State-dependent Binding Research Articles

Experimentally validated modeling of dynamic drug-hERG channel interactions reproducing the binding mechanisms and its importance in action potential duration.

Background and ObjectiveAssessment of drug cardiotoxicity is critical in the development of new compounds and modeling of drug-binding dynamics to hERG can improve early cardiotoxicity assessment. We previously developed a methodology to generate Markovian models reproducing preferential state-dependent binding properties, trapping dynamics and the onset of IKr block using simple voltage clamp protocols. Here, we test this methodology with real IKr blockers and investigate the impact of drug dynamics on action potential prolongation. MethodsExperiments were performed on HEK cells stably transfected with hERG and using the Nanion SyncroPatch 384i. Three protocols, P-80, P0 and P 40, were applied to obtain the experimental data from the drugs and the Markovian models were generated using our pipeline. The corresponding static models were also generated and a modified version of the O´Hara-Rudy action potential model was used to simulate the action potential duration. ResultsThe experimental Hill plots and the onset of IKr block of ten compounds were obtained using our voltage clamp protocols and the models generated successfully mimicked these experimental data, unlike the CiPA dynamic models. Marked differences in APD prolongation were observed when drug effects were simulated using the dynamic models and the static models. ConclusionsThese new dynamic models of ten well-known IKr blockers constitute a validation of our methodology to model dynamic drug–hERG channel interactions and highlight the importance of state-dependent binding, trapping dynamics and the time-course of IKr block to assess drug effects even at the steady-state.

State-dependent binding of cholesterol and an anionic lipid to the muscle-type Torpedo nicotinic acetylcholine receptor

The ability of the Torpedo nicotinic acetylcholine receptor (nAChR) to undergo agonist-induced conformational transitions requires the presence of cholesterol and/or anionic lipids. Here we use recently solved structures along with multiscale molecular dynamics simulations to examine lipid binding to the nAChR in bilayers that have defined effects on nAChR function. We examine how phosphatidic acid and cholesterol, lipids that support conformational transitions, individually compete for binding with phosphatidylcholine, a lipid that does not. We also examine how the two lipids work synergistically to stabilize an agonist-responsive nAChR. We identify rapidly exchanging lipid binding sites, including both phospholipid sites with a high affinity for phosphatidic acid and promiscuous cholesterol binding sites in the grooves between adjacent transmembrane α-helices. A high affinity cholesterol site is confirmed in the inner leaflet framed by a key tryptophan residue on the MX α-helix. Our data provide insight into the dynamic nature of lipid-nAChR interactions and set the stage for a detailed understanding of the mechanisms by which lipids facilitate nAChR function at the neuromuscular junction.

The state-dependent binding of polyunsaturated fatty acids and a novel modulation mechanism on ASIC channels

The state-dependent binding of polyunsaturated fatty acids and a novel modulation mechanism on ASIC channels

Discovery of lipid binding sites in a ligand-gated ion channel by integrating simulations and cryo-EM.

Ligand-gated ion channels transduce electrochemical signals in neurons and other excitable cells. Aside from canonical ligands, phospholipids are thought to bind specifically to the transmembrane domain of several ion channels. However, structural details of such lipid contacts remain elusive, partly due to limited resolution of these regions in experimental structures. Here, we discovered multiple lipid interactions in the channel GLIC by integrating cryo-electron microscopy and large-scale molecular simulations. We identified 25 bound lipids in the GLIC closed state, a conformation where none, to our knowledge, were previously known. Three lipids were associated with each subunit in the inner leaflet, including a buried interaction disrupted in mutant simulations. In the outer leaflet, two intrasubunit sites were evident in both closed and open states, while a putative intersubunit site was preferred in open-state simulations. This work offers molecular details of GLIC-lipid contacts particularly in the ill-characterized closed state, testable hypotheses for state-dependent binding, and a multidisciplinary strategy for modeling protein-lipid interactions.

