scholarly journals A Heuristic Derived from Analysis of the Ion Channel Structural Proteome Permits the Rapid Identification of Hydrophobic Gates

2018 ◽  
Author(s):  
Shanlin Rao ◽  
Gianni Klesse ◽  
Phillip J. Stansfeld ◽  
Stephen J. Tucker ◽  
Mark S.P. Sansom
Keyword(s):  
Machine Learning ◽  
Ion Channels ◽  
Ion Channel ◽  
Functional State ◽  
Conformational Changes ◽  
Rapid Identification ◽  
Ion Permeation ◽  
Ionic Flux ◽  
Easy Method ◽  
Channel Structures

AbstractIon channel proteins control ionic flux across biological membranes through conformational changes in their transmembrane pores. An exponentially increasing number of channel structures captured in different conformational states are now being determined. However, these newly-resolved structures are commonly classified as either open or closed based solely on the physical dimensions of their pore and it is now known that more accurate annotation of their conductive state requires an additional assessment of the effect of pore hydrophobicity. A narrow hydrophobic gate region may disfavour liquid-phase water, leading to local de-wetting which will form an energetic barrier to water and ion permeation without steric occlusion of the pore. Here we quantify the combined influence of radius and hydrophobicity on pore de-wetting by applying molecular dynamics simulations and machine learning to nearly 200 ion channel structures. This allows us to propose a simple simulation-free heuristic model that rapidly and accurately predicts the presence of hydrophobic gates. This not only enables the functional annotation of new channel structures as soon as they are determined, but may also facilitate the design of novel nanopores controlled by hydrophobic gates.Significance statementIon channels are nanoscale protein pores in cell membranes. An exponentially increasing number of structures for channels means that computational methods for predicting their functional state are needed. Hydrophobic gates in ion channels result in local de-wetting of pores which functionally closes them to water and ion permeation. We use simulations of water behaviour within nearly 200 different ion channel structures to explore how the radius and hydrophobicity of pores determine their hydration vs. de-wetting behaviour. Machine learning-assisted analysis of these simulations enables us to propose a simple model for this relationship. This allows us to present an easy method for the rapid prediction of the functional state of new channel structures as they emerge.

scholarly journals A heuristic derived from analysis of the ion channel structural proteome permits the rapid identification of hydrophobic gates

2019 ◽  
Vol 116 (28) ◽  
pp. 13989-13995 ◽  
Author(s):  
Shanlin Rao ◽  
Gianni Klesse ◽  
Phillip J. Stansfeld ◽  
Stephen J. Tucker ◽  
Mark S. P. Sansom
Keyword(s):  
Ion Channel ◽  
Conformational Changes ◽  
Rapid Identification ◽  
Ionic Flux ◽  
Simple Simulation ◽  
Transmembrane Pores ◽  
Ion Channel Proteins ◽  
Phase Water ◽  
Dynamics Simulations ◽  
Channel Structures

Ion channel proteins control ionic flux across biological membranes through conformational changes in their transmembrane pores. An exponentially increasing number of channel structures captured in different conformational states are now being determined; however, these newly resolved structures are commonly classified as either open or closed based solely on the physical dimensions of their pore, and it is now known that more accurate annotation of their conductive state requires additional assessment of the effect of pore hydrophobicity. A narrow hydrophobic gate region may disfavor liquid-phase water, leading to local dewetting, which will form an energetic barrier to water and ion permeation without steric occlusion of the pore. Here we quantify the combined influence of radius and hydrophobicity on pore dewetting by applying molecular dynamics simulations and machine learning to nearly 200 ion channel structures. This allows us to propose a simple simulation-free heuristic model that rapidly and accurately predicts the presence of hydrophobic gates. This not only enables the functional annotation of new channel structures as soon as they are determined, but also may facilitate the design of novel nanopores controlled by hydrophobic gates.

scholarly journals Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

Membranes ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 672
Author(s):  
Md. Ashrafuzzaman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Ion Channels ◽  
Ion Channel ◽  
Research Trend ◽  
Cellular Processes ◽  
Research Areas ◽  
Huge Data ◽  
Functional Aspects

Ion channels are linked to important cellular processes. For more than half a century, we have been learning various structural and functional aspects of ion channels using biological, physiological, biochemical, and biophysical principles and techniques. In recent days, bioinformaticians and biophysicists having the necessary expertise and interests in computer science techniques including versatile algorithms have started covering a multitude of physiological aspects including especially evolution, mutations, and genomics of functional channels and channel subunits. In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular. With the help of available articles and information, this review provide an introduction to this novel research trend. Ion channel understanding is usually made considering the structural and functional perspectives, gating mechanisms, transport properties, channel protein mutations, etc. Focused research on ion channels and related findings over many decades accumulated huge data which may be utilized in a specialized scientific manner to fast conclude pinpointed aspects of channels. AI, ML, and DL techniques and models may appear as helping tools. This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.

