Cryo-EM Structure of K+-Bound hERG Channel Complexed with the Blocker Astemizole

CUPID: A free drug discovery platform for the explainable multi-ion channel assessment of cardiotoxicity

The withdrawal of numerous approved drugs in late development stages, or even from the market, due to safety concerns remains a major challenge, contributing to the high attrition rate in drug discovery and development. Among these concerns, cardiotoxicity is a critical toxicological issue, particularly in oncology, as drugs can induce heart damage by triggering pathological conditions such as arrhythmia, myocardial infarction, and myocardial hypertrophy. Here, we introduce CUPID (Cardiotox Understanding Platform for Intelligent Drug Discovery), an explainable artificial intelligence (XAI) framework designed to predict cardiotoxicity associated with ERG (ether-à-go-go-related gene) potassium, Nav1.5 sodium, and Cav1.2 calcium ion channels. The framework was trained using three carefully curated interspecies experimental datasets from the latest ChEMBL database (release 34) and the CSFP (Core-Substituent Fingerprint), which encodes molecular fragments derived from the decomposition of drug-like small molecules. By leveraging these experimental datasets, highly accurate explainable machine learning models were developed, achieving approximately 80 % accuracy in 5-fold stratified cross-validation analyses. CUPID provides a comprehensive risk assessment of early cardiotoxicity and a key feature is its interpretability: predictions are annotated with clear applicability domain information, while chemical substructures linked to cardiotoxicity risks are highlighted using SHAP (SHapley Additive exPlanations) values. This enhances molecular understanding and facilitates the rational design of safer bioactive compounds. Last but not least, CUPID is freely accessible at https://prometheus.farmacia.uniba.it/cupid.

Molecular insights into the rescue mechanism of an HERG activator against severe LQT2 mutations

BACKGROUND

Mutations in the HERG potassium channel are a major cause of long QT syndrome type 2 (LQT2), which can lead to sudden cardiac death. The HERG channel plays a critical role in the repolarization of the myocardial action potential, and loss-of-function mutations prolong cardiac repolarization.

METHODS

In this study, we investigated the efficacy and underlying molecular mechanism of ICA-105574, an HERG activator, in shortening the duration of cardiac repolarization in severe LQT2 variants. We characterized the efficacy of ICA-105574 in vivo, using an animal model to assess its ability to shorten the QT interval and in vitro, in cellular models mimicking severe HERG channel mutations (A561V, G628S, and L779P) to evaluate its impact in enhancing IKr current. Additionally, molecular dynamics simulations were used to investigate the molecular mechanism of ICA-105574 action.

RESULTS

In vivo, ICA-105574 significantly shortened the QT interval. LQT2 mutations drastically reduced IKr amplitude and suppressed tail currents in cellular models. ICA-105574 restored IKr in A561V and G628S. Finally, in silico data showed that ICA-105574 stabilizes a pattern of interactions similar to gain-of-function SQT1 mutations and can reverse the G628S modifications, through an allosteric network linking the binding site to the selectivity filter and the S5P turret helix, thereby restoring its K+ ion permeability.

CONCLUSIONS

Our results support the development of HERG activators like ICA-105574 as promising pharmacological molecules against some severe LQT2 mutations and suggest that molecular dynamics simulations can be used to test the ability of molecules to modulate HERG function in silico, paving the way for the rational design of new HERG activators.

Unlocking the secrets of Cardiac development and function: the critical role of FHL2

FHL2 (Four-and-a-half LIM domain protein 2) is a crucial factor involved in cardiac morphogenesis, the process by which the heart develops its complex structure. It is expressed in various tissues during embryonic development, including the developing heart, and has been shown to play important roles in cell proliferation, differentiation, and migration. FHL2 interacts with multiple proteins to regulate cardiac development as a coactivator or a corepressor. It is involved in cardiac specification and determination of cell fate, cardiomyocyte growth, cardiac remodeling, myofibrillogenesis, and the regulation of HERG channels. Targeting FHL2 has therapeutic implications as it could improve cardiac function, control arrhythmias, alleviate heart failure, and maintain cardiac integrity in various pathological conditions. The identification of FHL2 as a signature gene in atrial fibrillation suggests its potential as a diagnostic marker and therapeutic target for this common arrhythmia.

Approaching Pharmacological Space: Events and Components

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

Stereoselective block of the hERG potassium channel by the Class Ia antiarrhythmic drug disopyramide

Potassium channels encoded by human Ether-à-go-go-Related Gene (hERG) are inhibited by diverse cardiac and non-cardiac drugs. Disopyramide is a chiral Class Ia antiarrhythmic that inhibits hERG at clinical concentrations. This study evaluated effects of disopyramide enantiomers on hERG current (IhERG) from hERG expressing HEK 293 cells at 37 °C. S(+) and R(-) disopyramide inhibited wild-type (WT) IhERG with IC50 values of 3.9 µM and 12.9 µM respectively. The attenuated-inactivation mutant N588K had little effect on the action of S(+) disopyramide but the IC50 for the R(-) enantiomer was ~ 15-fold that for S(+) disopyramide. The enhanced inactivation mutant N588E only slightly increased the potency of R(-) disopyramide. S6 mutation Y652A reduced S(+) disopyramide potency more than that of R(-) disopyramide (respective IC50 values ~ 49-fold and 11-fold their WT controls). The F656A mutation also exerted a stronger effect on S(+) than R(-) disopyramide, albeit with less IC50 elevation. A WT-Y652A tandem dimer exhibited a sensitivity to the enantiomers that was intermediate between that of WT and Y652A, suggesting Y652 groups on adjacent subunits contribute to the binding. Moving the Y (normally at site 652) one residue in the N- terminal (up) direction in N588K hERG markedly increased the blocking potency of R(-) disopyramide. Molecular dynamics simulations using a hERG pore model produced different binding modes for S(+) and R(-) disopyramide consistent with the experimental observations. In conclusion, S(+) disopyramide interacts more strongly with S6 aromatic binding residues on hERG than does R(-) disopyramide, whilst optimal binding of the latter is more reliant on intact inactivation.

