Literature DB >> 17876574

Estimating the dielectric constant of the channel protein and pore.

Jin Aun Ng1, Taira Vora, Vikram Krishnamurthy, Shin-Ho Chung.   

Abstract

When modelling biological ion channels using Brownian dynamics (BD) or Poisson-Nernst-Planck theory, the force encountered by permeant ions is calculated by solving Poisson's equation. Two free parameters needed to solve this equation are the dielectric constant of water in the pore and the dielectric constant of the protein forming the channel. Although these values can in theory be deduced by various methods, they do not give a reliable answer when applied to channel-like geometries that contain charged particles. To determine the appropriate values of the dielectric constants, here we solve the inverse problem. Given the structure of the MthK channel, we attempt to determine the values of the protein and pore dielectric constants that minimize the discrepancies between the experimentally-determined current-voltage curve and the curve obtained from BD simulations. Two different methods have been applied to determine these values. First, we use all possible pairs of the pore dielectric constant of water, ranging from 20 to 80 in steps of 10, and the protein dielectric constant of 2-10 in steps of 2, and compare the simulated results with the experimental values. We find that the best agreement is obtained with experiment when a protein dielectric constant of 2 and a pore water dielectric constant of 60 is used. Second, we employ a learning-based stochastic optimization algorithm to pick out the optimum combination of the two dielectric constants. From the algorithm we obtain an optimum value of 2 for the protein dielectric constant and 64 for the pore dielectric constant.

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Year:  2007        PMID: 17876574     DOI: 10.1007/s00249-007-0218-3

Source DB:  PubMed          Journal:  Eur Biophys J        ISSN: 0175-7571            Impact factor:   1.733


  31 in total

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Authors:  Taira Vora; Ben Corry; Shin-Ho Chung
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Journal:  Biophys J       Date:  2007-04-13       Impact factor: 4.033

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10.  Gating kinetics of Ca2+-activated K+ channels from rat muscle incorporated into planar lipid bilayers. Evidence for two voltage-dependent Ca2+ binding reactions.

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Journal:  J Gen Physiol       Date:  1983-10       Impact factor: 4.086

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  12 in total

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2.  Synthetic chloride-selective carbon nanotubes examined by using molecular and stochastic dynamics.

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3.  Conduction of Na+ and K+ through the NaK channel: molecular and Brownian dynamics studies.

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Journal:  Biophys J       Date:  2008-05-02       Impact factor: 4.033

4.  Ion conduction in ligand-gated ion channels: Brownian dynamics studies of four recent crystal structures.

Authors:  Chen Song; Ben Corry
Journal:  Biophys J       Date:  2010-02-03       Impact factor: 4.033

5.  The ionization state of D37 in E. coli porin OmpF and the nature of conductance fluctuations in D37 mutants.

Authors:  Maarten Vrouenraets; Henk Miedema
Journal:  Eur Biophys J       Date:  2010-06-04       Impact factor: 1.733

6.  Simulation of Gating Currents of the Shaker K Channel Using a Brownian Model of the Voltage Sensor.

Authors:  Luigi Catacuzzeno; Fabio Franciolini
Journal:  Biophys J       Date:  2019-10-08       Impact factor: 4.033

7.  Membrane Position Dependency of the pKa and Conductivity of the Protein Ion Channel.

Authors:  Nikolay A Simakov; Maria G Kurnikova
Journal:  J Membr Biol       Date:  2018-01-16       Impact factor: 1.843

8.  Computational study of the transmembrane domain of the acetylcholine receptor.

Authors:  Chen Song; Ben Corry
Journal:  Eur Biophys J       Date:  2009-05-23       Impact factor: 1.733

9.  Testing the applicability of Nernst-Planck theory in ion channels: comparisons with Brownian dynamics simulations.

Authors:  Chen Song; Ben Corry
Journal:  PLoS One       Date:  2011-06-23       Impact factor: 3.240

10.  Computational design of a carbon nanotube fluorofullerene biosensor.

Authors:  Tamsyn A Hilder; Ron J Pace; Shin-Ho Chung
Journal:  Sensors (Basel)       Date:  2012-10-12       Impact factor: 3.576

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