Dynamic Monte Carlo Simulation of Coupled Transport through a Narrow Multiply-Occupied Pore

Boda, Dezső; Csányi, Éva; Gillespie, Dirk; Kristóf, Tamás

doi:10.1021/jp406444u

Cited by 13 publications

(14 citation statements)

References 57 publications

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“…Moreover, our 1D DFT RyR profiles are very similar to cross-sectional averages of full 3D simulations (Dezs} o Boda, University of Pannonia, personal communication, 2014), so it is probable that the 1D Nernst-Planck equation used here is applicable. However, more studies on crowded channels should be done, which is work we plan to continue (64)(65)(66).…”

Section: Assumptions and Approximationsmentioning

confidence: 99%

Selecting Ions by Size in a Calcium Channel: The Ryanodine Receptor Case Study

Gillespie

Meissner

2014

Biophysical Journal

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Many calcium channels can distinguish between ions of the same charge but different size. For example, when cations are in direct competition with each other, the ryanodine receptor (RyR) calcium channel preferentially conducts smaller cations such as Li(+) and Na(+) over larger ones such as K(+) and Cs(+). Here, we analyze the physical basis for this preference using a previously established model of RyR permeation and selectivity. Like other calcium channels, RyR has four aspartate residues in its GGGIGDE selectivity filter. These aspartates have their terminal carboxyl group in the pore lumen, which take up much of the available space for permeating ions. We find that small ions are preferred by RyR because they can fit into this crowded environment more easily.

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Section: Assumptions and Approximationsmentioning

confidence: 99%

Selecting Ions by Size in a Calcium Channel: The Ryanodine Receptor Case Study

Gillespie

Meissner

2014

Biophysical Journal

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“…Yet, the Poisson-Nernst-Planck (PNP) theory can reproduce experimental data for rectifying nanopores [1,2,3,4,5,6]. Other examples include reduced models using hard sphere ions reproducing and predicting nonlinear phenomena in biological ion channels [7,8,9,10,11,12,13,14] and in nanopores [15,16]. Moreover, in many cases including nanopores and ion channels, reduced models are the only way to connect with experimental results, as the required low ion concentrations and small applied voltages are inaccessible to all-atom MD simulations.…”

Section: Introductionmentioning

confidence: 99%

Multiscale modeling of a rectifying bipolar nanopore: explicit-water versus implicit-water simulations

Ható

Valiskó

Kristóf

et al. 2017

Phys. Chem. Chem. Phys.

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In a multiscale modeling approach, we present computer simulation results for a rectifying bipolar nanopore at two modeling levels. In an all-atom model, we use explicit water to simulate ion transport directly with the molecular dynamics technique. In a reduced model, we use implicit water and apply the Local Equilibrium Monte Carlo method together with the Nernst-Planck transport equation. This hybrid method makes the fast calculation of ion transport possible at the price of lost details. We show that the implicit-water model is an appropriate representation of the explicit-water model when we look at the system at the device (i.e., input vs. output) level. The two models produce qualitatively similar behavior of the electrical current for different voltages and model parameters. Looking at the details of concentration and potential profiles, we find profound differences between the two models. These differences, however, do not influence the basic behavior of the model as a device because they do not influence the z-dependence of the concentration profiles which are the main determinants of current. These results then address an old paradox: how do reduced models, whose assumptions should break down in a nanoscale device, predict experimental data? Our simulations show that reduced models can still capture the overall device physics correctly, even though they get some important aspects of the molecular-scale physics quite wrong; reduced models work because they include the physics that is necessary from the point of view of device function. Therefore, reduced models can suffice for general device understanding and device design, but more detailed models might be needed for molecular level understanding.

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“…If one confines oneself to sinusoidal systems (as in classical impedance or dielectric spectroscopy [11,44,54,55]), one should explicitly introduce the sinusoids into the equations and not just assume that the simplified treatment of sinusoids in elementary circuit theory [56][57][58][59][60] is correct: it is not at all clear that Maxwell's equations-combined with other field equations (like Navier Stokes [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78] or PNP = drift diffusion [66,[79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94]); [joined] with constitutive equations; and boundary conditions-always have steady state solutions in the sinusoidal case. They certainly do not always have solutions that are linear functions of just the electric field…”

Section: Figurementioning

confidence: 99%

Maxwell Equations Without a Polarization Field, Using a Paradigm from Biophysics

Eisenberg¹

2020

Preprint

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Electrodynamics is usually written using polarization fields to describe changes in distribution of charge as electric fields change. This approach does not specify polarization fields uniquely from electrical measurements. Many polarization fields will produce the same electrodynamic forces and flows because only divergence of polarization enters Maxwell’s first equation, relating charge and electric field. The curl of any function can be added to a polarization field without changing the electric field at all. The divergence of the curl is always zero. To be unique, models must describe the charge distribution and how it varies. I propose a different paradigm to describe field dependent charge, i.e., the phenomenon of polarization. This operational definition of polarization has worked well in biophysics for fifty years, where a field dependent, time dependent polarization provides gating current that makes neurons respond sensitively to voltage. Theoretical estimates of polarization computed with this definition fit experimental data. I propose that operational definition be used to define polarization charge in general. Charge movement needs to be computed from a combination of electrodynamics and mechanics because ‘everything interacts with everything else’. The classical polarization field need not enter into that treatment at all. When nothing is known about polarization, it is necessary to use an approximate representation with a dielectric constant that is a single real positive number. This approximation allows important results in some cases, e.g., design of integrated circuits in silicon semiconductors, but can be seriously misleading in other cases, e.g., ionic solutions.

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Dynamic Monte Carlo Simulation of Coupled Transport through a Narrow Multiply-Occupied Pore

Cited by 13 publications

References 57 publications

Selecting Ions by Size in a Calcium Channel: The Ryanodine Receptor Case Study

Selecting Ions by Size in a Calcium Channel: The Ryanodine Receptor Case Study

Multiscale modeling of a rectifying bipolar nanopore: explicit-water versus implicit-water simulations

Maxwell Equations Without a Polarization Field, Using a Paradigm from Biophysics

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