The scorpion toxin BeKm-1 blocks hERG cardiac potassium channels using an indispensable arginine residue.

BeKm-1 is a peptide toxin from scorpion venom that blocks the pore of the potassium channel hERG (Kv11.1) in the human heart. Although individual protein structures have been resolved, the structure of the complex between hERG and BeKm-1 is unknown. Here, we used molecular dynamics and ensemble docking, guided by previous double-mutant cycle analysis data, to obtain an in silico model of the hERG-BeKm-1 complex. Adding to the previous mutagenesis study of BeKm-1, our model uncovers the key role of residue Arg20, which forms three interactions (a salt bridge and hydrogen bonds) with the channel vestibule simultaneously. Replacement of this residue even by lysine weakens the interactions significantly. In accordance, the recombinantly produced BeKm-1R20K mutant exhibited dramatically decreased activity on hERG. Our model may be useful for future drug design attempts.

Computational Studies Show How the H463R Mutation Turns hKv1.5 into an Inactivation State.

The KCNA5 gene provides the code for the α-subunit of the potassium channel Kv1.5. The genetic variant H463R in the Kv1.5 channel has been reported to cause a functional loss in atrial fibrillation (AF) patients. Understanding the mutations at a molecular level is key to developing improved therapeutics concerning cardiac hKv1.5 and hKv1.4 channels. Molecular dynamics and umbrella sampling free energy simulations are an effective tool to understand the mutation's effect on ion conduction, which we have employed and found that the hKv1.5[H463R] mutation imposes an energy barrier on the ion conduction pathway compared to the wild-type channel's ion free energy and pore structure. These results imply that the arginine mutation associated with the AF disease in particular modulates the inactivation process of hKv1.5. Kv1.4, encoded by the KCNA4 gene, is also present in the heart. Therefore, we considered simulation studies of the equivalent H507R mutation in the hKv1.4 channel and found that the mutation slightly reduces the ion conduction barrier in the ion conduction pathway, making it insignificant.

Methionine-isoleucine dichotomy at a key position in scorpion toxins inhibiting voltage-gated potassium channels.

Previous studies have identified some key amino acid residues in scorpion toxins blocking potassium channels. In particular, the most numerous toxins belonging to the α-KTx family and affecting voltage-gated potassium channels (KV) present a conserved K-C-X-N motif in the C-terminal half of their sequence. Here, we show that the X position of this motif is almost always occupied by either methionine or isoleucine. We compare the activity of three pairs of peptides that differ just by this residue on a panel of KV1 channels and find that toxins bearing methionine affect preferentially KV1.1 and 1.6 isoforms. The refined K-C-M/I-N motif stands out as the principal structural element of α-KTx conferring high affinity and selectivity to KV channels.

Interaction of the Inhibitory Peptides ShK and HmK with the Voltage-Gated Potassium Channel KV1.3: Role of Conformational Dynamics.

Peptide toxins that adopt the ShK fold can inhibit the voltage-gated potassium channel KV1.3 with IC50 values in the pM range and are therefore potential leads for drugs targeting autoimmune and neuroinflammatory diseases. Nuclear magnetic resonance (NMR) relaxation measurements and pressure-dependent NMR have shown that, despite being cross-linked by disulfide bonds, ShK itself is flexible in solution. This flexibility affects the local structure around the pharmacophore for the KV1.3 channel blockade and, in particular, the relative orientation of the key Lys and Tyr side chains (Lys22 and Tyr23 in ShK) and has implications for the design of KV1.3 inhibitors. In this study, we have performed molecular dynamics (MD) simulations on ShK and a close homologue, HmK, to probe the conformational space occupied by the Lys and Tyr residues, and docked the different conformations with a recently determined cryo-EM structure of the KV1.3 channel. Although ShK and HmK have 60% sequence identity, their dynamic behaviors are quite different, with ShK sampling a broad range of conformations over the course of a 5 µs MD simulation, while HmK is relatively rigid. We also investigated the importance of conformational dynamics, in particular the distance between the side chains of the key dyad Lys22 and Tyr23, for binding to KV1.3. Although these peptides have quite different dynamics, the dyad in both adopts a similar configuration upon binding, revealing a conformational selection upon binding to KV1.3 in the case of ShK. Both peptides bind to KV1.3 with Lys22 occupying the pore of the channel. Intriguingly, the more flexible peptide, ShK, binds with significantly higher affinity than HmK.

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.

AgTx2-GFP, Fluorescent Blocker Targeting Pharmacologically Important Kv1.x (x = 1, 3, 6) Channels.

