Ion Channel Models Research Articles

Memristor-based model of neuronal excitability and synaptic potentiation.

In this manuscript, we investigate the memristor-based implementation of neuronal ion channels in a mathematical model and an experimental circuit for a neuronal oscillator. We used a FitzHugh-Nagumo equation system describing neuronal excitability. Non-linearities introduced by the voltage-gated ion channels were modeled using memristive devices. We implemented three basic neuronal excitability modes including the excitable mode corresponding to a single spike generation, self-oscillation stable limit cycle mode with periodic spike trains and bistability between a fixed point and a limit cycle. We also found the spike-burst activity of mathematical and experimental models under certain system parameters. Modeling synaptic transmission, we simulated postsynaptic response triggered by periodic pulse stimulation. We found that due to the charge accumulation effect in the memristive device, the electronic synapse implemented a qualitatively bio-plausible synapse with a potentiation effect with increasing amplitude of the response triggered by a spike sequence.

A PNP ion channel deep learning solver with local neural network and finite element input data

Abstract This paper presents a deep learning method for solving an improved one-dimensional Poisson–Nernst–Planck ion channel (PNPic) model, called the PNPic deep learning solver. The solver combines a novel local neural network, adapted from the neural network with local converging inputs, with an efficient PNPic finite element solver, developed in this work. In particular, the local neural network is extended to handle the complexities of the PNPic model—a system of nonlinear convection–diffusion and elliptic equations with multiple subdomains connected by interface conditions. The PNPic finite element solver efficiently generates input and reference datasets for fast training the local neural network, as well as input datasets for quickly predicting PNPic solutions with high accuracy for a family of PNPic models. Initial numerical tests, involving perturbations of model parameters and interface locations, demonstrate that the PNPic deep learning solver can generate highly accurate numerical solutions.

Reverse-engineered models reveal differential membrane properties of autonomic and cutaneous unmyelinated fibers.

Unmyelinated C-fibers constitute the vast majority of axons in peripheral nerves and play key roles in homeostasis and signaling pain. However, little is known about their ion channel expression, which controls their firing properties. Also, because of their small diameters (~ 1 μm), it has not been possible to characterize their membrane properties using voltage clamp. We developed a novel library of isoform-specific ion channel models to serve as the basis functions of our C-fiber models. We then developed a particle swarm optimization (PSO) framework that used the isoform-specific ion channel models to reverse engineer C-fiber membrane properties from measured autonomic and cutaneous C-fiber conduction responses. Our C-fiber models reproduced experimental conduction velocity, chronaxie, action potential duration, intracellular threshold, and paired pulse recovery cycle. The models also matched experimental activity-dependent slowing, a property not included in model optimization. We found that simple conduction responses, characterizing the action potential, were controlled by similar membrane properties in both the autonomic and cutaneous C-fiber models, but complicated conduction response, characterizing the afterpotenials, were controlled by differential membrane properties. The unmyelinated C-fiber models constitute important tools to study autonomic signaling, assess the mechanisms of pain, and design bioelectronic devices. Additionally, the novel reverse engineering approach can be applied to generate models of other neurons where voltage clamp data are not available.

Nernst equilibrium, rectification, and saturation: Insights into ion channel behavior

The dissipation of electrochemical gradients through ion channels plays a central role in biology. Herein we use voltage-responsive kinetic models of ion channels to explore how electrical and chemical potentials differentially influence ion transport properties. These models demonstrate how electrically driven flux is greater than the Nernstian equivalent chemically driven flux yet still perfectly cancels when the two gradients oppose each other. We find that the location and relative stability of ion-binding sites dictates rectification properties by shifting the location of the most voltage-sensitive transitions. However, these rectification properties invert when bulk concentrations increase relative to the binding-site stabilities, moving the rate-limiting steps from uptake into a relatively empty channel to release from an ion-blocked full channel. Additionally, the origin of channel saturation is shown to depend on the free energy of uptake relative to bulk concentrations. Collectively these insights provide a framework for interpreting and predicting how channel properties manifest in electrochemical transport behavior.