Electrochemical/computational analysis and the derivation of Pourbaix diagrams of redox-active structural and adsorbed species in uranyl peroxide clusters (U60)

Electrochemical analysis of U60 nanoclusters (Li40K20[UO2(O2)(OH)]60(H2O)214) and their natural analog, the mineral studtite (UO2)O2(H2O)2·(H2O)2, was carried out using cyclic voltammetry with a powder microelectrode setup. Voltammetric analysis was supplemented by ab initio quantum mechanical modeling to understand the mechanism, thermodynamics, and structural changes during redox transition (with a semi-quantitative kinetics evaluation). This research aims to better understand the redox behavior and transport of dissolved and structural (incorporated) uranyl units in the environment. Further analysis is performed to determine if redox switching of actinyl ions in clusters can be performed while leaving the cluster intact or at what redox transition it disintegrates. Quantum-mechanical calculations were applied to approximate electrochemical peak potential shifts and add them to standard reduction potentials to more accurately assign peaks to specific redox transitions between different species. These peak potentials are calculated from oxidation state-dependent binding energies between different uranyl species either within or adsorbed to U60. This theoretical approach incorporates extensive error cancellation and serves to predict electrochemical redox potentials and identify the reaction taking place. Voltammograms of U60 in electrolyte solutions exhibit kinetically-inhibited coupled redox peaks assigned to the U(VI)/U(V) transition of cluster structural uranyl units at −0.34 V (vs. standard hydrogen electrode), indicative of U60 reduction from UO22+ to UO2+. The U(V)/U(IV) transition at −0.71 V is assigned to the subsequent U60 reduction of UO2+ to UO20. A method of observing these transitions in situ using synchrotron x-ray absorption spectroscopy was explored. Voltammetry, modeling, and peak analysis indicate a kinetically-inhibited two-step, one-e- (per step) reduction of U60 from U(VI) to U(V) to U(IV). Voltammetry and x-ray absorption spectroscopy measurements performed before and after exhaustive cycling and the highly stable nature of U60 provide evidence that clusters typically do not break apart upon reduction. Voltammograms of U60 collected in varied uranyl solutions are indicative of UO22+ adsorption to the cluster surface and its subsequent reduction at +0.24 V (U(VI)/U(V)) and −0.05 V (U(V)/U(IV)). For uranyl solutions in electrolyte, the two-step, one-e- (per step) reduction of U60 is superseded by uranyl adsorption from solution to the electrode surface with subsequent reduction via U(V) disproportionation. This study represents the sole electrochemical investigation of U60 in the literature and only the second of any uranyl peroxide nanocluster. It is also one of the first to use quantum–mechanical approaches in combination with existing solution-based standard reduction potentials to calculate Pourbaix (EH-pH) diagrams for adsorbed and structural species.

Elucidating molecular mechanisms of protoxin-II state-specific binding to the human NaV1.7 channel.

Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells, and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-II (PTx2) has high selectivity for hNaV1.7 and is a valuable scaffold for designing novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2's residues R22, K26, K27, K28, and W30 with VSD and the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.

Cannabidiol inhibits Nav channels through two distinct binding sites

Cannabidiol (CBD), a major non-psychoactive phytocannabinoid in cannabis, is an effective treatment for some forms of epilepsy and pain. At high concentrations, CBD interacts with a huge variety of proteins, but which targets are most relevant for clinical actions is still unclear. Here we show that CBD interacts with Nav1.7 channels at sub-micromolar concentrations in a state-dependent manner. Electrophysiological experiments show that CBD binds to the inactivated state of Nav1.7 channels with a dissociation constant of about 50 nM. The cryo-EM structure of CBD bound to Nav1.7 channels reveals two distinct binding sites. One is in the IV-I fenestration near the upper pore. The other binding site is directly next to the inactivated “wedged” position of the Ile/Phe/Met (IFM) motif on the short linker between repeats III and IV, which mediates fast inactivation. Consistent with producing a direct stabilization of the inactivated state, mutating residues in this binding site greatly reduced state-dependent binding of CBD. The identification of this binding site may enable design of compounds with improved properties compared to CBD itself.

