Modeling of open, closed, and open-inactivated states of the hERG1 channel: structural mechanisms of the state-dependent drug binding

Structural modeling of hERG channel-drug interactions using Rosetta

The human ether-a-go-go-related gene (hERG) not only encodes a potassium-selective voltage-gated ion channel essential for normal electrical activity in the heart but is also a major drug anti-target. Genetic hERG mutations and blockage of the channel pore by drugs can cause long QT syndrome, which predisposes individuals to potentially deadly arrhythmias. However, not all hERG-blocking drugs are proarrhythmic, and their differential affinities to discrete channel conformational states have been suggested to contribute to arrhythmogenicity. We used Rosetta electron density refinement and homology modeling to build structural models of open-state hERG channel wild-type and mutant variants (Y652A, F656A, and Y652A/F656 A) and a closed-state wild-type channel based on cryo-electron microscopy structures of hERG and EAG1 channels. These models were used as protein targets for molecular docking of charged and neutral forms of amiodarone, nifekalant, dofetilide, d/l-sotalol, flecainide, and moxifloxacin. We selected these drugs based on their different arrhythmogenic potentials and abilities to facilitate hERG current. Our docking studies and clustering provided atomistic structural insights into state-dependent drug-channel interactions that play a key role in differentiating safe and harmful hERG blockers and can explain hERG channel facilitation through drug interactions with its open-state hydrophobic pockets.

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.

hERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction

Pharmacological blockade of the IKr channel (hERG) by diverse drugs in clinical use is associated with the Long QT Syndrome that can lead to life threatening arrhythmia. Various computational tools including machine learning models (MLM) for the prediction of hERG inhibition have been developed to facilitate the throughput screening of drugs in development and optimise thus the prediction of hERG liabilities. The use of MLM relies on large libraries of training compounds for the quantitative structure-activity relationship (QSAR) modelling of hERG inhibition. The focus on inhibition omits potential effects of hERG channel agonist molecules and their associated QT shortening risk. It is instructive, therefore, to consider how known hERG agonists are handled by MLM. Here, two highly developed online computational tools for the prediction of hERG liability, Pred-hERG and HergSPred were probed for their ability to detect hERG activator drug molecules as hERG interactors. In total, 73 hERG blockers were tested with both computational tools giving overall good predictions for hERG blockers with reported IC50s below Pred-hERG and HergSPred cut-off threshold for hERG inhibition. However, for compounds with reported IC50s above this threshold such as disopyramide or sotalol discrepancies were observed. HergSPred identified all 20 hERG agonists selected as interacting with the hERG channel. Further studies are warranted to improve online MLM prediction of hERG related cardiotoxicity, by explicitly taking into account channel agonism as well as inhibition.

Structural analysis of hERG channel blockers and the implications for drug design

The repolarizing current (I_kr) produced by the hERG potassium channel forms a major component of the cardiac action potential and blocking this current by small molecule drugs can lead to life-threatening cardiotoxicity. Understanding the mechanisms of drug-mediated hERG inhibition is essential to develop a second generation of safe drugs, with minimal cardiotoxic effects. Although various computational tools and drug design guidelines have been developed to avoid binding of drugs to the hERG pore domain, there are many other aspects that are still open for investigation. This includes the use computational modelling to study the implications of hERG mutations on hERG structure and trafficking, the interactions of hERG with hERG chaperone proteins and with membrane-soluble molecules, the mechanisms of drugs that inhibit hERG trafficking and drugs that rescue hERG mutations. The plethora of available experimental data regarding all these aspects can guide the construction of much needed robust computational structural models to study these mechanisms for the rational design of safe drugs.

hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties

Human ether-a-go-go-related gene (hERG) potassium channel is well-known contributor to drug-induced cardiotoxicity and therefore an extremely important target when performing safety assessments of drug candidates. Ligand-based approaches in connection with quantitative structure active relationships (QSAR) analyses have been developed to predict hERG toxicity. Availability of the recent published cryogenic electron microscopy (cryo-EM) structure for the hERG channel opened the prospect for using structure-based simulation and docking approaches for hERG drug liability predictions. In recent time, the idea of combining structure- and ligand-based approaches for modeling hERG drug liability has gained momentum offering improvements in predictability when compared to ligand-based QSAR practices alone. The present article demonstrates uniting the structure-based SILCS (site-identification by ligand competitive saturation) approach in conjunction with physicochemical properties to develop predictive models for hERG blockade. This combination leads to improved model predictability based on Pearson's R and percent correct (represents rank-ordering of ligands) metric for different validation sets of hERG blockers involving diverse chemical scaffold and wide range of pIC50 values. The inclusion of the SILCS structure-based approach allows determination of the hERG region to which compounds bind and the contribution of different chemical moieties in the compounds to blockade, thereby facilitating the rational ligand design to minimize hERG liability.

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.

Genetic and Molecular Aspects of Drug-Induced QT Interval Prolongation

Long QT syndromes can be either acquired or congenital. Drugs are one of the many etiologies that may induce acquired long QT syndrome. In fact, many drugs frequently used in the clinical setting are a known risk factor for a prolonged QT interval, thus increasing the chances of developing torsade de pointes. The molecular mechanisms involved in the prolongation of the QT interval are common to most medications. However, there is considerable inter-individual variability in drug response, thus making the application of personalized medicine a relevant aspect in long QT syndrome, in order to evaluate the risk of every individual from a pharmacogenetic standpoint.

Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations

Drug-induced cardiotoxicity is a potentially lethal and yet one of the most common side effects with the drugs in clinical use. Most of the drug-induced cardiotoxicity is associated with an off-target pharmacological blockade of K+ currents carried out by the cardiac Human-Ether-a-go-go-Related (hERG1) potassium channel. There is a compulsory preclinical stage safety assessment for the hERG1 blockade for all classes of drugs, which adds substantially to the cost of drug development. The availability of a high-resolution cryogenic electron microscopy (cryo-EM) structure for the channel in its open/depolarized state solved in 2017 enabled the application of molecular modeling for rapid assessment of drug blockade by molecular docking and simulation techniques. More importantly, if successful, in silico methods may allow a path to lead-compound salvaging by mapping out key block determinants. Here, we report the blind application of the site identification by the ligand competitive saturation (SILCS) protocol to map out druggable/regulatory hotspots in the hERG1 channel available for blockers and activators. The SILCS simulations use small solutes representative of common functional groups to sample the chemical space for the entire protein and its environment using all-atom simulations. The resulting chemical maps, FragMaps, explicitly account for receptor flexibility, protein-fragment interactions, and fragment desolvation penalty allowing for rapid ranking of potential ligands as blockers or nonblockers of hERG1. To illustrate the power of the approach, SILCS was applied to a test set of 55 blockers with diverse chemical scaffolds and pIC50 values measured under uniform conditions. The original SILCS model was based on the all-atom modeling of the hERG1 channel in an explicit lipid bilayer and was further augmented with a Bayesian-optimization/machine-learning (BML) stage employing an independent literature-derived training set of 163 molecules. BML approach was used to determine weighting factors for the FragMaps contributions to the scoring function. pIC50 predictions from the combined SILCS/BML approach to the 55 blockers showed a Pearson correlation (PC) coefficient of >0.535 relative to the experimental data. SILCS/BML model was shown to yield substantially improved performance as compared to commonly used rigid and flexible molecular docking methods for a well-established cohort of hERG1 blockers, where no correlation with experimental data was recorded. SILCS/BML results also suggest that a proper weighting of protonation states of common blockers present at physiological pH is essential for accurate predictions of blocker potency. The precalculated and optimized SILCS FragMaps can now be used for the rapid screening of small molecules for their cardiotoxic potential as well as for exploring alternative binding pockets in the hERG1 channel with applications to the rational design of activators.

A structure-based computational workflow to predict liability and binding modes of small molecules to hERG

Off-target interactions of drugs with the human ether-à-go-go related gene 1 (hERG1) channel have been associated with severe cardiotoxic conditions leading to the withdrawal of many drugs from the market over the last decades. Consequently, predicting drug-induced hERG-liability is now a prerequisite in any drug discovery campaign. Understanding the atomic level interactions of drug with the channel is essential to guide the efficient development of safe drugs. Here we utilize the recent cryo-EM structure of the hERG channel and describe an integrated computational workflow to characterize different drug-hERG interactions. The workflow employs various structure-based approaches and provides qualitative and quantitative insights into drug binding to hERG. Our protocol accurately differentiated the strong blockers from weak and revealed three potential anchoring sites in hERG. Drugs engaging in all these sites tend to have high affinity towards hERG. Our results were cross-validated using a fluorescence polarization kit binding assay and with electrophysiology measurements on the wild-type (WT-hERG) and on the two hERG mutants (Y652A-hERG and F656A-hERG), using the patch clamp technique on HEK293 cells. Finally, our analyses show that drugs binding to hERG disrupt and hijack certain native—structural networks in the channel, thereby, gaining more affinity towards hERG.

Elucidation of interaction mechanism of hERG1 potassium channel with scorpion toxins BeKm-1 and BmTx3b

The human ether-a-go-go related gene 1 (hERG1) K⁺ channels play crucial role in the heart, different regions of brain, endocrine cells, smooth muscle cells, and numerous tumor cells. It is known that the inherited mutations of hERG1 gene may lead to the disorder of cardiac repolarization (i.e., long QT syndrome (LQTS)), which may result in sudden cardiac death. It is known that K⁺ ion channels involved in signaling pathways lead to cell proliferation or apoptosis and some specific toxins were investigated for diverse therapeutic applications on targeting the hERG1 K⁺ channel. Thus, investigation of channel/toxin interactions mechanisms in atomic level is an important topic for the development of toxin-based therapeutics. Thus, in this work, the interaction mechanisms of two toxins named as BeKm-1 and BmTx3b with the closed-state hERG1 channel have been studied by using different molecular modeling techniques including protein-protein docking and molecular dynamics (MD) simulations. The crucial residues of toxins in channel interactions have been elucidated. It is found that R1, K6, K18, R20, K23 and R27 residues in BeKm-1 and F1, K7, K19, K20 and K28 in BmTx3b are the important residues involved in the strong interactions with the closed-state hERG1 K⁺ channel. The results of this study can be used by medicinal chemists in the designing of diverse therapeutic applications of natural or synthetic peptides targeting the closed state hERG1 K⁺ channels.

Molecular Docking Guided Grid-Independent Descriptor Analysis to Probe the Impact of Water Molecules on Conformational Changes of hERG Inhibitors in Drug Trapping Phenomenon

Human ether a-go-go related gene (hERG) or KV11.1 potassium channels mediate the rapid delayed rectifier current (I_Kr) in cardiac myocytes. Drug-induced inhibition of hERG channels has been implicated in the development of acquired long QT syndrome type (aLQTS) and fatal arrhythmias. Several marketed drugs have been withdrawn for this reason. Therefore, there is considerable interest in developing better tests for predicting drugs which can block the hERG channel. The drug-binding pocket in hERG channels, which lies below the selectivity filter, normally contains K⁺ ions and water molecules. In this study, we test the hypothesis that these water molecules impact drug binding to hERG. We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. The ligand–protein interaction fingerprints (PLIF) scheme was used to summarize and compare the interactions. All models delineated similar 3D hERG binding features, however, small deviations of about ~0.4 Å were observed between important hotspots of molecular interaction fields (MIFs) between solvated and non-solvated hERG models. These small changes in conformations do not affect the performance and predictive power of the model to any significant extent. The model that exhibits the best statistical values was attained with a cryo_EM structure of the hERG channel in open state without water. This model also showed the best R² of 0.58 and 0.51 for the internal and external validation test sets respectively. Our results suggest that the inclusion of water molecules during the docking process has little effect on conformations and this conformational change does not impact the predictive ability of the 3D QSAR models.