Improved higher resolution cryo-EM structures reveal the binding modes of hERG channel inhibitors

During drug discovery, it is crucial to exclude compounds with toxic effects. The human ether-à-go-go-related gene (hERG) channel is essential for maintaining cardiac repolarization and is a critical target in drug safety evaluation due to its role in drug-induced arrhythmias. Inhibition of the hERG channel can lead to severe cardiac issues, including Torsades de Pointes tachycardia. Understanding hERG inhibition mechanisms is essential to avoid these toxicities. Several structural studies have elucidated the interactions between inhibitors and hERG. However, orientation and resolution issues have so far limited detailed insights. Here, we used digitonin to analyze the apo state of hERG, which resolved orientation issues and improved the resolution. We determined the structure of hERG bound to astemizole, showing a clear map in the pore pathway. Using this strategy, we also analyzed the binding modes of E-4031 and pimozide. These insights into inhibitor interactions with hERG may aid safer drug design and enhance cardiac safety.

Structural biology and molecular pharmacology of voltage-gated ion channels

Voltage-gated ion channels (VGICs), including those for Na+, Ca2+ and K+, selectively permeate ions across the cell membrane in response to changes in membrane potential, thus participating in physiological processes involving electrical signalling, such as neurotransmission, muscle contraction and hormone secretion. Aberrant function or dysregulation of VGICs is associated with a diversity of neurological, psychiatric, cardiovascular and muscular disorders, and approximately 10% of FDA-approved drugs directly target VGICs. Understanding the structure-function relationship of VGICs is crucial for our comprehension of their working mechanisms and role in diseases. In this Review, we discuss how advances in single-particle cryo-electron microscopy have afforded unprecedented structural insights into VGICs, especially on their interactions with clinical and investigational drugs. We present a comprehensive overview of the recent advances in the structural biology of VGICs, with a focus on how prototypical drugs and toxins modulate VGIC activities. We explore how these structures elucidate the molecular basis for drug actions, reveal novel pharmacological sites, and provide critical clues to future drug discovery.

Inhibition of hERG K channels by verapamil at physiological temperature: Implications for the CiPA initiative

The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative reassesses using the inhibition of hERG potassium channels by drugs as the major determinant for the potential to cause drug-induced Torsades de Pointes (TdP) cardiac arrhythmias. Here we report our findings on the next phase of CiPA: Determination of hERG inhibitory properties using the standard CiPA-defined data acquisition protocol, here called the standard protocol, at physiological temperature (37 degrees Celsius). To do this, we measured inhibition of hERG1a potassium channels stably expressed in HEK293 cells by the small molecule verapamil, using manual whole-cell patch-clamp electrophysiology recordings with the standard protocol, which is characterized, in part, by a series of 10 s duration voltage steps to 0 mV, ultimately leading to a cumulative recording time of approximately 30 min. Using the standard protocol, we measured an IC50 for verapamil of 225 nM, a Hill coefficient of 1, and time constant of inhibition at 0 mV of 0.64 s. But, using the standard protocol resulted in a very low (5 %) experimental success rate per cell, which had low practicality for future experiments. To address the 5 % success rate, we generated a revised protocol characterized, in part, by a series of 3 s duration voltage steps to 0 mV, leading to a cumulative recording time of approximately 10 min. Using the revised protocol, we found an IC50 for verapamil of 252 nM, a Hill coefficient of 0.8, and time constant of inhibition at 0 mV of 0.67 s. The values measured with the revised protocol were similar to those measured using the standard protocol and, furthermore, our success rate using the revised protocol rose to 25 %, an increase of 5-fold over the standard protocol, and more in line with the success rate for biophysical studies. In summary, we captured key pharmacological data for subsequent analysis in CiPA using a revised protocol with an increased success rate and an overall enhanced feasibility and practicality. We propose that the revised protocol may be more pragmatic for generation of some hERG channel drug inhibition data for CiPA and other regulatory sciences.

Unlocking the potential of higher-molecular-weight 5-HT7R ligands: Synthesis, affinity, and ADMET examination

An increasing number of drugs introduced to the market and numerous repositories of compounds with confirmed activity have posed the need to revalidate the state-of-the-art rules that determine the ranges of properties the compounds should possess to become future drugs. In this study, we designed a series of two chemotypes of aryl-piperazine hydantoin ligands of 5-HT7R, an attractive target in search for innovative CNS drugs, with higher molecular weight (close to or over 500). Consequently, 14 new compounds were synthesised and screened for their receptor activity accompanied by extensive docking studies to evaluate the observed structure-activity/properties relationships. The ADMET characterisation in terms of the biological membrane permeability, metabolic stability, hepatotoxicity, cardiotoxicity, and protein plasma binding of the obtained compounds was carried out in vitro. The outcome of these studies constituted the basis for the comprehensive challenge of computational tools for ADMET properties prediction. All the compounds possessed high affinity to the 5-HT7R (Ki below 250 nM for all analysed structures) with good selectivity over 5-HT6R and varying affinity towards 5-HT2AR, 5-HT1AR and D2R. For the best compounds of this study, the expression profile of genes associated with neurodegeneration, anti-oxidant response and anti-inflammatory function was determined, and the survival of the cells (SH-SY5Y as an in vitro model of Alzheimer's disease) was evaluated. One 5-HT7R agent (32) was characterised by a very promising ADMET profile, i.e. good membrane permeability, low hepatotoxicity and cardiotoxicity, and high metabolic stability with the simultaneous high rate of plasma protein binding and high selectivity over other GPCRs considered, together with satisfying gene expression profile modulations and neural cell survival. Such encouraging properties make it a good candidate for further testing and optimisation as a potential agent in the treatment of CNS-related disorders.