Alamethicin channel inactivation caused by voltage-driven flux of alamethicin

We show that voltage alone can inactivate alamethicin channels, which has been previously observed for monazomycin and suzukacillin channels. The voltage required to trigger inactivation is above the potential to form channels, and, like with channel activation, this threshold reduces with increasing peptide concentration and membrane fluidity. Since similar monazomycin channels inactivate via channel break up and translocation, we hypothesized that inactivation of alamethicin channels occurs via the same mechanism. Our data prove this hypothesis to be true through two experiments. First, we show that inactivation of channels at positive voltages when peptides are supplied to only the cis side correlates to new channel activity on the trans side at negative potentials. This result indicates translocation of alamethicin peptides occurs only during voltage-induced inactivation. Second, we measured the ratio of steady-state (with inactivation) to ideal (without inactivation) conductance versus voltage for membranes with equal amounts of alamethicin on both sides and used these values to quantify alamethicin flux. Plotting flux versus steady-state conductance across multiple alamethicin concentrations shows a single linear dependence, signifying that translocated peptides originate from active channels that break up under prolonged voltage. Given the frequent use of alamethicin as model ion channels, these results add important understanding of their kinetic responses when subjected to prolonged, high voltages.

DendroTweaks: An interactive approach for unraveling dendritic dynamics.

Neurons rely on the interplay between dendritic morphology and ion channels to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental in exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce DendroTweaks - a toolbox designed to illuminate how morpho-electric properties map to dendritic events and how these dendritic events shape neuronal output. DendroTweaks features a web-based graphical interface, where users can explore single-cell neuronal models and adjust their morphological and biophysical parameters with real-time visual feedback. In particular, DendroTweaks is tailored to interactive fine-tuning of subcellular properties, such as kinetics and distributions of ion channels, as well as the dynamics and allocation of synaptic inputs. It offers an automated approach for standardization and refinement of voltage-gated ion channel models to make them more comprehensible and reusable. The toolbox allows users to run various experimental protocols and record data from multiple dendritic and somatic locations, thereby enhancing model validation. Finally, it aims to deepen our understanding of which dendritic properties are essential for neuronal input-output transformation. Using this knowledge, one can simplify models through a built-in morphology reduction algorithm and export them for further use in faster, more interpretable networks. With DendroTweaks, users can gain better control and understanding of their models, advancing research on dendritic input-output transformations and their role in network computations.

Computational investigation in inhibitory effects of amantadine on classical swine fever virus p7 ion channel activity

Classical swine fever virus (CSFV) p7 viroporin plays crucial roles in cellular ion balance and permeabilization. The antiviral drug amantadine effectively inhibits viral replication by blocking the activity of CSFV p7 viroporin. However, little information is available for the binding mode of amantadine with CSFV p7 viroporin, due to the lack of a known polymer structure for CSFV p7. In this study, we employed AlphaFold2 to predict CSFV p7 structures. Subsequently, we conducted a docking study to investigate the binding sites of amantadine to CSFV p7. Computational analysis showed that CSFV p7 forms a pore channel in a hexameric structure. Furthermore, molecular dynamics (MD) simulations and mutant analyses further suggest that CSFV p7 likely exists as a hexamer. Docking studies and MD simulations showed that amantadine interacts with the hydrophibic regions of tetramer and pentamer, as well as with the hydrophobic pore channel of the hexamer. Considering the potential hexameric assembly of CSFV p7, along with docking results, MD simulations, and the characteristics of the gated ion channels, we propose a model of CSFV p7 ion channel based on its hexameric configuration. In this model, residues E21, Y25, and R34 are suggested to selectively recruit and dehydrate ions, while residues L28 and L31 likely act as hydrophobic constrictors, thereby restricting the free movement of water. The binding of amantadine to residues I20, E21, V24 and Y25 effectively blocks ion transport. However, this proposed molecular model requires experimental validation. Our findings give a structural insight into the models of CSFV p7 as an ion channel and provide a molecular explanation for the inhibition effects of amantadine on CSFV p7-mediated ion channel conductance.

