Biology subjects

Yarov-Yarovoy, V.

Publications and source records attributed to Yarov-Yarovoy, V..

At least 19 recordsLinked to original sources

bioRxiv2026

A hERG Blocker Facilitates K+ Channel Current by Agonizing Pore Opening while Blocking

Many drugs that block voltage-gated K+ channels encoded by the human Ether-a-go-go-Related Gene (hERG) can cause long QT syndrome and life-threatening cardiac arrhythmias, yet the molecular mechanisms that determine this risk remain unclear. A process that may counteract arrhythmogenic hERG block, termed facilitation, is common to many clinically-approved hERG blockers including nifekalant, amiodarone, promethazine, imipramine, nortriptyline, haloperidol, verapamil, carvedilol, metoprolol, propranolol, quinidine, fluoxetine, and chlorpheniramine. Facilitation is an increase in hERG current, under certain conditions, due to these blockers. Here, we propose that an agonism-while-blocking mechanism undergirds facilitation. We focus on nifekalant, a Class III antiarrhythmic drug and exemplar hERG blocker that induces facilitation. We tested the hypothesis that nifekalant opens hERG channel gates while blocking, and that unblock of these open-yet-blocked channels results in supranormal hERG current. We develop rate-theory kinetic models to identify features of agonism-while-blocking that produce facilitation. We generate atomistic models that predict that nifekalant blocks the hERG conduction path while modulating the intracellular conduction gate. Voltage-clamp measurements reveal that agonism-while-blocking undergirds nifekalant block and facilitation. We speculate that this agonism-while-blocking mechanism contributes to the relative safety of hERG blockers that induce facilitation.

pharmacology and toxicology↗

bioRxiv2025

Prediction of TdP Arrhythmia Risk Through Molecular Simulations of Conformation-specific Drug Interactions with the hERG K+, NaV1.5, and CaV1.2 Channels

Unintended block of cardiac ion channels, particularly hERG (KV11.1), remains a key concern in drug development as disruption of ion channel function can lead to deadly arrhythmia. To assess proarrhythmic risk, we investigated how drugs interact with hERG in its open and inactivated states and whether drug interactions with other cardiac channels like NaV1.5 and CaV1.2 mitigate that risk. Using cryo-EM structures, we modeled open and inactivated conformations of these channels with Rosetta and AlphaFold. We then applied Site Identification by Ligand Competitive Saturation (SILCS), a physics-based pre-computed ensemble docking method, to predict drug binding affinities. SILCS leverages molecular simulation-generated free energy maps for high-throughput docking against hydrated lipid bilayer-embedded ion channel models. Bayesian machine learning was used to refine SILCS scoring using experimental IC50 values from 69 known hERG blockers outperforming Schrodinger Glide, AutoDock Vina, and OpenEye FRED drug docking predictions. Computed drug binding affinities for hERG and CaV1.2 channels were used to train machine learning models that successfully classified around 300 drugs from the CredibleMeds database. Cationic nitrogen SILCS fragment free energy scores were found to be top physical properties that are predictive of drug-induced Torsades de Pointes (TdP) arrhythmia risk. This approach, which relies on the predicted binding free energies and predicted physical properties of drugs rather than the chemical structure of the drugs themselves as features could be extended to facilitate the design of new drugs where rapid assessment of arrhythmia risk can be performed prior to experimental testing.

