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Electrophysiological Properties from Computations at a Single Voltage: Testing Theory with Stochastic Simulations
We use stochastic simulations to investigate the performance of two recently developed methods for calculating the free energy profiles of ion channels and their electrophysiological properties, such as current–voltage dependence and reversal potential, from molecular dynamics simulations at a singl...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8148522/ https://www.ncbi.nlm.nih.gov/pubmed/34066581 http://dx.doi.org/10.3390/e23050571 |
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author | Wilson, Michael A. Pohorille, Andrew |
author_facet | Wilson, Michael A. Pohorille, Andrew |
author_sort | Wilson, Michael A. |
collection | PubMed |
description | We use stochastic simulations to investigate the performance of two recently developed methods for calculating the free energy profiles of ion channels and their electrophysiological properties, such as current–voltage dependence and reversal potential, from molecular dynamics simulations at a single applied voltage. These methods require neither knowledge of the diffusivity nor simulations at multiple voltages, which greatly reduces the computational effort required to probe the electrophysiological properties of ion channels. They can be used to determine the free energy profiles from either forward or backward one-sided properties of ions in the channel, such as ion fluxes, density profiles, committor probabilities, or from their two-sided combination. By generating large sets of stochastic trajectories, which are individually designed to mimic the molecular dynamics crossing statistics of models of channels of trichotoxin, p7 from hepatitis C and a bacterial homolog of the pentameric ligand-gated ion channel, GLIC, we find that the free energy profiles obtained from stochastic simulations corresponding to molecular dynamics simulations of even a modest length are burdened with statistical errors of only 0.3 kcal/mol. Even with many crossing events, applying two-sided formulas substantially reduces statistical errors compared to one-sided formulas. With a properly chosen reference voltage, the current–voltage curves can be reproduced with good accuracy from simulations at a single voltage in a range extending for over 200 mV. If possible, the reference voltages should be chosen not simply to drive a large current in one direction, but to observe crossing events in both directions. |
format | Online Article Text |
id | pubmed-8148522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81485222021-05-26 Electrophysiological Properties from Computations at a Single Voltage: Testing Theory with Stochastic Simulations Wilson, Michael A. Pohorille, Andrew Entropy (Basel) Article We use stochastic simulations to investigate the performance of two recently developed methods for calculating the free energy profiles of ion channels and their electrophysiological properties, such as current–voltage dependence and reversal potential, from molecular dynamics simulations at a single applied voltage. These methods require neither knowledge of the diffusivity nor simulations at multiple voltages, which greatly reduces the computational effort required to probe the electrophysiological properties of ion channels. They can be used to determine the free energy profiles from either forward or backward one-sided properties of ions in the channel, such as ion fluxes, density profiles, committor probabilities, or from their two-sided combination. By generating large sets of stochastic trajectories, which are individually designed to mimic the molecular dynamics crossing statistics of models of channels of trichotoxin, p7 from hepatitis C and a bacterial homolog of the pentameric ligand-gated ion channel, GLIC, we find that the free energy profiles obtained from stochastic simulations corresponding to molecular dynamics simulations of even a modest length are burdened with statistical errors of only 0.3 kcal/mol. Even with many crossing events, applying two-sided formulas substantially reduces statistical errors compared to one-sided formulas. With a properly chosen reference voltage, the current–voltage curves can be reproduced with good accuracy from simulations at a single voltage in a range extending for over 200 mV. If possible, the reference voltages should be chosen not simply to drive a large current in one direction, but to observe crossing events in both directions. MDPI 2021-05-06 /pmc/articles/PMC8148522/ /pubmed/34066581 http://dx.doi.org/10.3390/e23050571 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wilson, Michael A. Pohorille, Andrew Electrophysiological Properties from Computations at a Single Voltage: Testing Theory with Stochastic Simulations |
title | Electrophysiological Properties from Computations at a Single Voltage: Testing Theory with Stochastic Simulations |
title_full | Electrophysiological Properties from Computations at a Single Voltage: Testing Theory with Stochastic Simulations |
title_fullStr | Electrophysiological Properties from Computations at a Single Voltage: Testing Theory with Stochastic Simulations |
title_full_unstemmed | Electrophysiological Properties from Computations at a Single Voltage: Testing Theory with Stochastic Simulations |
title_short | Electrophysiological Properties from Computations at a Single Voltage: Testing Theory with Stochastic Simulations |
title_sort | electrophysiological properties from computations at a single voltage: testing theory with stochastic simulations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8148522/ https://www.ncbi.nlm.nih.gov/pubmed/34066581 http://dx.doi.org/10.3390/e23050571 |
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