Computer modeling of polypeptide toxins and ion channels: Achieving success through failure

Computer modeling of polypeptide toxins and ion channels: Achieving success through failure

Nernst Equilibrium, Rectification, and Saturation: Insights into Ion Channel Behavior.

The dissipation of electrochemical gradients through ion channels plays a central role in biology. Herein we use voltage responsive kinetic models of ion channels to explore how electrical and chemical potentials differentially influence ion transport properties. These models demonstrate how electrically driven flux is greater than the Nernstian equivalent chemically driven flux, yet still perfectly cancels when the two gradients oppose each other. We find that the location and relative stability of ion binding sites dictates rectification properties by shifting the location of the most voltage sensitive transitions. However, these rectification properties invert when bulk concentrations increase relative to the binding site stabilities, moving the rate limiting steps from uptake into a relatively empty channel to release from an ion-blocked full channel. Additionally, the origin of channel saturation is shown to depend on the free energy of uptake relative to bulk concentrations. Collectively these insights provide framework for interpreting and predicting how channel properties manifest in electrochemical transport behavior.

Voltage-Gated Ion Channels Are Transcriptional Targets of Sox10 during Oligodendrocyte Development.

The transcription factor Sox10 is an important determinant of oligodendroglial identity and influences oligodendroglial development and characteristics at various stages. Starting from RNA-seq data, we here show that the expression of several voltage-gated ion channels with known expression and important function in oligodendroglial cells depends upon Sox10. These include the Nav1.1, Cav2.2, Kv1.1, and Kir4.1 channels. For each of the four encoding genes, we found at least one regulatory region that is activated by Sox10 in vitro and at the same time bound by Sox10 in vivo. Cell-specific deletion of Sox10 in oligodendroglial cells furthermore led to a strong downregulation of all four ion channels in a mouse model and thus in vivo. Our study provides a clear functional link between voltage-gated ion channels and the transcriptional regulatory network in oligodendroglial cells. Furthermore, our study argues that Sox10 exerts at least some of its functions in oligodendrocyte progenitor cells, in myelinating oligodendrocytes, or throughout lineage development via these ion channels. By doing so, we present one way in which oligodendroglial development and properties can be linked to neuronal activity to ensure crosstalk between cell types during the development and function of the central nervous system.

Two co-existing and opposing mechanisms of proton transfer in one-dimensional open-end water chains.

The proton transport in one-dimensional (1D) confined water chains has been extensively studied as a model for ion channels in cell membrane and fuel cell. However, the mechanistic understanding of the proton transfer (PT) process in 1D water chains remains incomplete. In this study, we demonstrate that the two limiting structures of the hydrated excess proton, H5O2+ (Zundel) and H3O+ (linear H7O3+), undergo a change in dominance as the water chain grows, causing two co-existing and opposing PT mechanisms. Specifically, H5O2+ is stable in the middle of the chain, whereas H3O+ serves as a transition state (TS). Except for this region, H3O+ is stabilized while H5O2+ serves as a TS. The interaction analysis shows that the electrostatic interaction plays a crucial role in the difference in PT mechanisms. Our work fills a knowledge gap between the various PT mechanisms reported in bulk water and long 1D water chains, contributing to a deeper understanding of biological ion channels at the atomic level.

Flexible nanofiber composite solar evaporator for simultaneous interfacial evaporation and heavy metal ion adsorption

Solar driven interfacial evaporation (SDIE) is promising in harvesting fresh water because of its low cost, environmental friendless and sustainability; however, it is desirable but still difficult to achieve high efficiency heavy metal ion removal during SDIE. Here, we develop a superhydrophilic and stretchable nanofiber composite solar evaporator via carbon nanofiber (CNF) assembly on polymer fiber surface and then polydopamine (PDA) modification. The evaporation rate reaches as high as 1.456 kg m−2 h−1 with the evaporation efficiency of 91.40 %. The functional groups of PDA endow the nanofiber composite with good heavy metal ion adsorption performance that is proved to be an endothermic process, and SDIE can induce the increase of interface temperature and localized ion concentration and hence promote the Cr (VI) adsorption. One the other hand, NaCl ions are able to diffuse into body water without salt precipitation by virtue of powerful water transportation capability of the evaporation device. The dual Cr (VI) adsorption and NaCl ion diffusion channels ensure simultaneous and high efficiency Cr (VI) removal and seawater interface evaporation. SDIE accelerated heavy metal ion adsorption and the dual ion channel model provide inspiration to design multifunctional solar evaporator materials for waste water purification.