Importance of modelling hERG binding in predicting drug-induced action potential prolongations for drug safety assessment.

Reduction of the rapid delayed rectifier potassium current (I Kr) via drug binding to the human Ether-à-go-go-Related Gene (hERG) channel is a well recognised mechanism that can contribute to an increased risk of Torsades de Pointes. Mathematical models have been created to replicate the effects of channel blockers, such as reducing the ionic conductance of the channel. Here, we study the impact of including state-dependent drug binding in a mathematical model of hERG when translating hERG inhibition to action potential changes. We show that the difference in action potential predictions when modelling drug binding of hERG using a state-dependent model versus a conductance scaling model depends not only on the properties of the drug and whether the experiment achieves steady state, but also on the experimental protocols. Furthermore, through exploring the model parameter space, we demonstrate that the state-dependent model and the conductance scaling model generally predict different action potential prolongations and are not interchangeable, while at high binding and unbinding rates, the conductance scaling model tends to predict shorter action potential prolongations. Finally, we observe that the difference in simulated action potentials between the models is determined by the binding and unbinding rate, rather than the trapping mechanism. This study demonstrates the importance of modelling drug binding and highlights the need for improved understanding of drug trapping which can have implications for the uses in drug safety assessment.

Discovery of lipid binding sites in the ligand-gated ion channel GLIC through simulations and Cryo-EM.

Ligand-gated ion channels play an important role in electrochemical signal transduction in neurons and other excitable cells. Aside from canonical ligands, phospholipids have been shown to bind specifically and sometimes modulate several types of channels, but the structural details of such protein-lipid interactions remain unclear. Here, we present multiple lipid interaction sites in the pentameric ligand-gated ion channel, GLIC, by integrating cryo-electron microscopy and large-scale atomistic molecular dynamics simulations together with Markov state modeling. We identified 25 lipids in the closed state of GLIC - a conformation in which, to our knowledge, lipids have not been previously resolved. Both simulations and cryo-EM data predicted, for each subunit, three lipids in the inner leaflet and two in the outer leaflet. Further, we used simulations to make predictions about lipid interactions in the open state, resulting in six sites - three in each leaflet. Interactions of particular interest include one slow-exchanging interaction with a deeply buried lipid tail in the intersubunit space, where tail saturation levels played a role in the binding pose, and where the interaction could be broken due to a single mutation. An additional lipid interaction was preferential to the open state, potentially playing a role in stabilization of this state. This work provides molecular details of GLIC-lipid contacts, especially in the understudied closed state; a testable hypotheses for state-dependent binding; and a multidisciplinary strategy for modeling protein-lipid interactions.

Open-channel structure of a pentameric ligand-gated ion channel reveals a mechanism of leaflet-specific phospholipid modulation

Pentameric ligand-gated ion channels (pLGICs) mediate synaptic transmission and are sensitive to their lipid environment. The mechanism of phospholipid modulation of any pLGIC is not well understood. We demonstrate that the model pLGIC, ELIC (Erwinia ligand-gated ion channel), is positively modulated by the anionic phospholipid, phosphatidylglycerol, from the outer leaflet of the membrane. To explore the mechanism of phosphatidylglycerol modulation, we determine a structure of ELIC in an open-channel conformation. The structure shows a bound phospholipid in an outer leaflet site, and structural changes in the phospholipid binding site unique to the open-channel. In combination with streamlined alchemical free energy perturbation calculations and functional measurements in asymmetric liposomes, the data support a mechanism by which an anionic phospholipid stabilizes the activated, open-channel state of a pLGIC by specific, state-dependent binding to this site.