Ion Channel Permeation and Selectivity

2019 ◽  
Author(s):  
Juan J. Nogueira ◽  
Ben Corry
Keyword(s):  
Ion Channels ◽  
Ion Channel ◽  
Ion Selectivity ◽  
Ionic Currents ◽  
Biological Processes ◽  
Ion Permeation ◽  
Voltage Gated ◽  
Physical Mechanisms ◽  
Theoretical Approaches ◽  
Mechanistic Insight

Many biological processes essential for life rely on the transport of specific ions at specific times across cell membranes. Such exquisite control of ionic currents, which is regulated by protein ion channels, is fundamental for the proper functioning of the cells. It is not surprising, therefore, that the mechanism of ion permeation and selectivity in ion channels has been extensively investigated by means of experimental and theoretical approaches. These studies have provided great mechanistic insight but have also raised new questions that are still unresolved. This chapter first summarizes the main techniques that have provided significant knowledge about ion permeation and selectivity. It then discusses the physical mechanisms leading to ion permeation and the explanations that have been proposed for ion selectivity in voltage-gated potassium, sodium, and calcium channels.

scholarly journals Phosphorylation-Dependent Regulation of Ryanodine Receptors

2001 ◽  
Vol 153 (4) ◽  
pp. 699-708 ◽  
Author(s):  
Steven O. Marx ◽  
Steven Reiken ◽  
Yuji Hisamatsu ◽  
Marta Gaburjakova ◽  
Jana Gaburjakova ◽  
...  
Keyword(s):  
Ion Channels ◽  
Ion Channel ◽  
Calcium Release ◽  
Ryanodine Receptors ◽  
Rapid Identification ◽  
Calcium Release Channels ◽  
Macromolecular Complexes ◽  
Protein Binding Sites ◽  
Skeletal Muscle Contraction

Ryanodine receptors (RyRs), intracellular calcium release channels required for cardiac and skeletal muscle contraction, are macromolecular complexes that include kinases and phosphatases. Phosphorylation/dephosphorylation plays a key role in regulating the function of many ion channels, including RyRs. However, the mechanism by which kinases and phosphatases are targeted to ion channels is not well understood. We have identified a novel mechanism involved in the formation of ion channel macromolecular complexes: kinase and phosphatase targeting proteins binding to ion channels via leucine/isoleucine zipper (LZ) motifs. Activation of kinases and phosphatases bound to RyR2 via LZs regulates phosphorylation of the channel, and disruption of kinase binding via LZ motifs prevents phosphorylation of RyR2. Elucidation of this new role for LZs in ion channel macromolecular complexes now permits: (a) rapid mapping of kinase and phosphatase targeting protein binding sites on ion channels; (b) predicting which kinases and phosphatases are likely to regulate a given ion channel; (c) rapid identification of novel kinase and phosphatase targeting proteins; and (d) tools for dissecting the role of kinases and phosphatases as modulators of ion channel function.

scholarly journals Molecular insights from conformational ensembles via machine learning

2019 ◽  
Author(s):  
O. Fleetwood ◽  
M.A. Kasimova ◽  
A.M. Westerlund ◽  
L. Delemotte
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Ion Channel ◽  
Conformational Changes ◽  
Molecular Simulations ◽  
Principal Component ◽  
Voltage Sensitivity ◽  
Learning Tools ◽  
Biomolecular Systems ◽  
Dynamics Simulations