biophysics↗

bioRxiv2025

Inhibition of CaV1.4 channels by CaV3 channel antagonists ML218 and Z944

Among the three classes of voltage-gated Ca2+ channels (Cav1, Cav2, Cav3), Cav3 T-type channels are drug targets for disorders including epilepsy and pain. Antagonists such as Z944 and ML218 are highly selective for Cav3 compared to the Cav1.2 L-type channel but whether they have additional activity on other Cav1 subtypes is unknown. Here, we investigated the effects of Z944 and ML218 on the Cav1.4 channel which regulates neurotransmitter release from retinal photoreceptors. In HEK293T cells transfected with Cav1.4 and the auxiliary {beta}2x13 and 2{delta}-4 subunits, Z944 and ML218 inhibited Ca2+ currents with IC50 values of [~]30 {micro}M and 2 {micro}M, respectively. Structure-based modeling combined with functional studies revealed the importance of a cluster of methionine residues, particularly M1004, within the DHP binding site for the effects of ML218. Compared to mutation of a conserved threonine (T1007) that is required for DHP sensitivity of Cav1 channels, mutation of M1004 had a 10-fold greater impact in diminishing the potency of ML218. Cav1.2 was significantly less sensitive to ML218 inhibition (IC50[~] 37 {micro}M) than Cav1.4, which could not be attributed to a valine in place of M1004 in Cav1.2. We conclude that ML218 and Z944 are dual Cav1/Cav3 modulators of CaV1.4 and should be used with caution when dissecting the contributions of CaV3 channels in tissues where Cav1.4 is expressed.

biophysics↗

bioRxiv2024

Exploring voltage-gated sodium channel conformations and protein-protein interactions using AlphaFold2

Voltage-gated sodium (NaV) channels are vital regulators of electrical activity in excitable cells. Given their importance in physiology, NaV channels are key therapeutic targets for treating numerous conditions, yet developing subtype-selective drugs remains challenging due to the high sequence and structural conservation among NaV subtypes. Recent advances in cryo-electron microscopy have resolved most human NaV channels, providing valuable insights into their structure and function. However, limitations persist in fully capturing the complex conformational states that underlie NaV channel gating and modulation. This study explores the capability of AlphaFold2 to sample multiple NaV channel conformations and assess AlphaFold Multimers accuracy in modeling interactions between the NaV -subunit and its protein partners, including auxiliary {beta}-subunits and calmodulin. We enhance conformational sampling to explore NaV channel conformations using a subsampled multiple sequence alignment approach and varying the number of recycles. Our results demonstrate that AlphaFold2 models multiple NaV channel conformations, including those observed in experimental structures, states that have not been described experimentally, and potential intermediate states. Correlation and clustering analyses uncover coordinated domain behavior and recurrent state ensembles. Furthermore, AlphaFold Multimer models NaV complexes with auxiliary {beta}-subunits and calmodulin with high accuracy, and the presence of protein partners significantly alters both the modeled conformational landscape of the NaV -subunit and the coupling between its functional states. These findings highlight the potential of deep learning-based methods to expand our understanding of NaV channel structure, gating, and modulation, while also underscoring the limitations of predicted models that remain hypotheses until validated by experimental data. SummaryLopez-Mateos et al.s study demonstrates AlphaFold2s potential to sample multiple states of human NaV channels. Additionally, NaV -subunit interactions with {beta}-subunits and calmodulin reshape NaV -subunit conformational landscape. This study reveals potential of deep learning methods to model structural diversity of ion channels.

biophysics↗

bioRxiv2024

14-3-3 promotes sarcolemmal expression of cardiac CaV1.2 and nucleates isoproterenol-triggered channel super-clustering

