox·pre-miRNA 20b from which the mutated systems were designed. Comparison with the available NMR data on the wild type molecules (protein, RNA, and their complex) served to establish the accuracy of the calculations.Free energy calculations suggest that further improvements in affinity and selectivity are achieved by the S151T replacement.
000858888 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000858888 588__ $$aDataset connected to CrossRef
000858888 7001_ $$00000-0002-9833-4281$$aKrepl, Miroslav$$b1$$eCorresponding author
000858888 7001_ $$0P:(DE-HGF)0$$aYang, Fan$$b2
000858888 7001_ $$0P:(DE-HGF)0$$aVarani, Gabriele$$b3
000858888 7001_ $$00000-0001-6558-6186$$aSponer, Jiri$$b4
000858888 7001_ $$0P:(DE-Juel1)145614$$aCarloni, Paolo$$b5$$eCorresponding author$$ufzj
000858888 773__ $$0PERI:(DE-600)2193340-6$$a10.1371/journal.pcbi.1006642$$gVol. 14, no. 12, p. e1006642 -$$n12$$pe1006642 -$$tPLoS Computational Biology$$v14$$x1553-7358$$y2018
000858888 8564_ $$uhttps://juser.fz-juelich.de/record/858888/files/journal.pcbi.1006642.pdf$$yOpenAccess
000858888 8564_ $$uhttps://juser.fz-juelich.de/record/858888/files/journal.pcbi.1006642.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000858888 8767_ $$8PAB233524$$92018-12-02$$d2018-12-20$$eAPC$$jZahlung erfolgt$$lDeposit: PLoS$$pPCOMPBIOL-D-18-01097$$z2350 USD
000858888 909CO $$ooai:juser.fz-juelich.de:858888$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
000858888 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169975$$aForschungszentrum Jülich$$b0$$kFZJ
000858888 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145614$$aForschungszentrum Jülich$$b5$$kFZJ
000858888 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000858888 9141_ $$y2018
000858888 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000858888 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000858888 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000858888 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000858888 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bPLOS COMPUT BIOL : 2017
000858888 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal
000858888 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ
000858888 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000858888 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000858888 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000858888 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000858888 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000858888 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000858888 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000858888 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central
000858888 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000858888 920__ $$lyes
000858888 9201_ $$0I:(DE-Juel1)IAS-5-20120330$$kIAS-5$$lComputational Biomedicine$$x0
000858888 9201_ $$0I:(DE-Juel1)INM-11-20170113$$kINM-11$$lJara-Institut Quantum Information$$x1
000858888 9201_ $$0I:(DE-Juel1)INM-9-20140121$$kINM-9$$lComputational Biomedicine$$x2
000858888 980__ $$ajournal
000858888 980__ $$aVDB
000858888 980__ $$aI:(DE-Juel1)IAS-5-20120330
000858888 980__ $$aI:(DE-Juel1)INM-11-20170113
000858888 980__ $$aI:(DE-Juel1)INM-9-20140121
000858888 980__ $$aAPC
000858888 980__ $$aUNRESTRICTED
000858888 9801_ $$aAPC
000858888 9801_ $$aFullTexts