Molecular Dynamics Simulations of Ion Channels

Preorganized Electric Fields in Voltage-Gated Sodium Channels

Enzymes are reported to catalyze reactions by generating electric fields that promote the evolution of the reaction in the active site. Although seldom used outside enzymatic catalysis, electrostatic preorganization theory and language of electric fields can be generalized to other biological macromolecules. Herein, we performed molecular dynamics simulations of human Nav1.5, Nav1.6, and Nav1.7 with the atomic multipole optmimized energetics for biomolecular applications  polarizable force field. We show that in the absence of an external potential, charged and uncharged residues generate strong electric fields that assist in Na+ motion in the pore. This work emphasizes the importance of charge-dipole interactions in modulating Na+ dynamics, in addition to charge-charge interactions, the focus of a majority of previous studies. Finally, we find that residues share a high level of mutual information through electric fields that can enable the optimization of allosteric pathways.

MDTAP: a tool to analyze permeation events across membrane proteins

Motivation

Molecular dynamics (MD) simulations provide critical insights into the transport of solutes, solvents, and drug molecules across protein channels embedded in a membrane bilayer. However, identifying and analyzing the permeation events from complex simulation data remains as a challenging and laborious task. Thus, an automated tool that facilitates the capture of permeation events of any molecular type across any channel is essential to streamline MD trajectory analysis and enhance the understanding of biological processes in a timely manner.

Results

Molecular Dynamics Trajectory Analysis of Permeation (MDTAP) is a Linux/Mac-based software that automatically detects permeation events across membrane-embedded protein and nucleic acid channels. The tool accepts trajectories in DCD (CHARMM/NAMD) and PDB format (obtained from any MD simulation package) and employs bash scripts to analyze the input trajectories to characterize the molecular permeation. The efficiency of MDTAP is demonstrated using MD trajectories of Escherichia coli outer membrane protein Wzi and E. coli Aquaporin Z. MDTAP can also analyze permeation across heterogeneous lipid membranes and artificial nucleic acid channels, addressing their growing importance. Thus, MDTAP simplifies trajectory analysis and also reduces the need for manual inspection.

Availability and implementation

MDTAP is open-source and is freely available on GitHub (https://github.com/MBL-lab/MDTAP), including source code, installation instructions, and usage documentation.

Building predictive Markov models of ion channel permeation from molecular dynamics simulations

Molecular dynamics (MD) simulation of biological processes has always been a challenging task due to the long timescales of the processes involved and the large amount of output data to handle. Markov state models (MSMs) have been introduced as a powerful tool in this area of research, as they provide a mechanistically comprehensible synthesis of the large amount of MD data and, at the same time, can be used to rapidly estimate experimental properties of biological processes. Herein, we propose a method for building MSMs of ion channel permeation from MD trajectories, which directly evaluates the current flowing through the channel from the model's transition matrix (T), which is crucial for comparing simulations and experimental data. This is achieved by including in the model a flux matrix that summarizes information on the charge moving across the channel between each pair of states of the MSM and can be used in conjunction with T to predict the ion current. A procedure to drastically reduce the number of states in the MSM while preserving the estimated ion current is also proposed. Applying the method to the KcsA channel returned an MSM with five states with significant equilibrium occupancy, capable of accurately reproducing the single-channel ion current from microsecond MD trajectories.

Evaluating sequential and allosteric activation models in IKs channels with mutated voltage sensors

The ion-conducting IKs channel complex, important in cardiac repolarization and arrhythmias, comprises tetramers of KCNQ1 α-subunits along with 1-4 KCNE1 accessory subunits and calmodulin regulatory molecules. The E160R mutation in individual KCNQ1 subunits was used to prevent activation of voltage sensors and allow direct determination of transition rate data from complexes opening with a fixed number of 1, 2, or 4 activatable voltage sensors. Markov models were used to test the suitability of sequential versus allosteric models of IKs activation by comparing simulations with experimental steady-state and transient activation kinetics, voltage-sensor fluorescence from channels with two or four activatable domains, and limiting slope currents at negative potentials. Sequential Hodgkin-Huxley-type models approximately describe IKs currents but cannot explain an activation delay in channels with only one activatable subunit or the hyperpolarizing shift in the conductance-voltage relationship with more activatable voltage sensors. Incorporating two voltage sensor activation steps in sequential models and a concerted step in opening via rates derived from fluorescence measurements improves models but does not resolve fundamental differences with experimental data. Limiting slope current data that show the opening of channels at negative potentials and very low open probability are better simulated using allosteric models of activation with one transition per voltage sensor, which implies that movement of all four sensors is not required for IKs conductance. Tiered allosteric models with two activating transitions per voltage sensor can fully account for IKs current and fluorescence activation kinetics in constructs with different numbers of activatable voltage sensors.