The L-type Ca2+ channel (CaV1.2) is essential for cardiac excitation-contraction coupling. To contribute to the inward Ca2+ flux that drives Ca2+-induced-Ca2+-release, CaV1.2 channels must be expressed on the sarcolemma; thus the regulatory mechanisms that tune CaV1.2 expression to meet contractile demand are an emerging area of research. A ubiquitously expressed protein called 14-3-3 has been proposed to affect Ca2+ channel trafficking in non-myocytes, however whether 14-3-3 has similar effects on CaV1.2 in cardiomyocytes is unknown. 14-3-3 preferentially binds phospho-serine/threonine residues to affect many cellular processes and is known to regulate cardiac ion channels including NaV1.5 and hERG. Altered 14-3-3 expression and function have been implicated in cardiac pathologies including hypertrophy. Accordingly, we tested the hypothesis that 14-3-3 interacts with CaV1.2 in a phosphorylation-dependent manner and regulates cardiac CaV1.2 trafficking and recycling. Confocal imaging, proximity ligation assays, super-resolution imaging, and co-immunoprecipitation revealed a population of 14-3-3 colocalized and closely associated with CaV1.2. The degree of 14-3-3/CaV1.2 colocalization increased upon stimulation of {beta}-adrenergic receptors with isoproterenol. Notably, only the 14-3-3-associated CaV1.2 population displayed increased cluster size with isoproterenol, revealing a role for 14-3-3 as a nucleation factor that directs CaV1.2 super-clustering. 14-3-3 overexpression increased basal CaV1.2 cluster size and Ca2+ currents in ventricular myocytes, with maintained channel responsivity to isoproterenol. In contrast, isoproterenol-stimulated augmentation of sarcolemmal CaV1.2 expression and currents in ventricular myocytes were abrogated by 14-3-3 inhibition. These data support a model where 14-3-3 interacts with CaV1.2 in a phosphorylation-dependent manner to promote enhanced trafficking/recycling, clustering, and activity during {beta}-adrenergic stimulation. Significance StatementThe L-type Ca2+ channel, CaV1.2, plays an essential role in excitation-contraction coupling in the heart and in part regulates the overall strength of contraction during basal and fight- or-flight {beta}-adrenergic signaling conditions. Proteins that modulate the trafficking and/or activity of CaV1.2 are interesting both from a physiological and pathological perspective, since alterations in CaV1.2 can impact action potential duration and cause arrythmias. A small protein called 14-3-3 regulates other ion channels in the heart and other Ca2+ channels, but how it may interact with CaV1.2 in the heart has never been studied. Examining factors that affect CaV1.2 at rest and during {beta}-adrenergic stimulation is crucial for our ability to understand and treat disease and aging conditions where these pathways are altered.

physiology↗

bioRxiv2024

Towards high-resolution modeling of small molecule-ion channel interactions

Ion channels are critical drug discovery targets for a wider range of pathologies, such as epilepsy, chronic pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two Rosetta ligand docking methods, RosettaLigand and GALigandDock, on structures of known ligand - cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods were able to frequently sample a ligand binding pose within 1-2 [A] root mean square deviation (RMSD) relative to the native ligand - channel structure. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify native drug binding poses as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand - ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state specific interactions of the ion channel and inherent receptor site flexibility that could influence ligand binding.

biophysics↗

bioRxiv2024

An artificial intelligence accelerated virtual screening platform for drug discovery

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we developed a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screened multi-billion compound libraries against two unrelated targets, a novel ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. On both targets, we discover hits, including seven novel hits (14% hit rate) to KLHDC2 and four novel hits (44% hit rate) to NaV1.7 with single digit micromolar binding affinities. Screening in both cases was completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

biochemistry↗

bioRxiv2024

Harnessing AlphaFold to reveal state secrets: Prediction of hERG closed and inactivated states

To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been resolution of discrete conformational states of transmembrane ion channel proteins. An example is KV11.1 (hERG), comprising the primary cardiac repolarizing current, Ikr. hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. While prior studies have applied AlphaFold to predict alternative protein conformations, we show that the inclusion of carefully chosen structural templates can guide these predictions toward distinct functional states. This targeted modeling approach is validated through comparisons with experimental data, including proposed state-dependent structural features, drug interactions from molecular docking, and ion conduction properties from molecular dynamics simulations. Remarkably, AlphaFold not only predicts inactivation mechanisms of the hERG channel that prevent ion conduction but also uncovers novel molecular features explaining enhanced drug binding observed during inactivation, offering a deeper understanding of hERG channel function and pharmacology. Furthermore, leveraging AlphaFold-derived states enhances computational screening by significantly improving agreement with experimental drug affinities, an important advance for hERG as a key drug safety target where traditional single-state models miss critical state-dependent effects. By mapping protein residue interaction networks across closed, open, and inactivated states, we identified critical residues driving state transitions validated by prior mutagenesis studies. This innovative methodology sets a new benchmark for integrating deep learning-based protein structure prediction with experimental validation. It also offers a broadly applicable approach using AlphaFold to predict discrete protein conformations, reconcile disparate data, and uncover novel structure-function relationships, ultimately advancing drug safety screening and enabling the design of safer therapeutics.

