Liensinine, a Novel and Food-Derived Compound, Exerts Potent Antihepatoma Efficacy via Inhibiting the Kv10.1 Channel.

Plant metabolites from natural product extracts offer unique advantages against carcinogenesis in the development of drugs. The target-based virtual screening from food-derived compounds represents a promising approach for tumor therapy. In this study, we performed virtual screening to target the presumed inhibitor-binding pocket and identified a highly potent Kv10.1 inhibitor, liensinine (Lien), which can inhibit the channel in a dose-dependent way with an IC50 of 0.24 ± 0.07 µM. Combining molecular dynamics simulations with mutagenesis experiments, our data show that Lien interacts with Kv10.1 by binding with Y539, T543, D551, E553, and H601 in the C-linker domain of Kv10.1. In addition, the interaction of sequence alignment and 3D structural modeling revealed differences between the C-linker domain of the Kv10.1 channel and the Kv11.1 channel. Furthermore, antitumor experiments revealed that Lien suppresses the proliferation and migration of HCC both in vitro and in vivo. In summary, the food-derived compound, Lien, may serve as a lead compound for antihepatoma therapeutic drugs targeting Kv10.1.

Molecular mechanism of EAG1 channel inhibition by imipramine binding to the PAS domain.

Ether-a-go-go (EAG) channels are key regulators of neuronal excitability and tumorigenesis. EAG channels contain an N-terminal Per-Arnt-Sim (PAS) domain that can regulate currents from EAG channels by binding small molecules. The molecular mechanism of this regulation is not clear. Using surface plasmon resonance and electrophysiology we show that a small molecule ligand imipramine can bind to the PAS domain of EAG1 channels and inhibit EAG1 currents via this binding. We further used a combination of molecular dynamics (MD) simulations, electrophysiology, and mutagenesis to investigate the molecular mechanism of EAG1 current inhibition by imipramine binding to the PAS domain. We found that Tyr71, located at the entrance to the PAS domain cavity, serves as a "gatekeeper" limiting access of imipramine to the cavity. MD simulations indicate that the hydrophobic electrostatic profile of the cavity facilitates imipramine binding and in silico mutations of hydrophobic cavity-lining residues to negatively charged glutamates decreased imipramine binding. Probing the PAS domain cavity-lining residues with site-directed mutagenesis, guided by MD simulations, identified D39 and R84 as residues essential for the EAG1 channel inhibition by imipramine binding to the PAS domain. Taken together, our study identified specific residues in the PAS domain that could increase or decrease EAG1 current inhibition by imipramine binding to the PAS domain. These findings should further the understanding of molecular mechanisms of EAG1 channel regulation by ligands and facilitate the development of therapeutic agents targeting these channels.

Hydroxychloroquine Attenuates hERG Channel by Promoting the Membrane Channel Degradation: Computational Simulation and Experimental Evidence for QT-Interval Prolongation with Hydroxychloroquine Treatment.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has led to millions of confirmed cases and deaths worldwide and has no approved therapy. Currently, more than 700 drugs are tested in the COVID-19 clinical trials, and full evaluation of their cardiotoxicity risks is in high demand. METHODS: We mainly focused on hydroxychloroquine (HCQ), one of the most concerned drugs for COVID-19 therapy, and investigated the effects and underlying mechanisms of HCQ on hERG channel via molecular docking simulations. We further applied the HEK293 cell line stably expressing hERG-wild-type channel (hERG-HEK) and HEK293 cells transiently expressing hERG-p.Y652A or hERG-p.F656A mutants to validate our predictions. Western blot analysis was used to determine the hERG channel, and the whole-cell patch clamp was utilized to record hERG current (IhERG). RESULTS: HCQ reduced the mature hERG protein in a time- and concentration-dependent manner. Correspondingly, chronic and acute treatment of HCQ decreased the hERG current. Treatment with brefeldin A (BFA) and HCQ combination reduced hERG protein to a greater extent than BFA alone. Moreover, disruption of the typical hERG binding site (hERG-p.Y652A or hERG-p.F656A) rescued HCQ-mediated hERG protein and IhERG reduction. CONCLUSION: HCQ can reduce the mature hERG channel expression and IhERG via enhancing channel degradation. The QT prolongation effect of HCQ is mediated by typical hERG binding sites involving residues Tyr652 and Phe656.

Machine-learning technique, QSAR and molecular dynamics for hERG-drug interactions.