Drug-likeness of linear pentamidine analogues and their impact on the hERG K+channel – correlation with structural features

The pentamidines with S atoms or sulfanilide groups in the linker have favorable drug-likeness parameters and low toxicity.

Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers

Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.

Challenges and advances in atomistic simulations of potassium and sodium ion channel gating and permeation

Ion channels are implicated in many essential physiological events such as electrical signal propagation and cellular communication. The advent of K⁺ and Na⁺ ion channel structure determination has facilitated numerous investigations of molecular determinants of their behaviour. At the same time, rapid development of computer hardware and molecular simulation methodologies has made computational studies of large biological molecules in all-atom representation tractable. The concurrent evolution of experimental structural biology with biomolecular computer modelling has yielded mechanistic details of fundamental processes unavailable through experiments alone, such as ion conduction and ion channel gating. This review is a short survey of the atomistic computational investigations of K⁺ and Na⁺ ion channels, focusing on KcsA and several voltage-gated channels from the K_V and Na_V families, which have garnered many successes and engendered several long-standing controversies regarding the nature of their structure-function relationship. We review the latest advancements and challenges facing the field of molecular modelling and simulation regarding the structural and energetic determinants of ion channel function and their agreement with experimental observations.

Integration of multi-scale molecular modeling approaches with experiments for the in silico guided design and discovery of novel hERG-Neutral antihypertensive oxazalone and imidazolone derivatives and analysis of their potential restrictive effects on cell proliferation

AT1 antagonists is the most recent drug class of molecules against hypertension and they mediate their actions through blocking detrimental effects of angiotensin II (A-II) when acts on type I (AT1) A-II receptor. The effects of AT1 antagonists are not limited to cardiovascular diseases. AT1 receptor blockers may be used as potential anti-cancer agents - due to the inhibition of cell proliferation stimulated by A-II. Therefore, AT1 receptors and the A-II biosynthesis mechanisms are targets for the development of new synthetic drugs and therapeutic treatment of various cardiovascular and other diseases. In this work, multi-scale molecular modeling approaches were performed and it is found that oxazolone and imidazolone derivatives reveal similar/better interaction energy profiles compared to the FDA approved sartan molecules at the binding site of the AT1 receptor. In silico-guided designed hit molecules were then synthesized and tested for their binding affinities to human AT1 receptor in radioligand binding studies, using [¹²⁵I-Sar¹-Ile⁸] AngII. Among the compounds tested, 19d and 9j molecules bound to receptor in a dose response manner and with relatively high affinities. Next, cytotoxicity and wound healing assays were performed for these hit molecules. Since hit molecule 19d led to deceleration of cell motility in all three cell lines (NIH3T3, A549, and H358) tested in this study, this molecule is investigated in further tests. In two cell lines (HUVEC and MCF-7) tested, 19d induced G2/M cell cycle arrest in a concentration dependent manner. Adherent cells detached from the plates and underwent cell death possibly due to apoptosis at 19d concentrations that induced cell cycle arrest.

In Vitro and In Silico Risk Assessment in Acquired Long QT Syndrome: The Devil Is in the Details

Acquired long QT syndrome, mostly as a result of drug block of the Kv11. 1 potassium channel in the heart, is characterized by delayed cardiac myocyte repolarization, prolongation of the T interval on the ECG, syncope and sudden cardiac death due to the polymorphic ventricular arrhythmia Torsade de Pointes (TdP). In recent years, efforts are underway through the Comprehensive in vitro proarrhythmic assay (CiPA) initiative, to develop better tests for this drug induced arrhythmia based in part on in silico simulations of pharmacological disruption of repolarization. However, drug binding to Kv11.1 is more complex than a simple binary molecular reaction, meaning simple steady state measures of potency are poor surrogates for risk. As a result, there is a plethora of mechanistic detail describing the drug/Kv11.1 interaction—such as drug binding kinetics, state preference, temperature dependence and trapping—that needs to be considered when developing in silico models for risk prediction. In addition to this, other factors, such as multichannel pharmacological profile and the nature of the ventricular cell models used in simulations also need to be considered in the search for the optimum in silico approach. Here we consider how much of mechanistic detail needs to be included for in silico models to accurately predict risk and further, how much of this detail can be retrieved from protocols that are practical to implement in high throughout screens as part of next generation of preclinical in silico drug screening approaches?

Structure-based design of hERG-neutral antihypertensive oxazalone and imidazolone derivatives

Angiotensin II receptor type 1 (AT1) antagonists are the most recent drug class against hypertension. Recently first crystal structure of AT1 receptor is deposited to the protein data bank (PDB ID: 4YAY). In this work, several molecular screening methods such as molecular docking and de novo design studies were performed and it is found that oxazolone and imidazolone derivatives reveal similar/better interaction energy profiles compared to the FDA approved sartan molecules at the binding site of the AT1 receptor. A database consisting of 3500-fragments were used to enumerate de novo designed imidazolone and oxazolone derivatives and hereby more than 50000 novel small molecules were generated. These derivatives were then used in high throughput virtual screening simulations (Glide/HTVS) to find potent hit molecules. In addition, virtual screening of around 18 million small drug-like compounds from ZINC database were screened at the binding pocket of the AT1 receptor via Glide/HTVS method. Filtered structures were then used in more sophisticated molecular docking simulations protocols (i.e., Glide/SP; Glide/XP; Glide/IFD; Glide/QPLD, and GOLD). However, the K⁺ ion channel/drug interactions should also be considered in studies implemented in molecular level against their cardiovascular risks. Thus, selected compounds with high docking scores via all diverse docking algorithms are also screened at the pore domain regions of human ether-a-go-go-related gene (hERG1) K⁺ channel to remove the high affinity hERG1 blocking compounds. High docking scored compounds at the AT1 with low hERG1 affinity is considered for long molecular dynamics (MD) simulations. Post-processing analysis of MD simulations assisted for better understanding of molecular mechanism of studied compounds at the binding cavity of AT1 receptor. Results of this study can be useful for designing of novel and safe AT1 inhibitors.

