001     256296
005     20240625095122.0
024 7 _ |a 10.1371/journal.pone.0138679
|2 doi
024 7 _ |a 2128/9367
|2 Handle
024 7 _ |a WOS:000362962300008
|2 WOS
024 7 _ |a altmetric:4823416
|2 altmetric
024 7 _ |a pmid:26460827
|2 pmid
037 _ _ |a FZJ-2015-06260
082 _ _ |a 500
100 1 _ |a Finnerty, Justin John
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a Cation Selectivity in Biological Cation Channels Using Experimental Structural Information and Statistical Mechanical Simulation
260 _ _ |a Lawrence, Kan.
|c 2015
|b PLoS
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 1563263045_1091
|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 Cation selective channels constitute the gate for ion currents through the cell membrane. Here we present an improved statistical mechanical model based on atomistic structural information, cation hydration state and without tuned parameters that reproduces the selectivity of biological Na+ and Ca2+ ion channels. The importance of the inclusion of step-wise cation hydration in these results confirms the essential role partial dehydration plays in the bacterial Na+ channels. The model, proven reliable against experimental data, could be straightforwardly used for designing Na+ and Ca2+ selective nanopores.
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
|0 G:(DE-HGF)POF3-511
|c POF3-511
|f POF III
|x 0
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
|0 G:(DE-HGF)POF3-574
|c POF3-574
|f POF III
|x 1
536 _ _ |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)
|0 G:(DE-Juel1)HGF-SMHB-2013-2017
|c HGF-SMHB-2013-2017
|f SMHB
|x 2
536 _ _ |a SLNS - SimLab Neuroscience (Helmholtz-SLNS)
|0 G:(DE-Juel1)Helmholtz-SLNS
|c Helmholtz-SLNS
|x 3
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Peyser, Alexander
|0 P:(DE-Juel1)161525
|b 1
|e Corresponding author
|u fzj
700 1 _ |a Carloni, Paolo
|0 P:(DE-Juel1)145614
|b 2
|u fzj
773 _ _ |a 10.1371/journal.pone.0138679
|g Vol. 10, no. 10, p. e0138679 -
|0 PERI:(DE-600)2267670-3
|n 10
|p e0138679 -
|t PLoS one
|v 10
|y 2015
|x 1932-6203
856 4 _ |u http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138679
856 4 _ |u https://juser.fz-juelich.de/record/256296/files/finnerty-2015.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/256296/files/finnerty-2015.gif?subformat=icon
|x icon
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/256296/files/finnerty-2015.jpg?subformat=icon-1440
|x icon-1440
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/256296/files/finnerty-2015.jpg?subformat=icon-180
|x icon-180
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/256296/files/finnerty-2015.jpg?subformat=icon-640
|x icon-640
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/256296/files/finnerty-2015.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:256296
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a German Research School for Simulation Sciences
|0 I:(DE-588b)1026307295
|k GRS Aachen
|b 0
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)161525
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)145614
913 1 _ |a DE-HGF
|b Key Technologies
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|2 G:(DE-HGF)POF3-500
|v Computational Science and Mathematical Methods
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|l Supercomputing & Big Data
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
|2 G:(DE-HGF)POF3-500
|v Theory, modelling and simulation
|x 1
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2015
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 Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1040
|2 StatID
|b Zoological Record
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b PLOS ONE : 2014
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
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)0199
|2 StatID
|b Thomson Reuters Master Journal List
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
920 1 _ |0 I:(DE-Juel1)IAS-5-20120330
|k IAS-5
|l Computational Biomedicine
|x 1
920 1 _ |0 I:(DE-Juel1)INM-9-20140121
|k INM-9
|l Computational Biomedicine
|x 2
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a I:(DE-Juel1)IAS-5-20120330
980 _ _ |a I:(DE-Juel1)INM-9-20140121
980 _ _ |a APC
980 _ _ |a UNRESTRICTED
980 1 _ |a APC
980 1 _ |a UNRESTRICTED
980 1 _ |a FullTexts
981 _ _ |a I:(DE-Juel1)IAS-5-20120330
981 _ _ |a I:(DE-Juel1)INM-9-20140121


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21