Otoprotection against aminoglycoside- and cisplatin-induced ototoxicity focusing on the upstream drug uptake pathway

Aminoglycoside- and cisplatin-induced ototoxicity, which is a significant issue owing to the widespread use of these drugs in clinical practice, involves the entry of aminoglycosides and cisplatin into the endolymph and hair cells via specific channels or transporters, followed by reactive oxygen species (ROS) generation and hair cells apoptosis. Current strategies focalize primarily on interference with downstream ROS effects; however, recent evidence has demonstrated that inhibiting the uptake of aminoglycosides and cisplatin by hair cells is another promising strategy for tackling the upstream drug uptake pathway. With advances in structural biology, the conformations of certain aminoglycoside and cisplatin channels and transporters, such as the mechanoelectrical transduction channel and organic cation transporter-2, have been largely elucidated. These channels and transporters may become potential targets for the introduction of new otoprotective strategies. This review focuses on the strategies for inhibiting ototoxic drugs uptake by auditory hair cells and provides potential targets for recent developments in the field of otoprotection. Molecular dynamics (MD) simulations of these proteins could help identify the molecules that inhibit the uptake of aminoglycosides and cisplatin by hair cells. Integrating upstream drug uptake pathway targets and MD simulations may help dissect molecular mechanisms and develop novel otoprotective strategies for aminoglycoside- and cisplatin-induced ototoxicity.

Fluoride Transport and Inhibition Across CLC Transporters

The Chloride Channel (CLC) family includes proton-coupled chloride and fluoride transporters. Despite their similar protein architecture, the former exchange two chloride ions for each proton and are inhibited by fluoride, whereas the latter efficiently transport one fluoride in exchange for one proton. The combination of structural, mutagenesis, and functional experiments with molecular simulations has pinpointed several amino acid changes in the permeation pathway that capitalize on the different chemical properties of chloride and fluoride to fine-tune protein function. Here we summarize recent findings on fluoride inhibition and transport in the two prototypical members of the CLC family, the chloride/proton transporter from Escherichia coli (CLC-ec1) and the fluoride/proton transporter from Enterococcus casseliflavus (CLCF-eca).

Boron nitride nanotube-salt-water hybrid:crystalline precipitation

Molecular dynamics simulation is used to study the transport characteristics of NaCl solution in boron nitride nanotubes (BNNTs). It presents an interesting and well-supported MD study of the crystallization of NaCl from its water solution under the confinement of a 3 nm thick boron nitride nanotube with varied surface charging conditions. The results of the molecular dynamics simulation indicate that NaCl crystallization occurs in charged BNNTs at room temperature when the concentration of NaCl solution reaches about 1.2 M. The reason for this phenomenon is as follows: when the number of ions in the nanotubes is high, the double electric layer that forms at the nanoscale near the charged wall surface, the hydrophobicity of BNNTs, and the interaction among ions cause ions to aggregate in the nanotubes. As the concentration of NaCl solution increases, the concentration of ions when they aggregate in the nanotubes reaches the saturation concentration of the NaCl solution, resulting in the crystalline precipitation phenomenon.