A Deep Learning Approach to Uncover Voltage-Gated Ion Channels' Intermediate States

Owing to recent advancements in cryo-electron microscopy, voltage-gated ion channels have gained a greater comprehension of their structural characteristics. However, a significant enigma remains unsolved for a large majority of these channels: their gating mechanism. This mechanism, which encompasses the conformational changes between open and closed states, is pivotal to their proper functioning. Beyond the binary states of open and closed, an ensemble of intermediate states defines the transition path in-between. Due to the lack of experimental data, one might resort to molecular dynamics simulations as an alternative to decipher these states and the transitions between them. However, the high-energy barriers and the colossal time scales involved hinder access to the latter. We present here an application of deep learning as a reliable pipeline for a comprehensive exploration of voltage-gated ion channel conformational rearrangements during gating. We showcase the pipeline performance specifically on the Kv1.2 voltage sensor domain and confront the results with existing data. We demonstrate how our physics-based deep learning approach contributes to the theoretical understanding of these channels and how it might provide further insights into the exploration of channelopathies.

Potassium dependent structural changes in the selectivity filter of HERG potassium channels

The fine tuning of biological electrical signaling is mediated by variations in the rates of opening and closing of gates that control ion flux through different ion channels. Human ether-a-go-go related gene (HERG) potassium channels have uniquely rapid inactivation kinetics which are critical to the role they play in regulating cardiac electrical activity. Here, we exploit the K+ sensitivity of HERG inactivation to determine structures of both a conductive and non-conductive selectivity filter structure of HERG. The conductive state has a canonical cylindrical shaped selectivity filter. The non-conductive state is characterized by flipping of the selectivity filter valine backbone carbonyls to point away from the central axis. The side chain of S620 on the pore helix plays a central role in this process, by coordinating distinct sets of interactions in the conductive, non-conductive, and transition states. Our model represents a distinct mechanism by which ion channels fine tune their activity and could explain the uniquely rapid inactivation kinetics of HERG.

Computational insights into the mechanisms underlying structural destabilization and recovery in trafficking-deficient hERG mutants

Cardiovascular diseases are a major global health concern, responsible for a significant number of deaths each year, often linked to cardiac arrhythmias resulting from dysfunction in ion channels. Hereditary Long QT Syndrome (LQTS) is a condition characterized by a prolonged QT interval on ECG, increasing the risk of sudden cardiac death. The most common type of LQTS, LQT2, is caused by mutations in the hERG gene, affecting a potassium ion channel. The majority of these mutations disrupt the channel's trafficking to the cell membrane, leading to intracellular retention. Specific high-affinity hERG blockers (e.g., E-4031) can rescue this mutant phenotype, but the exact mechanism is unknown. This study used accelerated molecular dynamics simulations to investigate how these mutations affect the hERG channel's structure, folding, endoplasmic reticulum (ER) retention, and trafficking. We reveal that these mutations induce structural changes in the channel, narrowing its central pore and altering the conformation of the intracellular domains. These changes expose internalization signals that contribute to ER retention and degradation of the mutant hERG channels. Moreover, the study found that the trafficking rescue drug E-4031 can inhibit these structural changes, potentially rescuing the mutant channels. This research offers valuable insights into the structural issues responsible for the degradation of rescuable transmembrane trafficking mutants. Understanding the defective trafficking structure of the hERG channel could help identify binding sites for small molecules capable of restoring proper folding and facilitating channel trafficking. This knowledge has the potential to lead to mechanism-based therapies that address the condition at the cellular level, which may prove more effective than treating clinical symptoms, ultimately offering hope for individuals with hereditary Long QT Syndrome.

Multiple hERG channel blocking pathways: implications for macromolecules

Numerous non-cardiovascular drugs have a potential to induce life-threatening torsades de pointes (TdP) ventricular cardiac arrhythmias by blocking human ether-à-go-go-related gene (hERG) currents via binding to the channel's inner cavity. Identification of the hERG current-inhibiting properties of candidate drugs is performed focusing on binding sites in the channel pore. It has been suggested that biologicals have a low likelihood of hERG current inhibition, since their poor diffusion across the plasma membrane prevents them from reaching the binding site in the channel pore. However, biologicals could influence hERG channel function by binding to 'unconventional' noncanonical binding sites. This Opinion gives an overview on noncanonical blockers of hERG channels that might be of relevance for the assessment of the possible torsadogenic potential of macromolecular therapeutics.

ezAlign: A Tool for Converting Coarse-Grained Molecular Dynamics Structures to Atomistic Resolution for Multiscale Modeling

Soft condensed matter is challenging to study due to the vast time and length scales that are necessary to accurately represent complex systems and capture their underlying physics. Multiscale simulations are necessary to study processes that have disparate time and/or length scales, which abound throughout biology and other complex systems. Herein we present ezAlign, an open-source software for converting coarse-grained molecular dynamics structures to atomistic representation, allowing multiscale modeling of biomolecular systems. The ezAlign v1.1 software package is publicly available for download at github.com/LLNL/ezAlign. Its underlying methodology is based on a simple alignment of an atomistic template molecule, followed by position-restraint energy minimization, which forces the atomistic molecule to adopt a conformation consistent with the coarse-grained molecule. The molecules are then combined, solvated, minimized, and equilibrated with position restraints. Validation of the process was conducted on a pure POPC membrane and compared with other popular methods to construct atomistic membranes. Additional examples, including surfactant self-assembly, membrane proteins, and more complex bacterial and human plasma membrane models, are also presented. By providing these examples, parameter files, code, and an easy-to-follow recipe to add new molecules, this work will aid future multiscale modeling efforts.

Enhancing hERG Risk Assessment with Interpretable Classificatory and Regression Models

The human Ether-à-go-go-Related Gene (hERG) is a transmembrane protein that regulates cardiac action potential, and its inhibition can induce a potentially deadly cardiac syndrome. In vitro tests help identify hERG blockers at early stages; however, the high cost motivates searching for alternative, cost-effective methods. The primary goal of this study was to enhance the Pred-hERG tool for predicting hERG blockage. To achieve this, we developed new QSAR models that incorporated additional data, updated existing classificatory and multiclassificatory models, and introduced new regression models. Notably, we integrated SHAP (SHapley Additive exPlanations) values to offer a visual interpretation of these models. Utilizing the latest data from ChEMBL v30, encompassing over 14,364 compounds with hERG data, our binary and multiclassification models outperformed both the previous iteration of Pred-hERG and all publicly available models. Notably, the new version of our tool introduces a regression model for predicting hERG activity (pIC50). The optimal model demonstrated an R2 of 0.61 and an RMSE of 0.48, surpassing the only available regression model in the literature. Pred-hERG 5.0 now offers users a swift, reliable, and user-friendly platform for the early assessment of chemically induced cardiotoxicity through hERG blockage. The tool provides versatile outcomes, including (i) classificatory predictions of hERG blockage with prediction reliability, (ii) multiclassificatory predictions of hERG blockage with reliability, (iii) regression predictions with estimated pIC50 values, and (iv) probability maps illustrating the contribution of chemical fragments for each prediction. Furthermore, we implemented explainable AI analysis (XAI) to visualize SHAP values, providing insights into the contribution of each feature to binary classification predictions. A consensus prediction calculated based on the predictions of the three developed models is also present to assist the user's decision-making process. Pred-hERG 5.0 has been designed to be user-friendly, making it accessible to users without computational or programming expertise. The tool is freely available at http://predherg.labmol.com.br.

Non-invasive hERG channel screening based on electrical impedance tomography and extracellular voltage activation (EIT-EVA)

hERG channel screening has been achieved based on electrical impedance tomography and extracellular voltage activation (EIT-EVA) to improve the non-invasive aspect of drug discovery. EIT-EVA screens hERG channels by considering the change in extracellular ion concentration which modifies the extracellular resistance in cell suspension. The rate of ion passing in cell suspension is calculated from the extracellular resistance Rex, which is obtained from the EIT measurement at a frequency of 500 kHz. In the experiment, non-invasive screening is applied by a novel integrated EIT-EVA printed circuit board (PCB) sensor to human embryonic kidney (HEK) 293 cells transfected with the human ether-a-go-go-related gene (hERG) ion channel, while the E-4031 antiarrhythmic drug is used for hERG channel inhibition. The extracellular resistance Rex of the HEK 293 cells suspension is measured by EIT as the hERG channels are activated by EVA over time. The Rex is reconstructed into extracellular conductivity distribution change Δσ to reflect the extracellular K+ ion concentration change Δc resulting from the activated hERG channel. Δc is increased rapidly during the hERG channel non-inhibition state while Δc is increased slower with increasing drug concentration cd. In order to evaluate the EIT-EVA system, the inhibitory ratio index (IR) was calculated based on the rate of Δc over time. Half-maximal inhibitory concentration (IC50) of 2.7 nM is obtained from the cd and IR dose-response relationship. The IR from EIT-EVA is compared with the results from the patch-clamp method, which gives R2 of 0.85. In conclusion, EIT-EVA is successfully applied to non-invasive hERG channel screening.

Population-specific variations in KCNH2 predispose patients to delayed ventricular repolarization upon dihydroartemisinin-piperaquine therapy

Dihydroartemisinin-piperaquine is efficacious for the treatment of uncomplicated malaria and its use is increasing globally. Despite the positive results in fighting malaria, inhibition of the Kv11.1 channel (hERG; encoded by the KCNH2 gene) by piperaquine has raised concerns about cardiac safety. Whether genetic factors could modulate the risk of piperaquine-mediated QT prolongations remained unclear. Here, we first profiled the genetic landscape of KCNH2 variability using data from 141,614 individuals. Overall, we found 1,007 exonic variants distributed over the entire gene body, 555 of which were missense. By optimizing the gene-specific parametrization of 16 partly orthogonal computational algorithms, we developed a KCNH2-specific ensemble classifier that identified a total of 116 putatively deleterious missense variations. To evaluate the clinical relevance of KCNH2 variability, we then sequenced 293 Malian patients with uncomplicated malaria and identified 13 variations within the voltage sensing and pore domains of Kv11.1 that directly interact with channel blockers. Cross-referencing of genetic and electrocardiographic data before and after piperaquine exposure revealed that carriers of two common variants, rs1805121 and rs41314375, experienced significantly higher QT prolongations (ΔQTc of 41.8 ms and 61 ms, respectively, vs 14.4 ms in controls) with more than 50% of carriers having increases in QTc >30 ms. Furthermore, we identified three carriers of rare population-specific variations who experienced clinically relevant delayed ventricular repolarization. Combined, our results map population-scale genetic variability of KCNH2 and identify genetic biomarkers for piperaquine-induced QT prolongation that could help to flag at-risk patients and optimize efficacy and adherence to antimalarial therapy.

Corydaline binds to a druggable pocket of hEAG1 channel and inhibits hepatic carcinoma cell viability

Ether-à-go-go (EAG) potassium channels play a crucial role in the regulation of neuronal excitability and cancer progression, rendering them potential drug targets for cancer therapy. However, the scarcity of information regarding the selection sites on hEAG1 has posed a challenge in the discovery of new hEAG1 inhibitors. In this study, we introduced a novel natural product, corydaline, which selectively inhibits the hEAG1 channel without sensitivity to other KCNH channels. The IC50 of corydaline for the hEAG1 channel was 11.3 ± 0.6 μM, whereas the IC50 for hEAG2 and hERG1 were 73.6 ± 9.9 μM and 111.4 ± 8.5 μM, respectively. Molecular dynamics simulations together with site-directed mutagenesis, have unveiled that the site corydaline forms interactions with Lys217, Phe273, Pro276, Trp295 and Arg366, situated within the intracellular transmembrane segments S1-S4 of the voltage-sensor domain, be considered a novel drug pocket for hEAG1. Additionally, the intergaration of sequence alignment and 3D structural modeling revealed differences between the voltage sensor domain of hEAG1 channel and other EAG channels, suggesting the feasibility of a VSD modulation approach that could potentially lead to the selective inhibition of hEAG1 channels. Furthermore, antitumor experiments demonstrated that corydaline can inhibit the proliferation and migration of hepatic carcinoma cells by targeting hEAG1. The identification of this novel druggable pocket offers the possibility for drug screening against diseases linked to abnormal hEAG1 channels.