One of the most well-known anti-targets defining medication cardiotoxicity is the voltage-dependent hERG K + channel, which is well-known for its crucial involvement in cardiac action potential repolarization. Torsades de Pointes, QT prolongation, and sudden death are all caused by hERG (the human Ether-à-go-go-Related Gene) inhibition. There is great interest in creating predictive computational (in silico) tools to identify and weed out potential hERG blockers early in the drug discovery process because testing for hERG liability and the traditional experimental screening are complicated, expensive and time-consuming. This study used 2D descriptors of a large curated dataset of 6766 compounds and machine learning approaches to build robust descriptor-based QSAR and predictive classification models for KCNH2 liability. Decision Tree, Random Forest, Logistic Regression, Ada Boosting, kNN, SVM, Naïve Bayes, neural network and stochastic gradient classification classifier algorithms were used to build classification models. If a compound's IC50 value was between 10 µM and less, it was classified as a blocker (hERG-positive), and if it was more, it was classified as a non-blocker (hERG-negative). Matthew's correlation coefficient formula and F1score were applied to compare and track the developed models' performance. Molecular docking and dynamics studies were performed to understand the cardiotoxicity relating to the hERG-gene. The hERG residues interacting after 100 ns are LEU:697, THR:708, PHE:656, HIS:674, HIS:703, TRP:705 and ASN:709 and the hERG-ligand-16 complex trajectory showed stable behaviour with lesser fluctuations in the entire simulation of 200 ns.Communicated by Ramaswamy H. Sarma.

Mechanism underlying delayed rectifying in human voltage-mediated activation Eag2 channel.

The transmembrane voltage gradient is a general physico-chemical cue that regulates diverse biological function through voltage-gated ion channels. How voltage sensing mediates ion flows remains unknown at the molecular level. Here, we report six conformations of the human Eag2 (hEag2) ranging from closed, pre-open, open, and pore dilation but non-conducting states captured by cryo-electron microscopy (cryo-EM). These multiple states illuminate dynamics of the selectivity filter and ion permeation pathway with delayed rectifier properties and Cole-Moore effect at the atomic level. Mechanistically, a short S4-S5 linker is coupled with the constrict sites to mediate voltage transducing in a non-domain-swapped configuration, resulting transitions for constrict sites of F464 and Q472 from gating to open state stabilizing for voltage energy transduction. Meanwhile, an additional potassium ion occupied at positions S6 confers the delayed rectifier property and Cole-Moore effects. These results provide insight into voltage transducing and potassium current across membrane, and shed light on the long-sought Cole-Moore effects.

Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models.

The human ether-a-go-go (hERG) potassium channel (Kv11.1) plays a critical role in mediating cardiac action potential. The blockade of this ion channel can potentially lead fatal disorder and/or long QT syndrome. Many drugs have been withdrawn because of their serious hERG-cardiotoxicity. It is crucial to assess the hERG blockade activity in the early stage of drug discovery. We are particularly interested in the hERG-cardiotoxicity of compounds collected in the DrugBank database considering that many DrugBank compounds have been approved for therapeutic treatments or have high potential to become drugs. Machine learning-based in silico tools offer a rapid and economical platform to virtually screen DrugBank compounds. We design accurate and robust classifiers for blockers/non-blockers and then build regressors to quantitatively analyze the binding potency of the DrugBank compounds on the hERG channel. Molecular sequences are embedded with two natural language processing (NLP) methods, namely, autoencoder and transformer. Complementary three-dimensional (3D) molecular structures are embedded with two advanced mathematical approaches, i.e., topological Laplacians and algebraic graphs. With our state-of-the-art tools, we reveal that 227 out of the 8641 DrugBank compounds are potential hERG blockers, suggesting serious drug safety problems. Our predictions provide guidance for the further experimental interrogation of DrugBank compounds' hERG-cardiotoxicity.

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

The repolarizing current (Ikr) 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.

Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism.

Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Failure or inhibition of hERG channel activity caused by drug molecules can lead to prolonging QT interval, which will result in serious cardiotoxicity. Thus, evaluating the hERG blocking activity of all these small molecular compounds is technically challenging, and the relevant procedures are expensive and time-consuming. In this study, we develop a novel deep learning predictive model named DMFGAM for predicting hERG blockers. In order to characterize the molecule more comprehensively, we first consider the fusion of multiple molecular fingerprint features to characterize its final molecular fingerprint features. Then, we use the multi-head attention mechanism to extract the molecular graph features. Both molecular fingerprint features and molecular graph features are fused as the final features of the compounds to make the feature expression of compounds more comprehensive. Finally, the molecules are classified into hERG blockers or hERG non-blockers through the fully connected neural network. We conduct 5-fold cross-validation experiment to evaluate the performance of DMFGAM, and verify the robustness of DMFGAM on external validation datasets. We believe DMFGAM can serve as a powerful tool to predict hERG channel blockers in the early stages of drug discovery and development.