Abstract 15504: High-Throughput Assessment of Molecular Mechanisms of Drug-Induced Arrhythmia Proclivities and Their Sex Dependence

Introduction: The hERG K + channel is responsible for the repolarizing cardiac current I Kr . Drug block of hERG can prolong the QT interval on the surface electrocardiogram and in some cases promote deadly arrhythmias. Previous studies suggested that drugs that preferentially bind to the hERG channel’s inactivated state have higher proarrhythmic proclivities. Electrophysiology experiments and multiscale simulations also predicted that sex hormones can suppress hERG current and exacerbate drug-induced cardiotoxicity. Hypothesis: Atomistic simulations can make reliable correlations between a drug’s chemical structure and its state-dependent hERG binding affinities allowing us to distinguish safe from dangerous hERG blockers. Methods: We utilized site identification by ligand competitive saturation (SILCS), a pre-compute ensemble molecular docking technique, to predict hERG-channel binding poses and affinities of drug molecules. To improve agreement of SILCS results with experimental and molecular dynamics simulation data, we used a Bayesian machine learning protocol to optimize docking parameters. Results: We analyzed state-dependent binding of a sample set of 55 known hERG blockers and seven sex hormones to available structural models of the hERG channel. SILCS computed dissociation constants, K D , show good correlation with experimental IC 50 values for our open-state model (Panel A) and predict drug and hormone binding sites in agreement with previous studies (Panel B) validating the methodology. Conclusions: Our computational methodology allows us to do high-throughput screening and differentiation of open vs. inactivated-state hERG channel drug binding and can also predict how hormones may modulate a drug’s hERG blocking activity. This provides more selective criteria for differentiating cardiac safe and dangerous drugs compared to using only hERG block and QT prolongation as surrogate markers of pro-arrhythmia risk.

Automatic modeling of dynamic drug-hERG channel interactions using three voltage protocols and machine learning techniques: A simulation study

BackgroundAssessment of drug cardiac safety is critical in the development of new compounds and is commonly addressed by evaluating the half-maximal blocking concentration of the potassium human ether-à-go-go related gene (hERG) channels. However, recent works have evidenced that the modelling of drug-binding dynamics to hERG can help to improve early cardiac safety assessment. Our goal is to develop a methodology to automatically generate Markovian models of the drug-hERG channel interactions. MethodsThe training and the test sets consisted of 20800 and 5200 virtual drugs, respectively, distributed into 104 groups with different affinities and kinetics to the conformational states of the channel. In our system, drugs may bind to any state (individually or simultaneously), with different degrees of preference for a conformational state and the change of the conformational state of the drug bound channels may be restricted or allowed. To model such a wide range of possibilities, 12 Markovian chains are considered. Our approach uses the response of the drugs to our three previously developed voltage clamp protocols, which enhance the differences in the probabilities of occupying a certain conformational state of the channel (open, closed and inactivated). The computing tool is comprised of a classifier and a parameter optimizer and uses linear interpolation, support vector machines and a simplex method for function minimization. ResultsWe propose a novel methodology that automatically generates dynamic drug models using Markov model formulations and that elucidates the states where the drug binds and unbinds and the preferential binding state using data obtained from simple voltage clamp protocols that captures the preferential state-dependent binding properties, the relative affinities, trapping and non-trapping dynamics and the onset of IKr block. Overall, the tool correctly predicted the class of 92.04% of the drugs and the model provided by the tool accurately fitted the response of the target compound, the mean accuracy being 97.53%. Moreover, generation of the dynamic model of an IKr blocker from its response to our voltage clamp protocols usually takes less than an hour on a common desktop computer. ConclusionOur methodology could be very useful to model and simulate dynamic drug–hERG channel interactions. It would contribute to the improvement of the preclinical assessment of the proarrhythmic risk of drugs that inhibit IKr and the efficacy of antiarrhythmic IKr blockers.

Cholesterol Dependent Activity of the Adenosine A2A Receptor Is Modulated via the Cholesterol Consensus Motif.