Nonparametric Bayesian inference for meta-stable conformational dynamics

Analyses of structural dynamics of biomolecules hold great promise to deepen the understanding of and ability to construct complex molecular systems. To this end, both experimental and computational means are available, such as fluorescence quenching experiments or molecular dynamics simulations, respectively. We argue that while seemingly disparate, both fields of study have to deal with the same type of data about the same underlying phenomenon of conformational switching. Two central challenges typically arise in both contexts: (i) the amount of obtained data is large, and (ii) it is often unknown how many distinct molecular states underlie these data. In this study, we build on the established idea of Markov state modeling and propose a generative, Bayesian nonparametric hidden Markov state model that addresses these challenges. Utilizing hierarchical Dirichlet processes, we treat different meta-stable molecule conformations as distinct Markov states, the number of which we then do not have to set a priori. In contrast to existing approaches to both experimental as well as simulation data that are based on the same idea, we leverage a mean-field variational inference approach, enabling scalable inference on large amounts of data. Furthermore, we specify the model also for the important case of angular data, which however proves to be computationally intractable. Addressing this issue, we propose a computationally tractable approximation to the angular model. We demonstrate the method on synthetic ground truth data and apply it to known benchmark problems as well as electrophysiological experimental data from a conformation-switching ion channel to highlight its practical utility.

Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design

Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.

TUPÃ: Electric field analyses for molecular simulations

We introduce TUPÃ, a Python-based algorithm to calculate and analyze electric fields in molecular simulations. To demonstrate the features in TUPÃ, we present three test cases in which the orientation and magnitude of the electric field exerted by biomolecules help explain biological phenomena or observed kinetics. As part of TUPÃ, we also provide a PyMOL plugin to help researchers visualize how electric fields are organized within the simulation system. The code is freely available and can be obtained at https://mdpoleto.github.io/tupa/.

Unifying Single-Channel Permeability From Rare-Event Sampling and Steady-State Flux

Various all-atom molecular dynamics (MD) simulation methods have been developed to compute free energies and crossing rates of ions and small molecules through ion channels. However, a systemic comparison across different methods is scarce. Using a carbon nanotube as a model of small conductance ion channel, we computed the single-channel permeability for potassium ion using umbrella sampling, Markovian milestoning, and steady-state flux under applied voltage. We show that a slightly modified inhomogeneous solubility-diffusion equation yields a single-channel permeability consistent with the mean first passage time (MFPT) based method. For milestoning, applying cylindrical and spherical bulk boundary conditions yield consistent MFPT if factoring in the effective bulk concentration. The sensitivity of the MFPT to the output frequency of collective variables is highlighted using the convergence and symmetricity of the inward and outward MFPT profiles. The consistent transport kinetic results from all three methods demonstrated the robustness of MD-based methods in computing ion channel permeation. The advantages and disadvantages of each technique are discussed, focusing on the future applications of milestoning in more complex systems.

Druggable Lipid Binding Sites in Pentameric Ligand-Gated Ion Channels and Transient Receptor Potential Channels

Lipids modulate the function of many ion channels, possibly through direct lipid-protein interactions. The recent outpouring of ion channel structures by cryo-EM has revealed many lipid binding sites. Whether these sites mediate lipid modulation of ion channel function is not firmly established in most cases. However, it is intriguing that many of these lipid binding sites are also known sites for other allosteric modulators or drugs, supporting the notion that lipids act as endogenous allosteric modulators through these sites. Here, we review such lipid-drug binding sites, focusing on pentameric ligand-gated ion channels and transient receptor potential channels. Notable examples include sites for phospholipids and sterols that are shared by anesthetics and vanilloids. We discuss some implications of lipid binding at these sites including the possibility that lipids can alter drug potency or that understanding protein-lipid interactions can guide drug design. Structures are only the first step toward understanding the mechanism of lipid modulation at these sites. Looking forward, we identify knowledge gaps in the field and approaches to address them. These include defining the effects of lipids on channel function in reconstituted systems using asymmetric membranes and measuring lipid binding affinities at specific sites using native mass spectrometry, fluorescence binding assays, and computational approaches.

Computational methods and theory for ion channel research

Ion channels are fundamental biological devices that act as gates in order to ensure selective ion transport across cellular membranes; their operation constitutes the molecular mechanism through which basic biological functions, such as nerve signal transmission and muscle contraction, are carried out. Here, we review recent results in the field of computational research on ion channels, covering theoretical advances, state-of-the-art simulation approaches, and frontline modeling techniques. We also report on few selected applications of continuum and atomistic methods to characterize the mechanisms of permeation, selectivity, and gating in biological and model channels.

Photopharmacology of Ion Channels through the Light of the Computational Microscope

The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach.