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000255840 005__ 20210129220543.0
000255840 037__ $$aFZJ-2015-05951
000255840 1001_ $$0P:(DE-HGF)0$$aFinnerty, Justin$$b0$$eCorresponding author
000255840 1112_ $$aCECAM Workshop: Computational approaches to chemical senses$$cJülich$$d2015-09-09 - 2015-09-11$$wGermany
000255840 245__ $$aStatistical mechanical models for cation selectivity in biological channels
000255840 260__ $$c2015
000255840 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1446101995_9941$$xInvited
000255840 3367_ $$033$$2EndNote$$aConference Paper
000255840 3367_ $$2DataCite$$aOther
000255840 3367_ $$2ORCID$$aLECTURE_SPEECH
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000255840 3367_ $$2BibTeX$$aINPROCEEDINGS
000255840 520__ $$aCation selective channels constitute the gate for ion currents through the cell membrane. These proteins select between the physiologically important Na+, K+ and Ca2+ cations. Here we present a statistical mechanical model based on atomistic structural information and without tuned parameters that reproduces the selectivity of bacterial Na+ and Ca2+ selective ion channels, the only such channels for which we have X-ray structures. 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.
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000255840 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x1
000255840 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x2
000255840 7001_ $$0P:(DE-Juel1)161525$$aPeyser, Alexander$$b1
000255840 7001_ $$0P:(DE-Juel1)145614$$aCarloni, Paolo$$b2
000255840 909CO $$ooai:juser.fz-juelich.de:255840$$pVDB
000255840 9101_ $$0I:(DE-588b)1026307295$$6P:(DE-HGF)0$$aGerman Research School for Simulation Sciences$$b0$$kGRS Aachen
000255840 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161525$$aForschungszentrum Jülich GmbH$$b1$$kFZJ
000255840 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145614$$aForschungszentrum Jülich GmbH$$b2$$kFZJ
000255840 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000255840 9131_ $$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$$x1
000255840 9141_ $$y2015
000255840 920__ $$lyes
000255840 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000255840 9201_ $$0I:(DE-82)080012_20140620$$kJARA-HPC$$lJARA - HPC$$x1
000255840 9201_ $$0I:(DE-Juel1)IAS-5-20120330$$kIAS-5$$lComputational Biomedicine$$x2
000255840 9201_ $$0I:(DE-Juel1)INM-9-20140121$$kINM-9$$lComputational Biomedicine$$x3
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000255840 980__ $$aI:(DE-Juel1)INM-9-20140121
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