ABSTRACTBiomolecular simulations are intrinsically high dimensional and generate noisy datasets of ever increasing size. Extracting important features in the data is crucial for understanding the biophysical properties of molecular processes, but remains a big challenge. Machine learning (ML) provides powerful dimensionality reduction tools. However, such methods are often criticized to resemble black boxes with limited human-interpretable insight.We use methods from supervised and unsupervised ML to efficiently create interpretable maps of important features from molecular simulations. We benchmark the performance of several methods including neural networks, random forests and principal component analysis, using a toy model with properties reminiscent of macromolecular behavior. We then analyze three diverse biological processes: conformational changes within the soluble protein calmodulin, ligand binding to a G protein-coupled receptor and activation of an ion channel voltage-sensor domain, unravelling features critical for signal transduction, ligand binding and voltage sensing. This work demonstrates the usefulness of ML in understanding biomolecular states and demystifying complex simulations.STATEMENT OF SIGNIFICANCEUnderstanding how biomolecules function requires resolving the ensemble of structures they visit. Molecular dynamics simulations compute these ensembles and generate large amounts of data that can be noisy and need to be condensed for human interpretation. Machine learning methods are designed to process large amounts of data, but are often criticized for their black-box nature and have historically been modestly used in the analysis of biomolecular systems. We demonstrate how machine learning tools can provide an interpretable overview of important features in a simulation dataset. We develop a protocol to quickly perform data-driven analysis of molecular simulations. This protocol is applied to identify the molecular basis of ligand binding to a receptor and of voltage sensitivity of an ion channel.

scholarly journals The structure of a potassium-selective ion channel reveals a hydrophobic gate regulating ion permeation

IUCrJ ◽  
2020 ◽  
Vol 7 (5) ◽  
pp. 835-843
Author(s):  
Patricia S. Langan ◽  
Venu Gopal Vandavasi ◽  
Wojciech Kopec ◽  
Brendan Sullivan ◽  
Pavel V. Afonne ◽  
...  
Keyword(s):  
Ion Channels ◽  
Ion Channel ◽  
Conformational Transition ◽  
Transmembrane Proteins ◽  
Selectivity Filter ◽  
Ion Permeation ◽  
Hydrophobic Residue ◽  
Ion Flow ◽  
Flow Through ◽  
Resolution Structure

Protein dynamics are essential to function. One example of this is the various gating mechanisms within ion channels, which are transmembrane proteins that act as gateways into the cell. Typical ion channels switch between an open and closed state via a conformational transition which is often triggered by an external stimulus, such as ligand binding or pH and voltage differences. The atomic resolution structure of a potassium-selective ion channel named NaK2K has allowed us to observe that a hydrophobic residue at the bottom of the selectivity filter, Phe92, appears in dual conformations. One of the two conformations of Phe92 restricts the diameter of the exit pore around the selectivity filter, limiting ion flow through the channel, while the other conformation of Phe92 provides a larger-diameter exit pore from the selectivity filter. Thus, it can be concluded that Phe92 acts as a hydrophobic gate, regulating the flow of ions through the selectivity filter.

scholarly journals Neural Network Differential Equations For Ion Channel Modelling

2021 ◽  
Vol 12 ◽  
Author(s):  
Chon Lok Lei ◽  
Gary R. Mirams
Keyword(s):  
Neural Network ◽  
Ion Channels ◽  
Ion Channel ◽  
Conformational Changes ◽  
Traditional Approach ◽  
Equation Model ◽  
State Transitions ◽  
Abstract Description ◽  
Potential Applications ◽  
Model Discrepancy

Mathematical models of cardiac ion channels have been widely used to study and predict the behaviour of ion currents. Typically models are built using biophysically-based mechanistic principles such as Hodgkin-Huxley or Markov state transitions. These models provide an abstract description of the underlying conformational changes of the ion channels. However, due to the abstracted conformation states and assumptions for the rates of transition between them, there are differences between the models and reality—termed model discrepancy or misspecification. In this paper, we demonstrate the feasibility of using a mechanistically-inspired neural network differential equation model, a hybrid non-parametric model, to model ion channel kinetics. We apply it to the hERG potassium ion channel as an example, with the aim of providing an alternative modelling approach that could alleviate certain limitations of the traditional approach. We compare and discuss multiple ways of using a neural network to approximate extra hidden states or alternative transition rates. In particular we assess their ability to learn the missing dynamics, and ask whether we can use these models to handle model discrepancy. Finally, we discuss the practicality and limitations of using neural networks and their potential applications.