Dynamics accelerate the kinetics of ion diffusion through channels: Continuous-time random walk models beyond the mean field approximation.

Ion channels are proteins that play a significant role in physiological processes, including neuronal excitability and signal transduction. However, the precise mechanisms by which these proteins facilitate ion diffusion through cell membranes are not well understood. This is because experimental techniques to characterize ion channel activity operate on a time scale too large to understand the role of the various protein conformations on diffusion. Meanwhile, computational approaches operate on a time scale too short to rationalize the observed behavior at the microscopic scale. In this paper, we present a continuous-time random walk model that aims to bridge the scales between the atomistic models of ion channels and the experimental measurement of their conductance. We show how diffusion slows down in complex systems by using 3D lattices that map out the pore geometry of two channels: Nav1.7 and gramicidin. We also introduce spatial and dynamic site disorder to account for system heterogeneity beyond the mean field approximation. Computed diffusion coefficients show that an increase in spatial disorder slows down diffusion kinetics, while dynamic disorder has the opposite effect. Our results imply that microscopic or phenomenological models based on the potential of mean force data overlook the functional importance of protein dynamics on ion diffusion through channels.

Computational Insights into the Conformational Dynamics of HIV-1 Vpr in a Lipid Bilayer for Ion Channel Modeling.

HIV-1 Vpr is a multifunctional accessory protein consisting of 96 amino acids that play a critical role in viral pathogenesis. Among its diverse range of activities, Vpr can create a cation-selective ion channel within the plasma membrane. However, the oligomeric state of this channel has not yet been elucidated. In this study, we investigated the conformational dynamics of Vpr helices to model the ion channel topology. First, we employed a series of multiscale simulations to investigate the specific structure of monomeric Vpr in a membrane model. During the lipid bilayer self-assembly coarse grain simulation, the C-terminal helix (residues 56-77) effectively formed the transmembrane region, while the N-terminal helix exhibited an amphipathic nature by associating horizontally with a single leaflet. All-atom molecular dynamics (MD) simulations of full-length Vpr inside a phospholipid bilayer show that the C-terminal helix remains very stable inside the bilayer core in a vertical orientation. Subsequently, using the predicted C-terminal helix orientation and conformation, various oligomeric states (ranging from tetramer to heptamer) possibly forming the Vpr ion channel were built and further evaluated. Among these models, the pentameric form exhibited consistent stability in MD simulations and displayed a compatible conformation for a water-assisted ion transport mechanism. This study provides structural insights into the ion channel activity of the Vpr protein and the foundation for developing therapeutics against HIV-1 Vpr-related conditions.

Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR-ORd in silico model output.

Torsades de pointes (TdP) is a type of ventricular arrhythmia that can lead to sudden cardiac death. Drug-induced TdP has been an important concern for researchers and international regulatory boards. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative was proposed that integrates in vitro testing and computational models of cardiac ion channels and human cardiomyocyte cells to evaluate the proarrhythmic risk of drugs. The TdP risk classification performance using only a single TdP metric may require some improvements because of information limitations and the instability of generalizing results. This study evaluates the performance of TdP metrics from the in silico simulations of the Tomek-O'Hara Rudy (ToR-ORd) ventricular cell model for classifying the TdP risk of drugs. We utilized these metrics as an input to an artificial neural network (ANN)-based classifier. The ANN model was optimized through hyperparameter tuning using the grid search (GS) method to find the optimal model. The study outcomes show an area under the curve (AUC) value of 0.979 for the high-risk category, 0.791 for the intermediate-risk category, and 0.937 for the low-risk category. Therefore, this study successfully demonstrates the capability of the ToR-ORd ventricular cell model in classifying the TdP risk into three risk categories, providing new insights into TdP risk prediction methods.