Molecular mechanism of antihistamines recognition and regulation of the histamine H1 receptor

Histamine receptors are a group of G protein-coupled receptors (GPCRs) that play important roles in various physiological and pathophysiological conditions. Antihistamines that target the histamine H1 receptor (H1R) have been widely used to relieve the symptoms of allergy and inflammation. Here, to uncover the details of the regulation of H1R by the known second-generation antihistamines, thereby providing clues for the rational design of newer antihistamines, we determine the cryo-EM structure of H1R in the apo form and bound to different antihistamines. In addition to the deep hydrophobic cavity, we identify a secondary ligand-binding site in H1R, which potentially may support the introduction of new derivative groups to generate newer antihistamines. Furthermore, these structures show that antihistamines exert inverse regulation by utilizing a shared phenyl group that inserts into the deep cavity and block the movement of the toggle switch residue W4286.48. Together, these results enrich our understanding of GPCR modulation and facilitate the structure-based design of novel antihistamines.

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.

Facilitation of hERG Activation by Its Blocker: A Mechanism to Reduce Drug-Induced Proarrhythmic Risk

Modulation of the human Ether-à-go-go-Related Gene (hERG) channel, a crucial voltage-gated potassium channel in the repolarization of action potentials in ventricular myocytes of the heart, has significant implications on cardiac electrophysiology and can be either antiarrhythmic or proarrhythmic. For example, hERG channel blockade is a leading cause of long QT syndrome and potentially life-threatening arrhythmias, such as torsades de pointes. Conversely, hERG channel blockade is the mechanism of action of Class III antiarrhythmic agents in terminating ventricular tachycardia and fibrillation. In recent years, it has been recognized that less proarrhythmic hERG blockers with clinical potential or Class III antiarrhythmic agents exhibit, in addition to their hERG-blocking activity, a second action that facilitates the voltage-dependent activation of the hERG channel. This facilitation is believed to reduce the proarrhythmic potential by supporting the final repolarizing of action potentials. This review covers the pharmacological characteristics of hERG blockers/facilitators, the molecular mechanisms underlying facilitation, and their clinical significance, as well as unresolved issues and requirements for research in the fields of ion channel pharmacology and drug-induced arrhythmias.

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.

Inhibition of the hERG Potassium Channel by a Methanesulphonate-Free E-4031 Analogue

hERG (human Ether-à-go-go Related Gene)-encoded potassium channels underlie the cardiac rapid delayed rectifier (IKr) potassium current, which is a major target for antiarrhythmic agents and diverse non-cardiac drugs linked to the drug-induced form of long QT syndrome. E-4031 is a high potency hERG channel inhibitor from the methanesulphonanilide drug family. This study utilized a methanesulphonate-lacking E-4031 analogue, "E-4031-17", to evaluate the role of the methanesulphonamide group in E-4031 inhibition of hERG. Whole-cell patch-clamp measurements of the hERG current (IhERG) were made at physiological temperature from HEK 293 cells expressing wild-type (WT) and mutant hERG constructs. For E-4031, WT IhERG was inhibited by a half-maximal inhibitory concentration (IC50) of 15.8 nM, whilst the comparable value for E-4031-17 was 40.3 nM. Both compounds exhibited voltage- and time-dependent inhibition, but they differed in their response to successive applications of a long (10 s) depolarisation protocol, consistent with greater dissociation of E-4031-17 than the parent compound between applied commands. Voltage-dependent inactivation was left-ward voltage shifted for E-4031 but not for E-4031-17; however, inhibition by both compounds was strongly reduced by attenuated-inactivation mutations. Mutations of S6 and S5 aromatic residues (F656V, Y652A, F557L) greatly attenuated actions of both drugs. The S624A mutation also reduced IhERG inhibition by both molecules. Overall, these results demonstrate that the lack of a methanesulphonate in E-4031-17 is not an impediment to high potency inhibition of IhERG.

Mitigating hERG Liability of Toll-Like Receptor 9 and 7 Antagonists through Structure-Based Design

hERG is considered to be a primary anti-target in the drug development process, as the K+ channel encoded by hERG plays an important role in cardiac re-polarization. It is desirable to address the hERG safety liability during early-stage development to avoid the expenses of validating leads that will eventually fail at a later stage. We have previously reported the development of highly potent quinazoline-based TLR7 and TLR9 antagonists for possible application against autoimmune disease. Initial experimental hERG assessment showed that most of the lead TLR7 and TLR9 antagonists suffer from hERG liability rendering them ineffective for further development. The present study herein describes a coordinated strategy to integrate the understanding from structure-based protein-ligand interaction to develop non- hERG binders with IC50 >30 μM with retention of TLR7/9 antagonism through a single point change in the scaffold. This structure-guided strategy can serve as a prototype for abolishing hERG liability during lead optimization.

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.

Potassium channels, tumorigenesis and targeted drugs

Potassium channels play an important role in human physiological function. Recently, various molecular mechanisms have implicated abnormal functioning of potassium channels in the proliferation, migration, invasion, apoptosis, and cancer stem cell phenotype formation. Potassium channels also mediate the association of tumor cells with the tumor microenvironment. Meanwhile, potassium channels are important targets for cancer chemotherapy. A variety of drugs exert anti-cancer effects by modulating potassium channels in tumor cells. Therefore, there is a need to understand how potassium channels participate in tumor development and progression, which could reveal new, novel targets for cancer diagnosis and treatment. This review summarizes the roles of voltage-gated potassium channels, calcium-activated potassium channels, inwardly rectifying potassium channels, and two-pore domain potassium channels in tumorigenesis and the underlying mechanism of potassium channel-targeted drugs. Therefore, the study lays the foundation for rational and effective drug design and individualized clinical therapeutics.