scholarly journals Briefing in Application of Machine Learning Methods in Ion Channel Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hao Lin ◽  
Wei Chen
Keyword(s):  
Machine Learning ◽  
Ion Channels ◽  
Ion Channel ◽  
Inorganic Ions ◽  
Correct Identification ◽  
Biological Processes ◽  
Learning Methods ◽  
Channel Prediction ◽  
Machine Learning Methods ◽  
Wide Range

In cells, ion channels are one of the most important classes of membrane proteins which allow inorganic ions to move across the membrane. A wide range of biological processes are involved and regulated by the opening and closing of ion channels. Ion channels can be classified into numerous classes and different types of ion channels exhibit different functions. Thus, the correct identification of ion channels and their types using computational methods will provide in-depth insights into their function in various biological processes. In this review, we will briefly introduce and discuss the recent progress in ion channel prediction using machine learning methods.

scholarly journals Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes

2020 ◽  
Vol 21 (12) ◽  
pp. 4367 ◽  
Author(s):  
Jörn Lötsch ◽  
Dario Kringel ◽  
Gerd Geisslinger ◽  
Bruno G. Oertel ◽  
Eduard Resch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Next Generation Sequencing ◽  
Ion Channels ◽  
Ion Channel ◽  
Genetic Variants ◽  
Genetic Information ◽  
Pain Thresholds ◽  
Thermal Pain ◽  
Thermal Stimuli ◽  
Generation Sequencing

Genetic association studies have shown their usefulness in assessing the role of ion channels in human thermal pain perception. We used machine learning to construct a complex phenotype from pain thresholds to thermal stimuli and associate it with the genetic information derived from the next-generation sequencing (NGS) of 15 ion channel genes which are involved in thermal perception, including ASIC1, ASIC2, ASIC3, ASIC4, TRPA1, TRPC1, TRPM2, TRPM3, TRPM4, TRPM5, TRPM8, TRPV1, TRPV2, TRPV3, and TRPV4. Phenotypic information was complete in 82 subjects and NGS genotypes were available in 67 subjects. A network of artificial neurons, implemented as emergent self-organizing maps, discovered two clusters characterized by high or low pain thresholds for heat and cold pain. A total of 1071 variants were discovered in the 15 ion channel genes. After feature selection, 80 genetic variants were retained for an association analysis based on machine learning. The measured performance of machine learning-mediated phenotype assignment based on this genetic information resulted in an area under the receiver operating characteristic curve of 77.2%, justifying a phenotype classification based on the genetic information. A further item categorization finally resulted in 38 genetic variants that contributed most to the phenotype assignment. Most of them (10) belonged to the TRPV3 gene, followed by TRPM3 (6). Therefore, the analysis successfully identified the particular importance of TRPV3 and TRPM3 for an average pain phenotype defined by the sensitivity to moderate thermal stimuli.

Metal bridges to probe membrane ion channel structure and function

2015 ◽  
Vol 6 (3) ◽  
pp. 191-203 ◽  
Author(s):  
Paul Linsdell
Keyword(s):  
Ion Channels ◽  
Ion Channel ◽  
Conformational Changes ◽  
Three Dimensional ◽  
Structure And Function ◽  
Side Chains ◽  
Channel Protein ◽  
Conformational Rearrangements ◽  
And Function ◽  
Different Parts

AbstractIon channels are integral membrane proteins that undergo important conformational changes as they open and close to control transmembrane flux of different ions. The molecular underpinnings of these dynamic conformational rearrangements are difficult to ascertain using current structural methods. Several functional approaches have been used to understand two- and three-dimensional dynamic structures of ion channels, based on the reactivity of the cysteine side-chain. Two-dimensional structural rearrangements, such as changes in the accessibility of different parts of the channel protein to the bulk solution on either side of the membrane, are used to define movements within the permeation pathway, such as those that open and close ion channel gates. Three-dimensional rearrangements – in which two different parts of the channel protein change their proximity during conformational changes – are probed by cross-linking or bridging together two cysteine side-chains. Particularly useful in this regard are so-called metal bridges formed when two or more cysteine side-chains form a high-affinity binding site for metal ions such as Cd2+ or Zn2+. This review describes the use of these different techniques for the study of ion channel dynamic structure and function, including a comprehensive review of the different kinds of conformational rearrangements that have been studied in different channel types via the identification of intra-molecular metal bridges. Factors that influence the affinities and conformational sensitivities of these metal bridges, as well as the kinds of structural inferences that can be drawn from these studies, are also discussed.

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