Towards a Structural View of Drug Binding to hERG K+ Channels

The human ether-a-go-go-related gene (hERG) K⁺ channel is of great medical and pharmaceutical relevance. Inherited mutations in hERG result in congenital long-QT syndrome which is associated with a markedly increased risk of cardiac arrhythmia and sudden death. hERG K⁺ channels are also remarkably susceptible to block by a wide range of drugs, which in turn can cause drug-induced long-QT syndrome and an increased risk of sudden death. The recent determination of the near-atomic resolution structure of the hERG K⁺ channel, using single-particle cryo-electron microscopy (cryo-EM), provides tremendous insights into how these channels work. It also suggests a way forward in our quest to understand why these channels are so promiscuous with respect to drug binding.

A multiscale computational modelling approach predicts mechanisms of female sex risk in the setting of arousal-induced arrhythmias

KEY POINTS

This study represents a first step toward predicting mechanisms of sex-based arrhythmias that may lead to important developments in risk stratification and may inform future drug design and screening. We undertook simulations to reveal the conditions (i.e. pacing, drugs, sympathetic stimulation) required for triggering and sustaining reentrant arrhythmias. Using the recently solved cryo-EM structure for the Eag-family channel as a template, we revealed potential interactions of oestrogen with the pore loop hERG mutation (G604S). Molecular models suggest that oestrogen and dofetilide blockade can concur simultaneously in the hERG channel pore.

ABSTRACT

Female sex is a risk factor for inherited and acquired long-QT associated torsade de pointes (TdP) arrhythmias, and sympathetic discharge is a major factor in triggering TdP in female long-QT syndrome patients. We used a combined experimental and computational approach to predict 'the perfect storm' of hormone concentration, I_Kr block and sympathetic stimulation that induces arrhythmia in females with inherited and acquired long-QT. More specifically, we developed mathematical models of acquired and inherited long-QT syndrome in male and female ventricular human myocytes by combining effects of a hormone and a hERG blocker, dofetilide, or hERG mutations. These 'male' and 'female' model myocytes and tissues then were used to predict how various sex-based differences underlie arrhythmia risk in the setting of acute sympathetic nervous system discharge. The model predicted increased risk for arrhythmia in females when acute sympathetic nervous system discharge was applied in the settings of both inherited and acquired long-QT syndrome. Females were predicted to have protection from arrhythmia induction when progesterone is high. Males were protected by the presence of testosterone. Structural modelling points towards two plausible and distinct mechanisms of oestrogen action enhancing torsadogenic effects: oestradiol interaction with hERG mutations in the pore loop containing G604 or with common TdP-related blockers in the intra-cavity binding site. Our study presents findings that constitute the first evidence linking structure to function mechanisms underlying female dominance of arousal-induced arrhythmias.

First universal pharmacophore model for hERG1 K+ channel activators: acthER

The intra-cavitary drug blockade of hERG1 channel has been extensively studied, both experimentally and theoretically. Structurally diverse ligands inadvertently block the hERG1 K⁺ channel currents lead to drug induced Long QT Syndrome (LQTS). Accordingly, designing either hERG1 channel openers or current activators, with the potential to target other binding pockets of the channel, has been introduced as a viable approach in modern anti-arrhythmia drug development. However, reports and investigations on the molecular mechanisms underlying activators binding to the hERG1 channel remain sparse and the overall molecular design principles are largely unknown. Most of the hERG1 activators were discovered during mandatory screening for hERG1 blockade. To fill this apparent deficit, the first universal pharmacophore model for hERG1 K⁺ channel activators was developed using PHASE. 3D structures of 18 hERG1 K⁺ channel activators and their corresponding measured binding affinity values were used in the development of pharmacophore models. These compounds spanned a range of structurally different chemotypes with moderate variation in binding affinity. A five sites AAHRR (A, hydrogen-bond accepting, H, hydrophobic, R, aromatic) pharmacophore model has shown reasonable high statistical results compared to the other developed more than 1000 hypotheses. This model was used to construct steric and electrostatic contour maps. The predictive power of the model was tested with 3 external test set compounds as true unknowns. Finally, the pharmacophore model was combined with the previously developed receptor-based model of hERG1 K⁺ channel to develop and screen novel activators. The results are quite striking and it suggests a greater future role for pharmacophore modeling and virtual drug screening simulations in deciphering complex patterns of molecular mechanisms of hERG1 channel openers at the target sites. The developed model is available upon request and it may serve as basis for the synthesis of novel therapeutic hERG1 activators.

Prediction of hERG Liability - Using SVM Classification, Bootstrapping and Jackknifing

Drug-induced QT prolongation leads to life-threatening cardiotoxicity, mostly through blockage of the human ether-à-go-go-related gene (hERG) encoded potassium ion (K+ ) channels. The hERG channel is one of the most important antitargets to be addressed in the early stage of drug discovery process, in order to avoid more costly failures in the development phase. Using a thallium flux assay, 4,323 molecules were screened for hERG channel inhibition in a quantitative high throughput screening (qHTS) format. Here, we present support vector classification (SVC) models of hERG channel inhibition with the averaged area under the receiver operator characteristics curve (AUC-ROC) of 0.93 for the tested compounds. Both Jackknifing and bootstrapping have been employed to rebalance the heavily biased training datasets, and the impact of these two under-sampling rebalance methods on the performance of the predictive models is discussed. Our results indicated that the rebalancing techniques did not enhance the predictive power of the resulting models; instead, adoption of optimal cutoffs could restore the desirable balance of sensitivity and specificity of the binary classifiers. In an external validation set of 66 drug molecules, the SVC model exhibited an AUC-ROC of 0.86, further demonstrating the utility of this modeling approach to predict hERG liabilities.