In silico evaluation of usnic acid derivatives to discover potential antibacterial drugs against DNA gyrase B and DNA topoisomerase IV

Due to the rising increase in infectious diseases brought on by bacteria and anti-bacterial drug resistance, antibacterial therapy has become difficult. The majority of first-line antibiotics are no longer effective against numerous germs, posing a new hazard to global human health in the 21st century. Through the drug-likeness screening, 184 usnic acid derivatives were selected from an in-house database of 340 usnic acid compounds. The pharmacokinetics (ADMET) prediction produced fifteen hit compounds, of which the lead molecule was subsequently obtained through a molecular docking investigation. The lead compounds, labelled compound-277 and compound-276, respectively, with the substantial binding affinity towards the enzymes were obtained through further docking simulation on the DNA gyrase and DNA topoisomerase proteins. Additionally, molecular dynamic (MD) simulation was performed for 300 ns on the lead compounds in order to confirm the stability of the docked complexes and the binding pose discovered during docking tests. Due to their intriguing pharmacological characteristics, these substances may be promising therapeutic candidate for anti-bacterial medication.Communicated by Ramaswamy H. Sarma.

Calculations of the binding free energies of the Comprehensive in vitro Proarrhythmia Assay (CiPA) reference drugs to cardiac ion channels

The evaluation of the inhibitory activities of drugs on multiple cardiac ion channels is required for the accurate assessment of proarrhythmic risks. Moreover, the in silico prediction of such inhibitory activities of drugs on cardiac channels can improve the efficiency of the drug-development process. Here, we performed molecular docking simulations to predict the complex structures of 25 reference drugs that were proposed by the Comprehensive in vitro Proarrhythmia Assay consortium using two cardiac ion channels, the human ether-a-go-go-related gene (hERG) potassium channel and human NaV1.5 (hNaV1.5) sodium channel, with experimentally available structures. The absolute binding free energy (ΔGbind) values of the predicted structures were calculated by a molecular dynamics-based method and compared with the experimental half-maximal inhibitory concentration (IC50) data. Furthermore, the regression analysis between the calculated values and negative of the common logarithm of the experimental IC50 values (pIC50) revealed that the calculated values of four and ten drugs deviated significantly from the regression lines of the hERG and hNaV1.5 channels, respectively. We reconsidered the docking poses and protonation states of the drugs based on the experimental data and recalculated their ΔGbind values. Finally, the calculated ΔGbind values of 24 and 19 drugs correlated with their experimental pIC50 values (coefficients of determination=0.791 and 0.613 for the hERG and hNaV1.5 channels, respectively). Thus, the regression analysis between the calculated ΔGbind and experimental IC50 data ensured the realization of an increased number of reliable complex structures.

Ensemble of structure and ligand-based classification models for hERG liability profiling

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

Binding of RPR260243 at the intracellular side of the hERG1 channel pore domain slows closure of the helix bundle crossing gate

The opening and closing of voltage-dependent potassium channels is dependent on a tight coupling between movement of the voltage sensing S4 segments and the activation gate. A specific interaction between intracellular amino- and carboxyl-termini is required for the characteristically slow rate of channel closure (deactivation) of hERG1 channels. Compounds that increase hERG1 channel currents represent a novel approach for prevention of arrhythmia associated with prolonged ventricular repolarization. RPR260243 (RPR), a quinoline oxo-propyl piperidine derivative, inhibits inactivation and dramatically slows the rate of hERG1 channel deactivation. Here we report that similar to its effect on wild-type channels, RPR greatly slows the deactivation rate of hERG1 channels missing their amino-termini, or of split channels lacking a covalent link between the voltage sensor domain and the pore domain. By contrast, RPR did not slow deactivation of C-terminal truncated hERG1 channels or D540K hERG1 mutant channels activated by hyperpolarization. Together, these findings indicate that ability of RPR to slow deactivation requires an intact C-terminus, does not slow deactivation by stabilizing an interaction involving the amino-terminus or require a covalent link between the voltage sensor and pore domains. All-atom molecular dynamics simulations using the cryo-EM structure of the hERG1 channel revealed that RPR binds to a pocket located at the intracellular ends of helices S5 and S6 of a single subunit. The slowing of channel deactivation by RPR may be mediated by disruption of normal S5-S6 interactions.

Structural modeling of the hERG potassium channel and associated drug interactions

The voltage-gated potassium channel, KV11.1, encoded by the human Ether-à-go-go-Related Gene (hERG), is expressed in cardiac myocytes, where it is crucial for the membrane repolarization of the action potential. Gating of the hERG channel is characterized by rapid, voltage-dependent, C-type inactivation, which blocks ion conduction and is suggested to involve constriction of the selectivity filter. Mutations S620T and S641A/T within the selectivity filter region of hERG have been shown to alter the voltage dependence of channel inactivation. Because hERG channel blockade is implicated in drug-induced arrhythmias associated with both the open and inactivated states, we used Rosetta to simulate the effects of hERG S620T and S641A/T mutations to elucidate conformational changes associated with hERG channel inactivation and differences in drug binding between the two states. Rosetta modeling of the S641A fast-inactivating mutation revealed a lateral shift of the F627 side chain in the selectivity filter into the central channel axis along the ion conduction pathway and the formation of four lateral fenestrations in the pore. Rosetta modeling of the non-inactivating mutations S620T and S641T suggested a potential molecular mechanism preventing F627 side chain from shifting into the ion conduction pathway during the proposed inactivation process. Furthermore, we used Rosetta docking to explore the binding mechanism of highly selective and potent hERG blockers - dofetilide, terfenadine, and E4031. Our structural modeling correlates well with much, but not all, existing experimental evidence involving interactions of hERG blockers with key residues in hERG pore and reveals potential molecular mechanisms of ligand interactions with hERG in an inactivated state.