The growing interest in potassium channels as pharmacological targets has stimulated the development of their fluorescent ligands (including genetically encoded peptide toxins fused with fluorescent proteins) for analytical and imaging applications. We report on the properties of agitoxin 2 C-terminally fused with enhanced GFP (AgTx2-GFP) as one of the most active genetically encoded fluorescent ligands of potassium voltage-gated Kv1.x (x = 1, 3, 6) channels. AgTx2-GFP possesses subnanomolar affinities for hybrid KcsA-Kv1.x (x = 3, 6) channels and a low nanomolar affinity to KcsA-Kv1.1 with moderate dependence on pH in the 7.0-8.0 range. Electrophysiological studies on oocytes showed a pore-blocking activity of AgTx2-GFP at low nanomolar concentrations for Kv1.x (x = 1, 3, 6) channels and at micromolar concentrations for Kv1.2. AgTx2-GFP bound to Kv1.3 at the membranes of mammalian cells with a dissociation constant of 3.4 ± 0.8 nM, providing fluorescent imaging of the channel membranous distribution, and this binding depended weakly on the channel state (open or closed). AgTx2-GFP can be used in combination with hybrid KcsA-Kv1.x (x = 1, 3, 6) channels on the membranes of E. coli spheroplasts or with Kv1.3 channels on the membranes of mammalian cells for the search and study of nonlabeled peptide pore blockers, including measurement of their affinity.

Characterization and Chemical Synthesis of Cm39 (α-KTx 4.8): A Scorpion Toxin That Inhibits Voltage-Gated K+ Channel KV1.2 and Small- and Intermediate-Conductance Ca2+-Activated K+ Channels KCa2.2 and KCa3.1.

A novel peptide, Cm39, was identified in the venom of the scorpion Centruroides margaritatus. Its primary structure was determined. It consists of 37 amino acid residues with a MW of 3980.2 Da. The full chemical synthesis and proper folding of Cm39 was obtained. Based on amino acid sequence alignment with different K+ channel inhibitor scorpion toxin (KTx) families and phylogenetic analysis, Cm39 belongs to the α-KTx 4 family and was registered with the systematic number of α-KTx 4.8. Synthetic Cm39 inhibits the voltage-gated K+ channel hKV1.2 with high affinity (Kd = 65 nM). The conductance-voltage relationship of KV1.2 was not altered in the presence of Cm39, and the analysis of the toxin binding kinetics was consistent with a bimolecular interaction between the peptide and the channel; therefore, the pore blocking mechanism is proposed for the toxin-channel interaction. Cm39 also inhibits the Ca2+-activated KCa2.2 and KCa3.1 channels, with Kd = 502 nM, and Kd = 58 nM, respectively. However, the peptide does not inhibit hKV1.1, hKV1.3, hKV1.4, hKV1.5, hKV1.6, hKV11.1, mKCa1.1 K+ channels or the hNaV1.5 and hNaV1.4 Na+ channels at 1 µM concentrations. Understanding the unusual selectivity profile of Cm39 motivates further experiments to reveal novel interactions with the vestibule of toxin-sensitive channels.

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.

Insight on the interaction between the scorpion toxin blocker Discrepin on potassium voltage-gated channel Kv4.3 by molecular dynamics simulations.

Discrepin is a 38-residue α-toxin extracted from the venom of the Venezuelan scorpion Tityus discrepans, which inhibits ionic transit in the voltage-dependent potassium channels (Kv) of A-type current. The effect of specific residues on the IC50 between Discrepine and Kv4.3, the main component of A-type currents, is known; however, the molecular details of the toxin-channel interaction are not known. In this work, we present interaction models between Discrepin (wt) and two peptide variants (V6K/D20K and K13A) on the pore-forming domain of the Kv4.3 channel obtained from homology, docking, and molecular dynamics modeling techniques. The free energy calculations in these models correspond to the order of the experimentally determined IC50 values. Our studies shed light on the role of the K13 residue as responsible for occluding the Kv4.3 selectivity filter and the importance of the V6K mutation in the approach and stabilization of toxin-channel complex interactions.Communicated by Ramaswamy H. Sarma.

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.

Combining mKate2-Kv1.3 Channel and Atto488-Hongotoxin for the Studies of Peptide Pore Blockers on Living Eukaryotic Cells.

The voltage-gated potassium Kv1.3 channel is an essential component of vital cellular processes which is also involved in the pathogenesis of some autoimmune, neuroinflammatory and oncological diseases. Pore blockers of the Kv1.3 channel are considered as potential drugs and are used to study Kv1 channels' structure and functions. Screening and study of the blockers require the assessment of their ability to bind the channel. Expanding the variety of methods used for this, we report on the development of the fluorescent competitive binding assay for measuring affinities of pore blockers to Kv1.3 at the membrane of mammalian cells. The assay constituents are hongotoxin 1 conjugated with Atto488, fluorescent mKate2-tagged Kv1.3 channel, which was designed to improve membrane expression of the channel in mammalian cells, confocal microscopy, and a special protocol of image processing. The assay is implemented in the "mix and measure", format and allows the screening of Kv1.3 blockers, such as peptide toxins, that bind to the extracellular vestibule of the K+-conducting pore, and analyzing their affinity.