Computational Tool for Fast in silico Evaluation of hERG K+ Channel Affinity

The development of a novel comprehensive approach for the prediction of hERG activity is herein presented. Software Phase has been used to derive a 3D-QSAR model, employing as alignment rule a common pharmacophore built on a subset of 22 highly active compounds (threshold Ki: 50 nM) against hERG K⁺ channel. Five features comprised the pharmacophore: two aromatic rings (R₁ and R₂), one hydrogen-bond acceptor (A), one hydrophobic site (H), and one positive ionizable function (P). The sequential 3D-QSAR model developed with a set of 421 compounds (randomly divided in training and test set) yielded a test set (Q²) = 0.802 and proved to be predictive with respect to an external test set of 309 compounds that were not used to generate the model (rext_ts2 = 0.860). Furthermore, the model was submitted to an in silico validation for assessing the reliability of the approach, by applying a decoys set, evaluating the Güner and Henry score (GH) and the Enrichment Factor (EF), and by using the ROC curve analysis. The outcome demonstrated the high predictive power of the inclusive 3D-QSAR model developed for the hERG K⁺ channel blockers, confirming the fundamental validity of the chosen approach for obtaining a fast proprietary cardiotoxicity predictive tool to be employed for rationally designing compounds with reduced hERG K⁺ channel activity at the early steps of the drug discovery trajectory.

Investigation of PDE5/PDE6 and PDE5/PDE11 selective potent tadalafil-like PDE5 inhibitors using combination of molecular modeling approaches, molecular fingerprint-based virtual screening protocols and structure-based pharmacophore development

The essential biological function of phosphodiesterase (PDE) type enzymes is to regulate the cytoplasmic levels of intracellular second messengers, 3′,5′-cyclic guanosine monophosphate (cGMP) and/or 3′,5′-cyclic adenosine monophosphate (cAMP). PDE targets have 11 isoenzymes. Of these enzymes, PDE5 has attracted a special attention over the years after its recognition as being the target enzyme in treating erectile dysfunction. Due to the amino acid sequence and the secondary structural similarity of PDE6 and PDE11 with the catalytic domain of PDE5, first-generation PDE5 inhibitors (i.e. sildenafil and vardenafil) are also competitive inhibitors of PDE6 and PDE11. Since the major challenge of designing novel PDE5 inhibitors is to decrease their cross-reactivity with PDE6 and PDE11, in this study, we attempt to identify potent tadalafil-like PDE5 inhibitors that have PDE5/PDE6 and PDE5/PDE11 selectivity. For this aim, the similarity-based virtual screening protocol is applied for the “clean drug-like subset of ZINC database” that contains more than 20 million small compounds. Moreover, molecular dynamics (MD) simulations of selected hits complexed with PDE5 and off-targets were performed in order to get insights for structural and dynamical behaviors of the selected molecules as selective PDE5 inhibitors. Since tadalafil blocks hERG1 K channels in concentration dependent manner, the cardiotoxicity prediction of the hit molecules was also tested. Results of this study can be useful for designing of novel, safe and selective PDE5 inhibitors.

Potassium currents in the heart: functional roles in repolarization, arrhythmia and therapeutics

This is the second of the two White Papers from the fourth UC Davis Cardiovascular Symposium Systems Approach to Understanding Cardiac Excitation-Contraction Coupling and Arrhythmias (3-4 March 2016), a biennial event that brings together leading experts in different fields of cardiovascular research. The theme of the 2016 symposium was 'K⁺ channels and regulation', and the objectives of the conference were severalfold: (1) to identify current knowledge gaps; (2) to understand what may go wrong in the diseased heart and why; (3) to identify possible novel therapeutic targets; and (4) to further the development of systems biology approaches to decipher the molecular mechanisms and treatment of cardiac arrhythmias. The sessions of the Symposium focusing on the functional roles of the cardiac K⁺ channel in health and disease, as well as K⁺ channels as therapeutic targets, were contributed by Ye Chen-Izu, Gideon Koren, James Weiss, David Paterson, David Christini, Dobromir Dobrev, Jordi Heijman, Thomas O'Hara, Crystal Ripplinger, Zhilin Qu, Jamie Vandenberg, Colleen Clancy, Isabelle Deschenes, Leighton Izu, Tamas Banyasz, Andras Varro, Heike Wulff, Eleonora Grandi, Michael Sanguinetti, Donald Bers, Jeanne Nerbonne and Nipavan Chiamvimonvat as speakers and panel discussants. This article summarizes state-of-the-art knowledge and controversies on the functional roles of cardiac K⁺ channels in normal and diseased heart. We endeavour to integrate current knowledge at multiple scales, from the single cell to the whole organ levels, and from both experimental and computational studies.

Role of the pH in state-dependent blockade of hERG currents

Mutations that reduce inactivation of the voltage-gated Kv11.1 potassium channel (hERG) reduce binding for a number of blockers. State specific block of the inactivated state of hERG block may increase risks of drug-induced Torsade de pointes. In this study, molecular simulations of dofetilide binding to the previously developed and experimentally validated models of the hERG channel in open and open-inactivated states were combined with voltage-clamp experiments to unravel the mechanism(s) of state-dependent blockade. The computations of the free energy profiles associated with the drug block to its binding pocket in the intra-cavitary site display startling differences in the open and open-inactivated states of the channel. It was also found that drug ionization may play a crucial role in preferential targeting to the open-inactivated state of the pore domain. pH-dependent hERG blockade by dofetilie was studied with patch-clamp recordings. The results show that low pH increases the extent and speed of drug-induced block. Both experimental and computational findings indicate that binding to the open-inactivated state is of key importance to our understanding of the dofetilide’s mode of action.