Membranes under the Magnetic Lens: A Dive into the Diverse World of Membrane Protein Structures Using Cryo-EM

Membrane proteins are highly diverse in both structure and function and can, therefore, present different challenges for structure determination. They are biologically important for cells and organisms as gatekeepers for information and molecule transfer across membranes, but each class of membrane proteins can present unique obstacles to structure determination. Historically, many membrane protein structures have been investigated using highly engineered constructs or using larger fusion proteins to improve solubility and/or increase particle size. Other strategies included the deconstruction of the full-length protein to target smaller soluble domains. These manipulations were often required for crystal formation to support X-ray crystallography or to circumvent lower resolution due to high noise and dynamic motions of protein subdomains. However, recent revolutions in membrane protein biochemistry and cryo-electron microscopy now provide an opportunity to solve high resolution structures of both large, >1 megadalton (MDa), and small, <100 kDa (kDa), drug targets in near-native conditions, routinely reaching resolutions around or below 3 Å. This review provides insights into how the recent advances in membrane biology and biochemistry, as well as technical advances in cryo-electron microscopy, help us to solve structures of a large variety of membrane protein groups, from small receptors to large transporters and more complex machineries.

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.

Pore opening, not voltage sensor movement, underpins the voltage-dependence of facilitation by a hERG blocker

A drug that blocks the cardiac myocyte voltage-gated K+ channels encoded by the human Ether-à-go-go-Related Gene (hERG) carries a potential risk of long QT syndrome and life-threatening cardiac arrhythmia, including Torsade de Points Interestingly, certain hERG blockers can also facilitate hERG activation to increase hERG currents, which may reduce proarrhythmic potential. However, the molecular mechanism involved in the facilitation effect of hERG blockers remains unclear. The hallmark feature of the facilitation effect by hERG blockers is that a depolarizing preconditioning pulse shifts voltage-dependence of hERG activation to more negative voltages. Here we utilize a D540K hERG mutant to study the mechanism of the facilitation effect. D540K hERG is activated by not only depolarization but also hyperpolarization. This unusual gating property enables tests of the mechanism by which voltage induces facilitation of hERG by blockers. With D540K hERG, we find that nifekalant, a hERG blocker and Class III antiarrhythmic agent, blocks and facilitates not only current activation by depolarization but also current activation by hyperpolarization, suggesting a shared gating process upon depolarization and hyperpolarization. Moreover, in response to hyperpolarizing conditioning pulses, nifekalant facilitates D540K hERG currents but not wild-type currents. Our results indicate that induction of facilitation is coupled to pore opening, not voltage per se We propose that gated access to the hERG central cavity underlies the voltage-dependence of induction of facilitation. This study identifies hERG channel pore gate opening as the conformational change facilitated by nifekalant, a clinically important antiarrhythmic agent. Significance Statement Nifekalant is a clinically important antiarrhythmic agent and a hERG blocker which can also facilitate voltage-dependent activation of hERG channels after a preconditioning pulse. Here we show that the mechanism of action of the preconditioning pulse is to open a conductance gate to enable drug access to a facilitation site. Moreover, we find that facilitation increases hERG currents by altering pore dynamics, rather than acting through voltage sensors.

Inhibition of human ether-à-go-go-related gene K+ currents expressed in HEK293 cells by three gingerol components from ginger

OBJECTIVES

Gingerols are bioactive compounds derived from ginger, our experiment investigates the effects of 6-, 8- and 10-Gin on the human ether-à-go-go-related gene (hERG) K+ channels by using patch clamp technology.

KEY FINDINGS

hERG K+ currents were suppressed by 6-, 8- and 10-Gin in a concentration-dependent manner. The IC50 values of 6-, 8- and 10-Gin were 41.5, 16.1 and 86.5 μM for the hERG K+ currents, respectively. The maximum inhibitory effects caused by 6-, 8- and 10-Gin were 44.3% ± 2.0%, 88.6% ± 1.3% and 63.1% ± 1.1%, respectively, and the effects were almost completely reversible.

CONCLUSION

These findings suggest that 8-Gin is the most potent hERG K+ channel inhibitor among gingerol components and may offer a new approach for understanding and treating cancer.

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.

The ERG1 K+ Channel and Its Role in Neuronal Health and Disease

The ERG1 potassium channel, encoded by KCNH2, has long been associated with cardiac electrical excitability. Yet, a growing body of work suggests that ERG1 mediates physiology throughout the human body, including the brain. ERG1 is a regulator of neuronal excitability, ERG1 variants are associated with neuronal diseases (e.g., epilepsy and schizophrenia), and ERG1 serves as a potential therapeutic target for neuronal pathophysiology. This review summarizes the current state-of-the-field regarding the ERG1 channel structure and function, ERG1’s relationship to the mammalian brain and highlights key questions that have yet to be answered.

Fenestropathy of Voltage-Gated Sodium Channels

Voltage-gated sodium channels (Nav) are responsible for the initiation and propagation of action potentials in excitable cells. From pain to heartbeat, these integral membrane proteins are the ignition stations for every sensation and action in human bodies. They are large (>200 kDa, 24 transmembrane helices) multi-domain proteins that couple changes in membrane voltage to the gating cycle of the sodium-selective pore. Nav mutations lead to a multitude of diseases - including chronic pain, cardiac arrhythmia, muscle illnesses, and seizure disorders - and a wide variety of currently used therapeutics block Nav. Despite this, the mechanisms of action of Nav blocking drugs are only modestly understood at this time and many questions remain to be answered regarding their state- and voltage-dependence, as well as the role of the hydrophobic membrane access pathways, or fenestrations, in drug ingress or egress. Nav fenestrations, which are pathways that connect the plasma membrane to the central cavity in the pore domain, were discovered through functional studies more than 40 years ago and once thought to be simple pathways. A variety of recent genetic, structural, and pharmacological data, however, shows that these fenestrations are actually key functional regions of Nav that modulate drug binding, lipid binding, and influence gating behaviors. We discovered that some of the disease mutations that cause arrhythmias alter amino acid residues that line the fenestrations of Nav1.5. This indicates that fenestrations may play a critical role in channel's gating, and that individual genetic variation may also influence drug access through the fenestrations for resting/inactivated state block. In this review, we will discuss the channelopathies associated with these fenestrations, which we collectively name "Fenestropathy," and how changes in the fenestrations associated with the opening of the intracellular gate could modulate the state-dependent ingress and egress of drugs binding in the central cavity of voltage gated sodium channels.