Early Steps in C-Type Inactivation of the hERG Potassium Channel.

Fast C-type inactivation confers distinctive functional properties to the hERG potassium channel, and its association to inherited and acquired cardiac arrythmias makes the study of the inactivation mechanism of hERG at the atomic detail of paramount importance. At present, two models have been proposed to describe C-type inactivation in K+-channels. Experimental data and computational work on the bacterial KcsA channel support the hypothesis that C-type inactivation results from a closure of the selectivity filter that sterically impedes ion conduction. Alternatively, recent experimental structures of a mutated Shaker channel revealed a widening of the extracellular portion of the selectivity filter, which might diminish conductance by interfering with the mechanism of ion permeation. Here, we performed molecular dynamics simulations of the wild-type hERG, a non-inactivating mutant (hERG-N629D), and a mutant that inactivates faster than the wild-type channel (hERG-F627Y) to find out which and if any of the two reported C-type inactivation mechanisms applies to hERG. Closure events of the selectivity filter were not observed in any of the simulated trajectories but instead, the extracellular section of the selectivity filter deviated from the canonical conductive structure of potassium channels. The degree of widening of the potassium binding sites at the extracellular entrance of the channel was directly related to the degree of inactivation with hERG-F627Y > wild-type hERG > hERG-N629D. These findings support the hypothesis that C-type inactivation in hERG entails a widening of the extracellular entrance of the channel rather than a closure of the selectivity filter.

Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction.

In an earlier study (Didziapetris R & Lanevskij K (2016). J Comput Aided Mol Des. 30:1175-1188) we collected a database of publicly available hERG inhibition data for almost 6700 drug-like molecules and built a probabilistic Gradient Boosting classifier with a minimal set of physicochemical descriptors (log P, pKa, molecular size and topology parameters). This approach favored interpretability over statistical performance but still achieved an overall classification accuracy of 75%. In the current follow-up work we expanded the database (provided in Supplementary Information) to almost 9400 molecules and performed temporal validation of the model on a set of novel chemicals from recently published lead optimization projects. Validation results showed almost no performance degradation compared to the original study. Additionally, we rebuilt the model using AFT (Accelerated Failure Time) learning objective in XGBoost, which accepts both quantitative and censored data often reported in protein inhibition studies. The new model achieved a similar level of accuracy of discerning hERG blockers from non-blockers at 10 µM threshold, which can be conceived as close to the performance ceiling for methods aiming to describe only non-specific ligand interactions with hERG. Yet, this model outputs quantitative potency values (IC50) and is not tied to a particular classification cut-off. pIC50 from patch-clamp measurements can be predicted with R2 ≈ 0.4 and MAE < 0.5, which enables ligand ranking according to their expected potency levels. The employed approach can be valuable for quantitative modeling of various ADME and drug safety endpoints with a high prevalence of censored data.

Multiple mechanisms underlie reduced potassium conductance in the p.T1019PfsX38 variant of hERG.

Long QT syndrome type II (LQT2) is caused by loss-of-function mutations in the hERG K+ channel, leading to increased incidence of cardiac arrest and sudden death. Many genetic variants have been reported in the hERG gene with various consequences on channel expression, permeation, and gating. Only a small number of LQT2 causing variants has been characterized to define the underlying pathophysiological causes of the disease. We sought to determine the characteristics of the frameshift variant p.Thr1019ProfsX38 (T1019PfsX38) which affects the C-terminus of the protein. This mutation was identified in an extended Omani family of LQT2. It replaces the last 140 amino acids of hERG with 37 unique amino acids. T1019 is positioned at a distinguished region of the C-terminal tail of hERG, as predicted from the deep learning system AlphaFold v2.0. We employed the whole-cell configuration of the patch-clamp technique to study wild-type and mutant channels that were transiently expressed in human embryonic kidney 293 (HEK293) cells. Depolarizing voltages elicited slowly deactivating tail currents that appeared upon repolarization of cells that express either wild-type- or T1019PfsX38-hERG. There were no differences in the voltage and time dependencies of activation between the two variants. However, the rates of hERG channel deactivation at hyperpolarizing potentials were accelerated by T1019PfsX38. In addition, the voltage dependence of inactivation of T1019PfsX38-hERG was shifted by 20 mV in the negative direction when compared with wild-type hERG. The rates of channel inactivation were increased in the mutant channel variant. Next, we employed a step-ramp protocol to mimic membrane repolarization by the cardiac action potential. The amplitudes of outward currents and their integrals were reduced in the mutant variant when compared with the wild-type variant during repolarization. Thus, changes in the gating dynamics of hERG by the T1019PfsX38 variant contribute to the pathology seen in affected LQT2 patients.