In silico design of novel hERG-neutral sildenafil-like PDE5 inhibitors

Cyclic nucleotide phosphodiesterase enzymes (PDEs) have functions in regulating the levels of intracellular second messengers, 3', 5'-cyclic adenosine monophosphate (cAMP) and 3', 5'-cyclic guanosine monophosphate (cGMP), via hydrolysis and decomposing mechanisms in cells. They take essential roles in modulating various cellular activities such as memory and smooth muscle functions. PDE type 5 (PDE5) inhibitors enhance the vasodilatory effects of cGMP in the corpus cavernosum and they are used to treat erectile dysfunction. Patch clamp experiments showed that the IC₅₀ values of the human ether-à-go-go-related gene (hERG1) potassium (K) ion channel blocking affinity of PDE5 inhibitors sildenafil, vardenafil, and tadalafil as 33, 12, and 100 μM, respectively. hERG1 channel is responsible for the regulation of the action potential of human ventricular myocyte by contributing the rapid component of delayed rectifier K⁺ current (I_Kr) component of the cardiac action potential. In this work, interaction patterns and binding affinity predictions of selected PDE5 inhibitors against the hERG1 channel are studied. It is attempted to develop PDE5 inhibitor analogs with lower binding affinity to hERG1 ion channel while keeping their pharmacological activity against their principal target PDE5 using in silico methods. Based on detailed analyses of docking poses and predicted interaction energies, novel analogs of PDE5 inhibitors with lower predicted binding affinity to hERG1 channels without loosing their principal target activity were proposed. Moreover, molecular dynamics (MD) simulations and post-processing MD analyses (i.e. Molecular Mechanics/Generalized Born Surface Area calculations) were performed. Detailed analysis of molecular simulations helped us to better understand the PDE5 inhibitor-target binding interactions in the atomic level. Results of this study can be useful for designing of novel and safe PDE5 inhibitors with enhanced activity and other tailored properties.

Pharmacokinetic-pharmacodynamic modelling of drug-induced QTc interval prolongation in man: prediction from in vitro human ether-à-go-go-related gene binding and functional inhibition assays and conscious dog studies

BACKGROUND AND PURPOSE

Functional measures of human ether-à-go-go-related gene (hERG; Kv 11.1) channel inhibition have been prioritized as an in vitro screening tool for candidate molecules. However, it is unclear how these results can be translated to humans. Here, we explore how data on drug binding and functional inhibition in vitro relate to QT prolongation in vivo. Using cisapride, sotalol and moxifloxacin as paradigm compounds, we assessed the relationship between drug concentrations, binding, functional measures and in vivo effects in preclinical species and humans.

EXPERIMENTAL APPROACH

Pharmacokinetic-pharmacodynamic modelling was used to characterize the drug effects in hERG functional patch clamp, hERG radio-labelled dofetilide displacement experiments and QT interval in conscious dogs. Data were analysed in parallel to identify potential correlations between pharmacological activity in vitro and in vivo.

KEY RESULTS

An Emax model could not be used due to large variability in the functional patch clamp assay. Dofetilide displacement revealed that binding curves are unrelated to the in vivo potency estimates for QTc prolongation in dogs and humans. Mean in vitro potency estimates ranged from 99.9 nM for cisapride to 1030 μM for moxifloxacin.

CONCLUSIONS AND IMPLICATIONS

The lack of standardized protocols for in vitro assays leads to significant differences in experimental conditions, making the assessment of in vitro-in vivo correlations unreliable. Identification of an accurate safety window during the screening of candidate molecules requires a quantitative framework that disentangles system- from drug-specific properties under physiological conditions, enabling translation of the results to humans. Similar considerations will be relevant for the comprehensive in vitro pro-arrhythmia assay initiative.

Propofol inhibits hERG K+ channels and enhances the inhibition effects on its mutations in HEK293 cells

QT interval prolongation, a potential risk for arrhythmias, may result from gene polymorphisms relevant to cardiomyocyte repolarization. Another noted cause of QT interval prolongation is the administration of chemical compounds such as anesthetics, which may affect a specific type of cardiac K⁺ channel encoded by the human ether-a-go-go-related gene (hERG). hERG K⁺ current was recorded using whole-cell patch clamp in human embryonic kidney (HEK293) cells expressing wild type (WT) or mutated hERG channels. Expression of hERG K⁺ channel proteins was evaluated using western blot and confirmed by fluorescent staining and imaging. Computational modeling was adopted to identify the possible binding site(s) of propofol with hERG K⁺ channels. Propofol had a significant inhibitory effect on WT hERG K⁺ currents in a concentration-dependent manner, with a half-maximal inhibitory concentration (IC₅₀) of 60.9±6.4μM. Mutations in drug-binding sites (Y652A or F656C) of the hERG channel were found to attenuate hERG current blockage by propofol. However, propofol did not inhibit the trafficking of hERG protein to the cell membrane. Meanwhile, for the three selective hERG K⁺ channel mutant heterozygotes WT/Q738X-hERG, WT/A422T-hERG, and WT/H562P-hERG, the IC₅₀ of propofol was calculated as 14.2±2.8μM, 3.3±1.2μM, and 5.9±1.9μM, respectively, which were much lower than that for the wild type. These findings indicate that propofol may potentially increase QT interval prolongation risk in patients via direct inhibition of the hERG K⁺ channel, especially in those with other concurrent triggering factors such as hERG gene mutations.

NS1643 interacts around L529 of hERG to alter voltage sensor movement on the path to activation

Activators of hERG1 such as NS1643 are being developed for congenital/acquired long QT syndrome. Previous studies identify the neighborhood of L529 around the voltage-sensor as a putative interacting site for NS1643. With NS1643, the V1/2 of activation of L529I (-34 ± 4 mV) is similar to wild-type (WT) (-37 ± 3 mV; P > 0.05). WT and L529I showed no difference in the slope factor in the absence of NS1643 (8 ± 0 vs. 9 ± 0) but showed a difference in the presence of NS1643 (9 ± 0.3 vs. 22 ± 1; P < 0.01). Voltage-clamp-fluorimetry studies also indicated that in L529I, NS1643 reduces the voltage-sensitivity of S4 movement. To further assess mechanism of NS1643 action, mutations were made in this neighborhood. NS1643 shifts the V1/2 of activation of both K525C and K525C/L529I to hyperpolarized potentials (-131 ± 4 mV for K525C and -120 ± 21 mV for K525C/L529I). Both K525C and K525C/K529I had similar slope factors in the absence of NS1643 (18 ± 2 vs. 34 ± 5, respectively) but with NS1643, the slope factor of K525C/L529I increased from 34 ± 5 to 71 ± 10 (P < 0.01) whereas for K525C the slope factor did not change (18 ± 2 at baseline and 16 ± 2 for NS1643). At baseline, K525R had a slope factor similar to WT (9 vs. 8) but in the presence of NS1643, the slope factor of K525R was increased to 24 ± 4 vs. 9 ± 0 mV for WT (P < 0.01). Molecular modeling indicates that L529I induces a kink in the S4 voltage-sensor helix, altering a salt-bridge involving K525. Moreover, docking studies indicate that NS1643 binds to the kinked structure induced by the mutation with a higher affinity. Combining biophysical, computational, and electrophysiological evidence, a mechanistic principle governing the action of some activators of hERG1 channels is proposed.