Small molecule modulation of the Drosophila Slo channel elucidated by cryo-EM

Slowpoke (Slo) potassium channels display extraordinarily high conductance, are synergistically activated by a positive transmembrane potential and high intracellular Ca²⁺ concentrations and are important targets for insecticides and antiparasitic drugs. However, it is unknown how these compounds modulate ion translocation and whether there are insect-specific binding pockets. Here, we report structures of Drosophila Slo in the Ca²⁺-bound and Ca²⁺-free form and in complex with the fungal neurotoxin verruculogen and the anthelmintic drug emodepside. Whereas the architecture and gating mechanism of Slo channels are conserved, potential insect-specific binding pockets exist. Verruculogen inhibits K⁺ transport by blocking the Ca²⁺-induced activation signal and precludes K⁺ from entering the selectivity filter. Emodepside decreases the conductance by suboptimal K⁺ coordination and uncouples ion gating from Ca²⁺ and voltage sensing. Our results expand the mechanistic understanding of Slo regulation and lay the foundation for the rational design of regulators of Slo and other voltage-gated ion channels.

Slowpoke (Slo) channels are voltage-gated potassium channels that are activated by high intracellular Ca²+ concentrations, and they are targets for insecticides and antiparasitic drugs. Here, the authors present the cryo-EM structures of the Drosophila melanogaster Slo channel in the Ca²+-bound and Ca²+-free conformations, as well as in complex with the fungal neurotoxin verruculogen and the anthelmintic drug emodepside and discuss the mechanisms by which they affect the activity of Slo.

Inhibition of the hERG potassium channel by phenanthrene: a polycyclic aromatic hydrocarbon pollutant

The lipophilic polycyclic aromatic hydrocarbon (PAH) phenanthrene is relatively abundant in polluted air and water and can access and accumulate in human tissue. Phenanthrene has been reported to interact with cardiac ion channels in several fish species. This study was undertaken to investigate the ability of phenanthrene to interact with hERG (human Ether-à-go-go-Related Gene) encoded Kv11.1 K+ channels, which play a central role in human ventricular repolarization. Pharmacological inhibition of hERG can be proarrhythmic. Whole-cell patch clamp recordings of hERG current (IhERG) were made from HEK293 cells expressing wild-type (WT) and mutant hERG channels. WT IhERG1a was inhibited by phenanthrene with an IC50 of 17.6 ± 1.7 µM, whilst IhERG1a/1b exhibited an IC50 of 1.8 ± 0.3 µM. WT IhERG block showed marked voltage and time dependence, indicative of dependence of inhibition on channel gating. The inhibitory effect of phenanthrene was markedly impaired by the attenuated inactivation N588K mutation. Remarkably, mutations of S6 domain aromatic amino acids (Y652, F656) in the canonical drug binding site did not impair the inhibitory action of phenanthrene; the Y652A mutation augmented IhERG block. In contrast, the F557L (S5) and M651A (S6) mutations impaired the ability of phenanthrene to inhibit IhERG, as did the S624A mutation below the selectivity filter region. Computational docking using a cryo-EM derived hERG structure supported the mutagenesis data. Thus, phenanthrene acts as an inhibitor of the hERG K+ channel by directly interacting with the channel, binding to a distinct site in the channel pore domain.

Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information.

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints

The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

Drug-induced blockade of the human ether-à-go-go-related gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligand-based approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein–ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUC_MAX = 0.86 ± 0.01) and negative predictive values (NPV_MAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein–ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

Molecular determinants of pro-arrhythmia proclivity of d- and l-sotalol via a multi-scale modeling pipeline

Drug isomers may differ in their proarrhythmia risk. An interesting example is the drug sotalol, an antiarrhythmic drug comprising d- and l- enantiomers that both block the hERG cardiac potassium channel and confer differing degrees of proarrhythmic risk. We developed a multi-scale in silico pipeline focusing on hERG channel – drug interactions and used it to probe and predict the mechanisms of pro-arrhythmia risks of the two enantiomers of sotalol. Molecular dynamics (MD) simulations predicted comparable hERG channel binding affinities for d- and l-sotalol, which were validated with electrophysiology experiments. MD derived thermodynamic and kinetic parameters were used to build multi-scale functional computational models of cardiac electrophysiology at the cell and tissue scales. Functional models were used to predict inactivated state binding affinities to recapitulate electrocardiogram (ECG) QT interval prolongation observed in clinical data. Our study demonstrates how modeling and simulation can be applied to predict drug effects from the atom to the rhythm for dl-sotalol and also increased proarrhythmia proclivity of d- vs. l-sotalol when accounting for stereospecific beta-adrenergic receptor blocking.

Structural Insights into the Mechanisms and Pharmacology of K2P Potassium Channels

Leak currents, defined as voltage and time independent flows of ions across cell membranes, are central to cellular electrical excitability control. The K_2P (KCNK) potassium channel class comprises an ion channel family that produces potassium leak currents that oppose excitation and stabilize the resting membrane potential in cells in the brain, cardiovascular system, immune system, and sensory organs. Due to their widespread tissue distribution, K_2Ps contribute to many physiological and pathophysiological processes including anesthesia, pain, arrythmias, ischemia, hypertension, migraine, intraocular pressure regulation, and lung injury responses. Structural studies of six homomeric K_2Ps have established the basic architecture of this channel family, revealed key moving parts involved in K_2P function, uncovered the importance of asymmetric pinching and dilation motions in the K_2P selectivity filter (SF) C-type gate, and defined two K_2P structural classes based on the absence or presence of an intracellular gate. Further, a series of structures characterizing K_2P:modulator interactions have revealed a striking polysite pharmacology housed within a relatively modestly sized (~70 kDa) channel. Binding sites for small molecules or lipids that control channel function are found at every layer of the channel structure, starting from its extracellular side through the portion that interacts with the membrane bilayer inner leaflet. This framework provides the basis for understanding how gating cues sensed by different channel parts control function and how small molecules and lipids modulate K_2P activity. Such knowledge should catalyze development of new K_2P modulators to probe function and treat a wide range of disorders.