A Fluorescent Peptide Toxin for Selective Visualization of the Voltage-Gated Potassium Channel KV1.3.

Upregulation of the voltage-gated potassium channel KV1.3 is implicated in a range of autoimmune and neuroinflammatory diseases, including rheumatoid arthritis, psoriasis, multiple sclerosis, and type I diabetes. Understanding the expression, localization, and trafficking of KV1.3 in normal and disease states is key to developing targeted immunomodulatory therapies. HsTX1[R14A], an analogue of a 34-residue peptide toxin from the scorpion Heterometrus spinifer, binds KV1.3 with high affinity (IC50 of 45 pM) and selectivity (2000-fold for KV1.3 over KV1.1). We have synthesized a fluorescent analogue of HsTX1[R14A] by N-terminal conjugation of a Cy5 tag. Electrophysiology assays show that Cy5-HsTX1[R14A] retains activity against KV1.3 (IC50 ∼ 0.9 nM) and selectivity over a range of other potassium channels (KV1.2, KV1.4, KV1.5, KV1.6, KCa1.1 and KCa3.1), as well as selectivity against heteromeric channels assembled from KV1.3/KV1.5 tandem dimers. Live imaging of CHO cells expressing green fluorescent protein-tagged KV1.3 shows co-localization of Cy5-HsTX1[R14A] and KV1.3 fluorescence signals at the cell membrane. Moreover, flow cytometry demonstrated that Cy5-HsTX1[R14A] can detect KV1.3-expressing CHO cells. Stimulation of mouse microglia by lipopolysaccharide, which enhances membrane expression of KV1.3, was associated with increased staining by Cy5-HsTX1[R14A], demonstrating that it can be used to identify KV1.3 in disease-relevant models of inflammation. Furthermore, the biodistribution of Cy5-HsTX1[R14A] could be monitored using ex vivo fluorescence imaging of organs in mice dosed subcutaneously with the peptide. These results illustrate the utility of Cy5-HsTX1[R14A] as a tool for visualizing KV1.3, with broad applicability in fundamental investigations of KV1.3 biology, and the validation of novel disease indications where KV1.3 inhibition may be of therapeutic value.

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.

Artificial pore blocker acts specifically on voltage-gated potassium channel isoform KV1.6.

Among voltage-gated potassium channel (KV) isoforms, KV1.6 is one of the most widespread in the nervous system. However, there are little data concerning its physiological significance, in part due to the scarcity of specific ligands. The known high-affinity ligands of KV1.6 lack selectivity, and conversely, its selective ligands show low affinity. Here, we present a designer peptide with both high affinity and selectivity to KV1.6. Previously, we have demonstrated that KV isoform-selective peptides can be constructed based on the simplistic α-hairpinin scaffold, and we obtained a number of artificial Tk-hefu peptides showing selective blockage of KV1.3 in the submicromolar range. We have now proposed amino acid substitutions to enhance their activity. As a result, we have been able to produce Tk-hefu-11 that shows an EC50 of ≈70 nM against KV1.3. Quite surprisingly, Tk-hefu-11 turns out to block KV1.6 with even higher potency, presenting an EC50 of ≈10 nM. Furthermore, we have solved the peptide structure and used molecular dynamics to investigate the determinants of selective interactions between artificial α-hairpinins and KV channels to explain the dramatic increase in KV1.6 affinity. Since KV1.3 is not highly expressed in the nervous system, we hope that Tk-hefu-11 will be useful in studies of KV1.6 and its functions.

Retroviral glycoprotein-mediated immune suppression via the potassium channel KCa3.1 - A new strategy for amelioration of inflammatory bowel diseases.

Peptides derived from retroviral envelope proteins have been shown to possess a wide range of immunosuppressive and anti-inflammatory activities. We have previously reported identification of such a peptide derived from the envelope protein coded by a human endogenous retrovirus (HERV). In this study, we identify that in vitro the peptide inhibits the KCa3.1 potassium channel, a potential target for therapy of immune diseases. We describe in vitro ENV59-GP3 effects with respect to potency of inhibition on KCa3.1 channels and calcium influx. Furthermore, we asses in vivo the effect of blocking KCa3.1 with ENV59-GP3 peptide or KCa3.1-blocker NS6180 on protection against DSS-induced acute colitis. ENV59-GP3 peptide treatment showed reduction of the disease score in the DSS-induced acute colitis mice model, which was comparable to effects of the KCa3.1 channel blocker NS6180. Analysis of cytokine production from DSS-mice model treated animals revealed equipotent inhibitory effects of the ENV59-GP3 and NS6180 compounds on the production of IL-6, TNF-α, IL-1ß. These findings altogether suggest that ENV59-GP3 functions as a KCa3.1 channel inhibitor and underline the implications of using virus derived channel blockers for treatment of autoimmune diseases. Additionally, they open the possibilities whether KCa3.1 inhibition is efficacious in patients with inflammatory bowel diseases.