biophysics↗

bioRxiv2023

Modeling stereospecific drug interactions with beta-adrenergic receptors

Beta adrenergic receptors ({beta}ARs) are G protein-coupled receptors that control processes as varied as heart rhythm and vascular tone by binding agonists such as norepinephrine to induce downstream signaling pathways. Beta blockers antagonize {beta}ARs to downregulate their activity, thus reducing heart rate and lowering vascular tone. We developed new Rosetta structural modeling protocol to develop state-specific models of {beta}1AR, expressed in cardiac myocytes, as well as {beta}2AR, expressed in the smooth muscle cells of vasculature and other tissues, and their atomistic-scale interactions with beta-blockers using RosettaLigand. We identified structural features of drug - receptor interactions, which may account for their receptor conformational state and drug stereospecific preferences. Furthermore, we estimated structural stabilities of our models using atomistic molecular dynamics (MD) simulations. In our recent study we validated our structural models of norepinephrine-bound {beta}2AR and its complex with stimulatory G protein via multi-microsecond MD simulations. Thus, here we mostly focused on state-dependent and stereospecific {beta}1AR interactions with beta-blocking drugs sotalol and propranolol. We observed expected inactive receptor state preferences and structural stabilities of our models in MD simulations, but neither those simulations nor RosettaLigand docking could clearly distinguish stereospecific preferences of those drugs. This warrants consideration of alternative hypotheses and enhanced sampling MD simulations, which we discussed as well. Nevertheless, our study provides basis for understanding conformational state selectivity and stereospecificity of beta-blockers for {beta}ARs, important pharmacological targets, and may be extended to other drug classes and receptor types. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/560334v1_fig0.gif" ALT="Figure 0"> View larger version (82K): org.highwire.dtl.DTLVardef@e8a838org.highwire.dtl.DTLVardef@7c2743org.highwire.dtl.DTLVardef@f5ac43org.highwire.dtl.DTLVardef@100a3d3_HPS_FORMAT_FIGEXP M_FIG Norepinephrine (NE) bound active-state beta-1 adrenergic receptor (1AR) in complex with the stimulatory G protein (Gs) heterotrimer embedded in a lipid bilayer. When expressed at the plasma membrane, the 1AR is oriented such that the ligand binding pocket (*) is accessible to ligands from the extracellular side (Ex.) of the membrane. The Gs (red), G (blue), and G{gamma} (yellow) subunits comprise the Gs heterotrimer. Nucleotides GDP or GTP bind G at the P-loop (**). Inset: Representative image of NE bound within the orthosteric ligand binding pocket. C_FIG

molecular biology↗

bioRxiv2023

Elucidating Molecular Mechanisms of Protoxin-2 State-specific Binding to the Human NaV1.7 Channel

Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-2 (PTx2) has high selectivity for hNaV1.7 and serves as a valuable scaffold to design novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2s residues R22, K26, K27, K28, and W30 with VSD as well as the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain. SummaryNaV1.7, a voltage-gated sodium channel, plays a crucial role in pain perception and is specifically targeted by PTx2, which serves as a template for designing pain therapeutics. In this study, Ngo et al. employed computational modeling to evaluate the state-dependent binding of PTx2 to NaV1.7.

biophysics↗

bioRxiv2023

TRPV1 Opening is Stabilized Equally by Its Four Subunits

Capsaicin receptor TRPV1 is a nociceptor for vanilloid molecules such as capsaicin and resiniferatoxin (RTX). Even though cryo-EM structures of TRPV1 in complex with these molecules are available, how their binding energetically favors the open conformation is not known. Here we report an approach to control the number of bound RTX molecules (0-to-4) in functional mouse TRPV1. The approach allowed direct measurements of each of the intermediate open states under equilibrium conditions at both macroscopic and single-molecule levels. We found that RTX binding to each of the four subunits contributes virtually the same activation energy, which we estimated to be 1.86 kcal/mol and found to arise predominately from destabilizing the closed conformation. We further showed that sequential bindings of RTX increase open probability without altering single-channel conductance, confirming that there is likely a single open-pore conformation for TRPV1 activated by RTX.