Use of Solvent Mapping for Characterizing the Binding Site and for Predicting the Inhibition of the Human Ether-á-Go-Go-Related K+ Channel.

Molecular dynamics was used to optimize the droperidol-hERG complex obtained from docking. To accommodate the inhibitor, residues T623, S624, V625, G648, Y652, and F656 did not move significantly during the simulation, while F627 moved significantly. Binding sites in cryo-EM structures and in structures obtained from molecular dynamics simulations were characterized using solvent mapping and Atlas ligands, which were negative images of the binding site, were generated. Atlas ligands were found to be useful for identifying human ether-á-go-go-related potassium channel (hERG) inhibitors by aligning compounds to them or by guiding the docking of compounds in the binding site. A molecular dynamics optimized structure of hERG led to improved predictions using either compound alignment to the Atlas ligand or docking. The structure was also found to be suitable to define a strategy for lowering inhibition based on the proposed binding mode of compounds in the channel.

Molecular Determinants for the High-Affinity Blockade of Human Ether-à-go-go-Related Gene K + Channel by Tolterodine.

ABSTRACT: Tolterodine is a first-line antimuscarinic drug used to treat overactive bladder. Adverse cardiac effects including tachycardia and palpitations have been observed, presumably because of its inhibition of the human ether-à-go-go-related gene (hERG) K + channel. However, the molecular mechanism of hERG channel inhibition by tolterodine is largely unclear. In this study, we performed molecular docking to identify potential binding sites of tolterodine in hERG channel, and two-microelectrode voltage-clamp to record the currents of hERG and its mutants expressed in Xenopus oocytes. The results of computational modeling demonstrated that phenylalanine at position 656 (F656) and tyrosine at position 652 (Y652) on the S6 helix of hERG channel are the most favorable binding residues of tolterodine, which was validated by electrophysiological recordings on Y652A and F656A hERG mutants. The Y652A and F656A mutations decreased inhibitory potency of tolterodine 345-fold and 126-fold, respectively. The Y652A mutation significantly altered the voltage dependence of channel inhibition by tolterodine. For both the wild-type and the mutant channels, tolterodine reduced the currents in a time-dependent manner, and the blockade occurred with the channel activated. Tolterodine did not interfere with hERG channel deactivation, whereas channel inactivation greatly impaired its blocking effect. The inhibition of hERG channel by tolterodine is independent of its action on muscarinic acetylcholine receptors. In conclusion, tolterodine is an open-state blocker of hERG K + channel with nanomolar potency. Y652 and F656, 2 aromatic residues on the inner S6 helix, are responsible for the high-affinity binding of tolterodine to hERG channel.

The N-linker region of hERG1a upregulates hERG1b potassium channels.

A major physiological role of hERG1 (human Ether-á-go-go-Related Gene 1) potassium channels is to repolarize cardiac action potentials. Two isoforms, hERG1a and hERG1b, associate to form the potassium current IKr in cardiomyocytes. Inherited mutations in hERG1a or hERG1b cause prolonged cardiac repolarization, long QT syndrome, and sudden death arrhythmia. hERG1a subunits assemble with and enhance the number of hERG1b subunits at the plasma membrane, but the mechanism for the increase in hERG1b by hERG1a is not well understood. Here, we report that the hERG1a N-terminal region expressed in trans with hERG1b markedly increased hERG1b currents and increased biotin-labeled hERG1b protein at the membrane surface. hERG1b channels with a deletion of the N-terminal 1b domain did not have a measurable increase in current or biotinylated protein when coexpressed with hERG1a N-terminal regions, indicating that the 1b domain was required for the increase in hERG1b. Using a biochemical pull-down interaction assay and a FRET hybridization experiment, we detected a direct interaction between the hERG1a N-terminal region and the hERG1b N-terminal region. Using engineered deletions and alanine mutagenesis, we identified a short span of amino acids at positions 216 to 220 within the hERG1a "N-linker" region that were necessary for the upregulation of hERG1b. We propose that direct structural interactions between the hERG1a N-linker region and the hERG1b 1b domain increase hERG1b at the plasma membrane. Mechanisms regulating hERG1a and hERG1b are likely critical for cardiac function, may be disrupted by long QT syndrome mutants, and serve as potential targets for therapeutics.

BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers.

Unintended inhibition of the human ether-à-go-go-related gene (hERG) ion channel by small molecules leads to severe cardiotoxicity. Thus, hERG channel blockage is a significant concern in the development of new drugs. Several computational models have been developed to predict hERG channel blockage, including deep learning models; however, they lack robustness, reliability and interpretability. Here, we developed a graph-based Bayesian deep learning model for hERG channel blocker prediction, named BayeshERG, which has robust predictive power, high reliability and high resolution of interpretability. First, we applied transfer learning with 300 000 large data in initial pre-training to increase the predictive performance. Second, we implemented a Bayesian neural network with Monte Carlo dropout to calibrate the uncertainty of the prediction. Third, we utilized global multihead attentive pooling to augment the high resolution of structural interpretability for the hERG channel blockers and nonblockers. We conducted both internal and external validations for stringent evaluation; in particular, we benchmarked most of the publicly available hERG channel blocker prediction models. We showed that our proposed model outperformed predictive performance and uncertainty calibration performance. Furthermore, we found that our model learned to focus on the essential substructures of hERG channel blockers via an attention mechanism. Finally, we validated the prediction results of our model by conducting in vitro experiments and confirmed its high validity. In summary, BayeshERG could serve as a versatile tool for discovering hERG channel blockers and helping maximize the possibility of successful drug discovery. The data and source code are available at our GitHub repository (https://github.com/GIST-CSBL/BayeshERG).

Combining multi-dimensional molecular fingerprints to predict the hERG cardiotoxicity of compounds.

Recently, drug toxicity has become a critical problem with heavy medical and economic burdens. Acquired long QT syndrome (acLQTS) is an acquired cardiac ion channel disease caused by drugs blocking the hERG channel. Therefore, it is necessary to avoid cardiotoxicity in drug design, and computer models have been widely used to fix this predicament. In this study, we collected a hERG inhibitor dataset containing 8671 compounds, and then, these compounds were featurized by traditional molecular fingerprints (including Baseline2D, ECFP4, PropertyFP, and 3DFP) and the newly proposed molecular dynamics fingerprint (MDFP). Subsequently, regression prediction models were established by using four machine learning algorithms based on these fingerprints and the combined multi-dimensional molecular fingerprints (MultiFP). After cross-validation and independent test dataset validation, the results show that the best model was built by the consensus of four algorithms with MultiFP, and this model bests recently published methods in terms of hERG cardiotoxicity prediction with a RMSE of 0.531 and a R2 of 0.653 on the test dataset. Feature importance analysis and correlation analysis identified some novel structural features and molecular dynamics features that are highly associated with the hERG inhibition of compounds. Our findings provide new insight into multi-dimensional molecular fingerprints and consensus models for hERG cardiotoxicity prediction.

Molecular dynamics simulations suggest possible activation and deactivation pathways in the hERG channel.

The elusive activation/deactivation mechanism of hERG is investigated, a voltage-gated potassium channel involved in severe inherited and drug-induced cardiac channelopathies, including the Long QT Syndrome. Firstly, the available structural data are integrated by providing a homology model for the closed state of the channel. Secondly, molecular dynamics combined with a network analysis revealed two distinct pathways coupling the voltage sensor domain with the pore domain. Interestingly, some LQTS-related mutations known to impair the activation/deactivation mechanism are distributed along the identified pathways, which thus suggests a microscopic interpretation of their role. Split channels simulations clarify a surprising feature of this channel, which is still able to gate when a cut is introduced between the voltage sensor domain and the neighboring helix S5. In summary, the presented results suggest possible activation/deactivation mechanisms of non-domain-swapped potassium channels that may aid in biomedical applications.

Patients with KCNH1-related intellectual disability without distinctive features of Zimmermann-Laband/Temple-Baraitser syndrome.