Cm28, a scorpion toxin having a unique primary structure, inhibits KV1.2 and KV1.3 with high affinity.

The Cm28 in the venom of Centruroides margaritatus is a short peptide consisting of 27 amino acid residues with a mol wt of 2,820 D. Cm28 has <40% similarity with other known α-KTx from scorpions and lacks the typical functional dyad (lysine-tyrosine) required to block KV channels. However, its unique sequence contains the three disulfide-bond traits of the α-KTx scorpion toxin family. We propose that Cm28 is the first example of a new subfamily of α-KTxs, registered with the systematic number α-KTx32.1. Cm28 inhibited voltage-gated K+ channels KV1.2 and KV1.3 with Kd values of 0.96 and 1.3 nM, respectively. There was no significant shift in the conductance-voltage (G-V) relationship for any of the channels in the presence of toxin. Toxin binding kinetics showed that the association and dissociation rates are consistent with a bimolecular interaction between the peptide and the channel. Based on these, we conclude that Cm28 is not a gating modifier but rather a pore blocker. In a selectivity assay, Cm28 at 150 nM concentration (>100× Kd value for KV1.3) did not inhibit KV1.5, KV11.1, KCa1.1, and KCa3.1 K+ channels; NaV1.5 and NaV1.4 Na+ channels; or the hHV1 H+ channel but blocked ∼27% of the KV1.1 current. In a biological functional assay, Cm28 strongly inhibited the expression of the activation markers interleukin-2 receptor and CD40 ligand in anti-CD3-activated human CD4+ effector memory T lymphocytes. Cm28, due to its unique structure, may serve as a template for the generation of novel peptides targeting KV1.3 in autoimmune diseases.

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).

Mechanisms Underlying the Inhibition of KV1.3 Channel by Scorpion Toxin ImKTX58.

Voltage-gated KV1.3 channel has been reported to be a drug target for the treatment of autoimmune diseases, and specific inhibitors of Kv1.3 are potential therapeutic drugs for multiple diseases. The scorpions could produce various bioactive peptides that could inhibit KV1.3 channel. Here, we identified a new scorpion toxin polypeptide gene ImKTX58 from the venom gland cDNA library of the Chinese scorpion Isometrus maculatus Sequence alignment revealed high similarities between ImKTX58 mature peptide and previously reported KV1.3 channel blockers-LmKTX10 and ImKTX88-suggesting that ImKTX58 peptide might also be a KV1.3 channel blocker. By using electrophysiological recordings, we showed that recombinant ImKTX58 prepared by genetic engineering technologies had a highly selective inhibiting effect on KV1.3 channel. Further alanine scanning mutagenesis and computer simulation identified four amino acid residues in ImKTX58 peptide as key binding sites to KV1.3 channel by forming hydrogen bonds, salt bonds, and hydrophobic interactions. Among these four residues, 28th lysine of the ImKTX58 mature peptide was found to be the most critical amino acid residue for blocking KV1.3 channel. SIGNIFICANCE STATEMENT: In this study, we discovered a scorpion toxin gene ImKTX58 that has not been reported before in Hainan Isometrus maculatus and successfully used the prokaryotic expression system to express and purify the polypeptides encoded by this gene. Electrophysiological experiments on ImKTX58 showed that ImKTX58 has a highly selective blocking effect on KV1.3 channel over Kv1.1, Kv1.2, Kv1.5, SK2, SK3, and BK channels. These findings provide a theoretical basis for designing highly effective KV1.3 blockers to treat autoimmune and other diseases.

HergSPred: Accurate Classification of hERG Blockers/Nonblockers with Machine-Learning Models.

The human ether-à-go-go-related gene (hERG) K+ channel plays an important role in cardiac action potentials. The inhibition of the hERG channel may lead to long QT syndrome (LQTS) and even sudden cardiac death. Due to severe hERG-related cardiotoxicity, many drugs have been withdrawn from the market. Therefore, it is necessary to estimate the chemical blockade of hERG in the early stage of drug discovery. In this study, we collected 12,850 compounds with hERG inhibition data from the literature and trained a series of hERG blocking classification models based on the MACCS and Morgan fingerprints. A consensus model named HergSPred was generated based on the individual models using voting principles. The accuracy of HergSPred is higher than previous models using identical training and test sets. Moreover, we analyzed the contribution of each input fingerprint to the prediction output to obtain intuitive chemical insights into the hERG inhibition, which allows visualization of warning substructures that may cause cardiotoxicity in the input compound. The model is available at http://www.icdrug.com/ICDrug/T.