New potential binding determinant for hERG channel inhibitors

Human ether-à-go-go related gene (hERG) 1 channels conduct the rapid delayed rectifier K⁺ current (I_Kr) and are essential for the repolarization of the cardiac action potential. hERG1 inhibition by structurally diverse drugs may lead to life threatening arrhythmia. Putative binding determinants of hERG1 channel blockers include T623, S624 and V625 on the pore helix, and residues G648, Y652 and F656, located on segment S6. We and others have previously hypothesized that additional binding determinants may be located on helix S5, which is in close contact with the S6 segments. In order to test this hypothesis, we performed a detailed investigation combining ionic current measurements with two-microelectrode voltage clamp and molecular modeling techniques. We identified a novel aromatic high affinity binding determinant for blockers located in helix S5, F557, which is equally potent as Y652. Modeling supports a direct interaction with the outer pore helix.

Combined gating and trafficking defect in Kv11.1 manifests as a malignant long QT syndrome phenotype in a large Danish p.F29L founder family

BACKGROUND

Congenital long QT syndrome (LQTS) is a hereditary cardiac channelopathy characterized by delayed ventricular repolarization, syncope, torsades de pointes and sudden cardiac death. Thirty-three members of five apparently 'unrelated' Danish families carry the KCNH2:c.87C> A; p.F29L founder mutation.

METHODS AND RESULTS

Linkage disequilibrium mapping with microsatellites around KCNH2 enabled us to estimate the age of the founder mutation to be approximately 22 generations, corresponding to around 550 years. Neighbouring-Joining analysis disclosed one early and three later nodes. The median QTc time of the carriers was 490 ms (range: 415-589 ms) and no difference was seen between the different branches of the family. The mutation is malignant with a penetrance of 73%. Ten F29L carriers received implantable defibrillators (ICDs) (median age at implant 20 years), and of those four individuals experienced eight appropriate shocks. Patch-clamp analysis in HEK 293 cells, performed at 34°C disclosed a loss-of-function phenotype with fast deactivation, reduced steady-state inactivation current density and a positive voltage shift in inactivation. Western blotting of HEK 293 cells transfected with KCNH2:WT and KCNH2:c.87C> A revealed a reduced fraction of fully glycosylated hERG:p.F29L suggesting that this mutation results in defective trafficking.

CONCLUSION

The altered channel gating kinetics in combination with defective trafficking of mutated channels is expected to result in reduced repolarizing current density and, thus, a LQTS phenotype.

Molecular Simulations of Solved Co-crystallized X-Ray Structures Identify Action Mechanisms of PDEδ Inhibitors

PDEδ is a small protein that binds and controls the trafficking of RAS subfamily proteins. Its inhibition protects initiation of RAS signaling, and it is one of the common targets considered for oncological drug development. In this study, we used solved x-ray structures of inhibitor-bound PDEδ targets to investigate mechanisms of action of six independent all-atom MD simulations. An analysis of atomic simulations combined with the molecular mechanic-Poisson-Boltzmann solvent accessible surface area/generalized Born solvent accessible surface area calculations led to the identification of action mechanisms for a panel of novel PDEδ inhibitors. To the best of our knowledge, this study is one of the first in silico investigations on co-crystallized PDEδ protein. A detailed atomic-scale understanding of the molecular mechanism of PDEδ inhibition may assist in the design of novel PDEδ inhibitors. One of the most common side effects for diverse small molecules/kinase inhibitors is their off-target interactions with cardiac ion channels and human-ether-a-go-go channel specifically. Thus, all of the studied PDEδ inhibitors are also screened in silico at the central cavities of hERG1 potassium channels.

Voltage sensor gating charge transfer in a hERG potassium channel model

Relaxation of a hERG K⁺ channel model during molecular-dynamics simulation in a hydrated POPC bilayer was accompanied by transitions of an arginine gating charge across a charge transfer center in two voltage sensor domains. Inspection of the passage of arginine side chains across the charge transfer center suggests that the unique hydration properties of the arginine guanidine cation facilitates charge transfer during voltage sensor responses to changes in membrane potential, and underlies the preference of Arg over Lys as a mobile charge carrier in voltage-sensitive ion channels.

Molecular docking and molecular dynamics studies on the structure-activity relationship of fluoroquinolone for the HERG channel

Fluoroquinolones play an important role in the treatment of serious bacterial infections, but at the same time they could lead to cardiac toxicity due to the blockage of the HERG potassium channel, which even leads to the withdrawal of some fluoroquinolones. Blockage of the HERG potassium channel by drugs or drug-like compounds has become a critical problem in drug discovery. Though there were large amounts of bioactivity data of fluoroquinolones on the blockage of HERG, little structural basis of binding of blockers to the HERG channel was known. Here, we combined molecular docking, molecular dynamics simulations, free energy calculations and binding energy decomposition analysis to explore the binding modes of fluoroquinolones in the HERG potassium channel. The calculated binding free energies were consistent with the experimental binding affinities. Our results showed that the CH3 group in MX was favorable for the binding to the HERG channel, while Tyr652 and Phe656 were critical for the hydrophobic interaction between fluoroquinolones and the HERG channel. We expected that our results of calculation could provide important insights for the rational design and discovery of drugs.