Background: Membrane cholesterol dysregulation has been shown to alter the activity of the adenosine A2A receptor (A2AR), a G protein-coupled receptor, thereby implicating cholesterol levels in diseases such as Alzheimer’s and Parkinson’s. A limited number of A2AR crystal structures show the receptor interacting with cholesterol, as such molecular simulations are often used to predict cholesterol interaction sites. Methods: Here, we use experimental methods to determine whether a specific interaction between amino acid side chains in the cholesterol consensus motif (CCM) of full length, wild-type human A2AR, and cholesterol modulates activity of the receptor by testing the effects of mutational changes on functional consequences, including ligand binding, G protein coupling, and downstream activation of cyclic AMP. Results and conclusions: Our data, taken with previously published studies, support a model of receptor state-dependent binding between cholesterol and the CCM, whereby cholesterol facilitates both G protein coupling and downstream signaling of A2AR.

Transition between conformational states of the TREK-1 K2P channel promoted by interaction with PIP2

Members of the TREK family of two-pore domain potassium channels are highly sensitive to regulation by membrane lipids, including phosphatidylinositol-4,5-bisphosphate (PIP2). Previous studies have demonstrated that PIP2 increases TREK-1 channel activity; however, the mechanistic understanding of the conformational transitions induced by PIP2 remain unclear. Here, we used coarse-grained molecular dynamics and atomistic molecular dynamics simulations to model the PIP2-binding site on both the up and down state conformations of TREK-1. We also calculated the free energy of PIP2 binding relative to other anionic phospholipids in both conformational states using potential of mean force and free-energy-perturbation calculations. Our results identify state-dependent binding of PIP2 to sites involving the proximal C-terminus, and we show that PIP2 promotes a conformational transition from a down state toward an intermediate that resembles the up state. These results are consistent with functional data for PIP2 regulation, and together provide evidence for a structural mechanism of TREK-1 channel activation by phosphoinositides.

Tuning CO2 Binding in Metal–Organic Framework Materials by Varying Metal Identity and Oxidation State

CO2 adsorption from distributed and point sources has attracted significant interest in the recent past, and advanced sorbent design remains a fruitful avenue of research, yet structure–property relationships that capture the effect of an adsorption site coordination environment on binding characteristics remain (for the most part) elusive. Metal–organic framework (MOF) materials carry adsorption sites in highly well-defined coordination environments and present an opportunity for relating specific features of an open-metal coordination environment (such as oxidation state) with sorbate binding characteristics. In this work, we investigate CO2 adsorption onto isostructural Al-, Fe-, and Cr-containing MIL-100(M) materials to clearly demonstrate the oxidation state-dependent binding of CO2 to some metals but not others. Specifically, Cr2+ sites on MIL-100 nodes─found challenging to access in the prior literature─are demonstrated to exhibit remarkably stronger binding to CO2 compared to their Cr3+ counterparts. Results presented provide a template for tuning CO2 adsorption properties by systematically varying metal identity and oxidation state in MOFs specifically and CO2 capture sorbents more generally.

Multi-Scale modeling of state-dependent drug binding to the hERG channel

The hERG channel plays an important role in the repolarization phase of the cardiac action potential. Blockage of the channel conductance can lead to prolongation of the cardiac action potential and the QT interval on the surface electrocardiogram, which may result in deadly arrhythmias. hERG channel blockade is a leading cause for drug withdrawal from the drug development pipeline or marketplace. However, not all hERG-blocking and QT-prolonging drugs cause arrhythmia. Therefore, understanding the molecular mechanisms of drug interactions with the hERG channel and the subsequent modulation of the channel gating leading to different pro-arrhythmia outcomes is crucial for drug design and development.