De novo missense variants in KCNH1 encoding Kv10.1 are responsible for two clinically recognisable phenotypes: Temple-Baraitser syndrome (TBS) and Zimmermann-Laband syndrome (ZLS). The clinical overlap between these two syndromes suggests that they belong to a spectrum of KCNH1-related encephalopathies. Affected patients have severe intellectual disability (ID) with or without epilepsy, hypertrichosis and distinctive features such as gingival hyperplasia and nail hypoplasia/aplasia (present in 20/23 reported cases).We report a series of seven patients with ID and de novo pathogenic KCNH1 variants identified by whole-exome sequencing or an epilepsy gene panel in whom the diagnosis of TBS/ZLS had not been first considered. Four of these variants, p.(Thr294Met), p.(Ala492Asp), p.(Thr493Asn) and p.(Gly496Arg), were located in the transmembrane domains S3 and S6 of Kv10.1 and one, p.(Arg693Gln), in its C-terminal cyclic nucleotide-binding homology domain (CNBHD). Clinical reappraisal by the referring clinical geneticists confirmed the absence of the distinctive gingival and nail features of TBS/ZLS.Our study expands the phenotypical spectrum of KCNH1-related encephalopathies to individuals with an attenuated extraneurological phenotype preventing a clinical diagnosis of TBS or ZLS. This subtype may be related to recurrent substitutions of the Gly496, suggesting a genotype-phenotype correlation and, possibly, to variants in the CNBHD domain.

Molecular dynamics of hERG channel: insights into understanding the binding of small molecules for detuning cardiotoxicity.

Evaluation of cardiotoxicity potential of new chemical entities (NCEs) has lately become one of the stringent filters in the drug discovery and development process. Cardiotoxicity is caused mainly by the inhibition of human ether-a-go-go related gene (hERG) channel protein. Inhibition of the hERG channel leads to a life-threatening condition known as cardiac arrhythmia. Knowledge of the structural behaviour of the hERG would aid greatly in the design of new drug molecules that do not interact with the protein and add to the safety index. In this study, a computational model for the active-state of hERG was developed. This model was equilibrated by performing the molecular dynamics simulations for 100 ns followed by clustering and selection of a representative structure based on the largest populated cluster. To study the changes in the protein structure on inhibition, three inhibitory ligands, namely, dofetilide, cisapride and terfenadine were docked, followed by molecular dynamics simulations of 200 ns for the apo and each ligand-bound structure. It was observed that docking and simulation studies of the hERG model exhibited noticeable conformational changes in the protein upon ligand-binding. A significant change in the kink of the S6-transmembrane helix was observed. Inter-chain distances between the crucial residues Y652 and F656 (present below the ion-selectivity filter), their side-chain orientation and hydrogen bonding indicated a probable collapse of the pore. These changes may infer the initiation in transition of hERG from an open to an inactive state. Hence, these findings would help in designing compounds devoid of hERG inhibition with reduced cardiotoxicity.Communicated by Ramaswamy H. Sarma.

Molecular Dynamics-Derived Pharmacophore Model Explaining the Nonselective Aspect of KV10.1 Pore Blockers.

The KV10.1 voltage-gated potassium channel is highly expressed in 70% of tumors, and thus represents a promising target for anticancer drug discovery. However, only a few ligands are known to inhibit KV10.1, and almost all also inhibit the very similar cardiac hERG channel, which can lead to undesirable side-effects. In the absence of the structure of the KV10.1-inhibitor complex, there remains the need for new strategies to identify selective KV10.1 inhibitors and to understand the binding modes of the known KV10.1 inhibitors. To investigate these binding modes in the central cavity of KV10.1, a unique approach was used that allows derivation and analysis of ligand-protein interactions from molecular dynamics trajectories through pharmacophore modeling. The final molecular dynamics-derived structure-based pharmacophore model for the simulated KV10.1-ligand complexes describes the necessary pharmacophore features for KV10.1 inhibition and is highly similar to the previously reported ligand-based hERG pharmacophore model used to explain the nonselectivity of KV10.1 pore blockers. Moreover, analysis of the molecular dynamics trajectories revealed disruption of the π-π network of aromatic residues F359, Y464, and F468 of KV10.1, which has been reported to be important for binding of various ligands for both KV10.1 and hERG channels. These data indicate that targeting the KV10.1 channel pore is also likely to result in undesired hERG inhibition, and other potential binding sites should be explored to develop true KV10.1-selective inhibitors as new anticancer agents.