Getting to the heart of hERG K(+) channel gating

Potassium ion channels encoded by the human ether-a-go-go related gene (hERG) form the ion-conducting subunit of the rapid delayed rectifier potassium current (IKr ). Although hERG channels exhibit a widespread tissue distribution they play a particularly important role in the heart. There has been considerable interest in hERG K(+) channels for three main reasons. First, they have very unusual gating kinetics, most notably rapid and voltage-dependent inactivation coupled to slow deactivation, which has led to the suggestion that they may play a specific role in the suppression of arrhythmias. Second, mutations in hERG are the cause of 30-40% of cases of congenital long QT syndrome (LQTS), the commonest inherited primary arrhythmia syndrome. Third, hERG is the molecular target for the vast majority of drugs that cause drug-induced LQTS, the commonest cause of drug-induced arrhythmias and cardiac death. Drug-induced LQTS has now been reported for a large range of both cardiac and non-cardiac drugs, in which this side effect is entirely undesired. In recent years there have been comprehensive reviews published on hERG K(+) channels (Vandenberg et al. 2012) and we will not re-cover this ground. Rather, we focus on more recent work on the structural basis and dynamics of hERG gating with an emphasis on how the latest developments may facilitate translational research in the area of stratifying risk of arrhythmias.

In silico prediction of hERG inhibition

The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure–activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

Experimentally validated HERG pharmacophore models as cardiotoxicity prediction tools

The goal of this study was to design, experimentally validate, and apply a virtual screening workflow to identify novel hERG channel blockers. The hERG channel is an important antitarget in drug development since cardiotoxic risks remain as a major cause of attrition. A ligand-based pharmacophore model collection was developed and theoretically validated. The seven most complementary and suitable models were used for virtual screening of in-house and commercially available compound libraries. From the hit lists, 50 compounds were selected for experimental validation through bioactivity assessment using patch clamp techniques. Twenty compounds inhibited hERG channels expressed in HEK 293 cells with IC50 values ranging from 0.13 to 2.77 μM, attesting to the suitability of the models as cardiotoxicity prediction tools in a preclinical stage.

Structure driven design of novel human ether-a-go-go-related-gene channel (hERG1) activators

One of the main culprits in modern drug discovery is apparent cardiotoxicity of many lead-candidates via inadvertent pharmacologic blockade of K⁺, Ca²⁺ and Na⁺ currents. Many drugs inadvertently block hERG1 leading to an acquired form of the Long QT syndrome and potentially lethal polymorphic ventricular tachycardia. An emerging strategy is to rely on interventions with a drug that may proactively activate hERG1 channels reducing cardiovascular risks. Small molecules-activators have a great potential for co-therapies where the risk of hERG-related QT prolongation is significant and rehabilitation of the drug is impractical. Although a number of hERG1 activators have been identified in the last decade, their binding sites, functional moieties responsible for channel activation and thus mechanism of action, have yet to be established. Here, we present a proof-of-principle study that combines de-novo drug design, molecular modeling, chemical synthesis with whole cell electrophysiology and Action Potential (AP) recordings in fetal mouse ventricular myocytes to establish basic chemical principles required for efficient activator of hERG1 channel. In order to minimize the likelihood that these molecules would also block the hERG1 channel they were computationally engineered to minimize interactions with known intra-cavitary drug binding sites. The combination of experimental and theoretical studies led to identification of functional elements (functional groups, flexibility) underlying efficiency of hERG1 activators targeting binding pocket located in the S4–S5 linker, as well as identified potential side-effects in this promising line of drugs, which was associated with multi-channel targeting of the developed drugs.

A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity

Acquired cardiac long QT syndrome (LQTS) is a frequent drug-induced toxic event that is often caused through blocking of the human ether-á-go-go-related (hERG) K(+) ion channel. This has led to the removal of several major drugs post-approval and is a frequent cause of termination of clinical trials. We report here a computational atomistic model derived using long molecular dynamics that allows sensitive prediction of hERG blockage. It identified drug-mediated hERG blocking activity of a test panel of 18 compounds with high sensitivity and specificity and was experimentally validated using hERG binding assays and patch clamp electrophysiological assays. The model discriminates between potent, weak, and non-hERG blockers and is superior to previous computational methods. This computational model serves as a powerful new tool to predict hERG blocking thus rendering drug development safer and more efficient. As an example, we show that a drug that was halted recently in clinical development because of severe cardiotoxicity is a potent inhibitor of hERG in two different biological assays which could have been predicted using our new computational model.

Pore dynamics and conductance of RyR1 transmembrane domain

Ryanodine receptors (RyR) are calcium release channels, playing a major role in the regulation of muscular contraction. Mutations in skeletal muscle RyR (RyR1) are associated with congenital diseases such as malignant hyperthermia and central core disease (CCD). The absence of high-resolution structures of RyR1 has limited our understanding of channel function and disease mechanisms at the molecular level. Previously, we have reported a hypothetical structure of the RyR1 pore-forming region, obtained by homology modeling and supported by mutational scans, electrophysiological measurements, and cryo-electron microscopy. Here, we utilize the expanded model encompassing six transmembrane helices to calculate the RyR1 pore region conductance, to analyze its structural stability, and to hypothesize the mechanism of the Ile4897 CCD-associated mutation. The calculated conductance of the wild-type RyR1 suggests that the proposed pore structure can sustain ion currents measured in single-channel experiments. We observe a stable pore structure on timescales of 0.2 μs, with multiple cations occupying the selectivity filter and cytosolic vestibule, but not the inner chamber. We further suggest that stability of the selectivity filter critically depends on the interactions between the I4897 residue and several hydrophobic residues of the neighboring subunit. Loss of these interactions in the case of polar substitution I4897T results in destabilization of the selectivity filter, a possible cause of the CCD-specific reduced Ca(2+) conductance.