State-dependent binding of DHA uncovers a mechanism for desensitization of pLGICs

Pentameric ligand-gated ion channels (pLGICs) are essential to ultra-fast synaptic transmission. Dysfunction of these channels is associated with a host of debilitating neurological disorders, including congenital myasthenias and Alzheimer’s dementia. Initial studies on pLGICs noted membrane lipid composition to be integral to proper channel function, with certain lipid species causing complete inactivity. Free fatty acid (FFA) lipid species are important to proper neuronal development and FFA levels in synaptic membranes are altered in pathologic states, such as stroke.

Polyunsaturated fatty acids inhibit a pentameric ligand-gated ion channel through one of two binding sites.

Polyunsaturated fatty acids (PUFAs) inhibit pentameric ligand-gated ion channels (pLGICs) but the mechanism of inhibition is not well understood. The PUFA, docosahexaenoic acid (DHA), inhibits agonist responses of the pLGIC, ELIC, more effectively than palmitic acid, similar to the effects observed in the GABAA receptor and nicotinic acetylcholine receptor. Using photo-affinity labeling and coarse-grained molecular dynamics simulations, we identified two fatty acid binding sites in the outer transmembrane domain (TMD) of ELIC. Fatty acid binding to the photolabeled sites is selective for DHA over palmitic acid, and specific for an agonist-bound state. Hexadecyl-methanethiosulfonate modification of one of the two fatty acid binding sites in the outer TMD recapitulates the inhibitory effect of PUFAs in ELIC. The results demonstrate that DHA selectively binds to multiple sites in the outer TMD of ELIC, but that state-dependent binding to a single intrasubunit site mediates DHA inhibition of ELIC.

An open state of a voltage-gated sodium channel involving a π-helix and conserved pore-facing asparagine

Voltage-gated sodium (Nav) channels play critical roles in propagating action potentials and otherwise manipulating ionic gradients in excitable cells. These channels open in response to membrane depolarization, selectively permeating sodium ions until rapidly inactivating. Structural characterization of the gating cycle in this channel family has proved challenging, particularly due to the transient nature of the open state. A structure from the bacterium Magnetococcus marinus Nav (NavMs) was initially proposed to be open, based on its pore diameter and voltage-sensor conformation. However, the functional annotation of this model, and the structural details of the open state, remain disputed. In this work, we used molecular modeling and simulations to test possible open-state models of NavMs. The full-length experimental structure, termed here the α-model, was consistently dehydrated at the activation gate, indicating an inability to conduct ions. Based on a spontaneous transition observed in extended simulations, and sequence/structure comparison to other Nav channels, we built an alternative π-model featuring a helix transition and the rotation of a conserved asparagine residue into the activation gate. Pore hydration, ion permeation, and state-dependent drug binding in this model were consistent with an open functional state. This work thus offers both a functional annotation of the full-length NavMs structure and a detailed model for a stable Nav open state, with potential conservation in diverse ion-channel families.

Predicting lipid and ligand binding sites in TRPV1 channel by molecular dynamics simulation and machine learning.

As a key cellular sensor, the TRPV1 channel undergoes a gating transition from a closed state to an open state in response to many physical and chemical stimuli. This transition is regulated by small-molecule ligands including lipids and various agonists/antagonists, but the underlying molecular mechanisms remain obscure. Thanks to recent revolution in cryo-electron microscopy, a growing list of new structures of TRPV1 and other TRPV channels have been solved in complex with various ligands including lipids. Toward elucidating how ligand binding correlates with TRPV1 gating, we have performed extensive molecular dynamics simulations (with cumulative time of 20 μs), starting from high-resolution structures of TRPV1 in both the closed and open states. By comparing between the open and closed state ensembles, we have identified state-dependent binding sites for small-molecule ligands in general and lipids in particular. We further use machine learning to predict top ligand-binding sites as important features to classify the closed vs open states. The predicted binding sites are thoroughly validated by matching homologous sites in all structures of TRPV channels bound to lipids and other ligands, and with previous functional/mutational studies of ligand binding in TRPV1. Taken together, this study has integrated rich structural, dynamic, and functional data to inform future design of small-molecular drugs targeting TRPV1.