Quantifying the efficacy of voltage protocols in characterising ion channel kinetics: A novel information-theoretic approach.

Voltage-clamp experiments are commonly utilised to characterise cellular ion channel kinetics. In these experiments, cells are stimulated using a known time-varying voltage, referred to as the voltage protocol, and the resulting cellular response, typically in the form of current, is measured. Parameters of models that describe ion channel kinetics are then estimated by solving an inverse problem which aims to minimise the discrepancy between the predicted response of the model and the actual measured cell response. In this paper, a novel framework to evaluate the information content of voltage-clamp protocols in relation to ion channel model parameters is presented. Additional quantitative information metrics that allow for comparisons among various voltage protocols are proposed. These metrics offer a foundation for future optimal design frameworks to devise novel, information-rich protocols. The efficacy of the proposed framework is evidenced through the analysis of seven voltage protocols from the literature. By comparing known numerical results for inverse problems using these protocols with the information-theoretic metrics, the proposed approach is validated. The essential steps of the framework are: (i) generate random samples of the parameters from chosen prior distributions; (ii) run the model to generate model output (current) for all samples; (iii) construct reduced-dimensional representations of the time-varying current output using proper orthogonal decomposition (POD); (iv) estimate information-theoretic metrics such as mutual information, entropy equivalent variance, and conditional mutual information using non-parametric methods; (v) interpret the metrics; for example, a higher mutual information between a parameter and the current output suggests the protocol yields greater information about that parameter, resulting in improved identifiability; and (vi) integrate the information-theoretic metrics into a single quantitative criterion, encapsulating the protocol's efficacy in estimating model parameters.

Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold

ABSTRACT Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel – CaV1.1, and human voltage-gated potassium (KV) channel – KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.

Equivalent circuits in nanopore-based electrochemical systems

Single-nanopore membranes constitute physical models for biological ion channels and are also central to electrochemical energy transducers, logical circuits in sensors and actuators, and converters in bioelectrical interfaces. The membrane potential, defined as the electrical potential difference established between two different ionic solutions separated by the membrane, is crucial for the electrical coupling of nanofluidic concentration cells to electronic components such as load capacitors and resistors. Here we analyze, theoretically and experimentally, the validity of basic principles governing electric circuit theory such as the Thévenin equivalent circuit, the additivity of voltages and resistances, and the Millman theorem in nanopore-based concentration cells. To this end, we consider a wide range of practical conditions with respect to salt concentrations, nanopore asymmetry, electrical capacitances, and series and parallel arrangements. Additionally, we investigate the scaling of the single pore results to the multipore case. The results obtained suggest that basic circuit models offer approximate tools to guide the analysis of the nanofluidic concentration cells.

The Nicotinic Acetylcholine Receptor and Its Pentameric Homologs: Toward an Allosteric Mechanism of Signal Transduction at the Atomic Level.

The nicotinic acetylcholine receptor has served, since its biochemical identification in the 1970s, as a model of an allosteric ligand-gated ion channel mediating signal transition at the synapse. In recent years, the application of X-ray crystallography and high-resolution cryo-electron microscopy, together with molecular dynamic simulations of nicotinic receptors and homologs, have opened a new era in the understanding of channel gating by the neurotransmitter. They reveal, at atomic resolution, the diversity and flexibility of the multiple ligand-binding sites, including recently discovered allosteric modulatory sites distinct from the neurotransmitter orthosteric site, and the conformational dynamics of the activation process as a molecular switch linking these multiple sites. The model emerging from these studies paves the way for a new pharmacology based, first, upon the occurrence of an original mode of indirect allosteric modulation, distinct from a steric competition for a single and rigid binding site, and second, the design of drugs that specifically interact with privileged conformations of the receptor such as agonists, antagonists, and desensitizers. Research on nicotinic receptors is still at the forefront of understanding the mode of action of drugs on the nervous system.

Expression of model ion channel currents generated using neuron in silico software in real time using dynamic clamp

Expression of model ion channel currents generated using neuron in silico software in real time using dynamic clamp