biophysics↗

bioRxiv2022

Computational design of peptides to target Nav1.7 channel with high potency and selectivity for the treatment of pain

The voltage-gated sodium NaV1.7 channel plays a key role as a mediator of action potential propagation in C-fiber nociceptors and is an established molecular target for pain therapy. ProTx-II is a potent and moderately selective peptide toxin from tarantula venom that inhibits human NaV1.7 activation. Here we used available structural and experimental data to guide Rosetta design of potent and selective ProTx-II-based peptide inhibitors of human NaV1.7 channels. Functional testing of designed peptides using electrophysiology identified the PTx2-3127 and PTx2-3258 peptides with IC50s of 7 nM and 4 nM for hNaV1.7 and more than 1,000-fold selectivity over human NaV1.1, NaV1.3, NaV1.4, NaV1.5, NaV1.8, and NaV1.9 channels. PTx2-3127 inhibits NaV1.7 currents in mouse and human sensory neurons and shows efficacy in rat models of chronic and thermal pain when administered intrathecally. Rationally-designed peptide inhibitors of human NaV1.7 channels have transformative potential to define a new class of biologics to treat pain.

biophysics↗

bioRxiv2022

Structural Modeling of Peptide Toxin - Ion Channel Interactions using RosettaDock

SUMMARYVoltage-gated ion channels play essential physiological roles in action potential generation and propagation. Peptidic toxins from animal venoms target ion channels and provide useful scaffolds for the rational design of novel channel modulators with enhanced potency and subtype selectivity. Despite recent progress in obtaining experimental structures of peptide toxin - ion channel complexes, structural determination of peptide toxins bound to ion channels in physiologically important states remains challenging. Here we describe an application of RosettaDock approach to structural modeling of peptide toxins interactions with ion channels. We tested this approach on 10 structures of peptide toxinion channel complexes and demonstrated that it can sample near-native structures in all tested cases. Our approach will be useful for improving understanding of the molecular mechanism of natural peptide toxin modulation of ion channel gating and for the structural modeling of novel peptide-based ion channel modulators.

biophysics↗

bioRxiv2022

Two mosquito odorant receptors with reciprocal specificity mediated by a single amino acid residue

The southern house mosquito, Culex quinquefasciatus, utilizes two odorant receptors, CquiOR10 and CquiOR2, narrowly tuned to oviposition attractants and well conserved among mosquito species. They detect skatole and indole, respectively, with reciprocal specificity. We swapped the seven transmembrane (TM) domains of CquiOR10 and CquiOR2 and identified TM2 as a specificity determinant. With additional mutations, we showed that CquiOR10A73L behaved like CquiOR2. Conversely, CquiOR2L74A recapitulated CquiOR10 specificity. Next, we generated structural models of CquiOR10 and CquiOR10A73L using RoseTTAFold and AlphaFold and docked skatole and indole using RosettaLigand. These modeling studies suggested space-filling constraints around A73. Consistent with this hypothesis, CquiOR10 mutants with a bulkier residue (Ile, Val) were insensitive to skatole and indole, whereas CquiOR10A73G retained the specificity to skatole and showed a more robust response than the wildtype receptor CquiOR10. On the other hand, Leu to Gly mutation of the indole receptor CquiOR2 reverted the specificity to skatole. Lastly, CquiOR10A73L, CquiOR2, and CquiOR2L74I were insensitive to 3-ethylindole, whereas CquiOR2L74A and CquiOR2L74G gained activity. Additionally, CquiOR10A73G gave more robust responses to 3-ethylindole than CquiOR10. Thus, we suggest the specificity of these receptors is mediated by a single amino acid substitution, leading to finely tuned volumetric space to accommodate specific oviposition attractants.

neuroscience↗

bioRxiv2021

Structural modeling of the hERG potassium channel and associated drug interactions

The voltage-gated potassium channel, KV11.1, encoded by the human Ether-a-go-go- Related Gene (hERG) is expressed in cardiac myocytes, where it is crucial for the membrane repolarization of the action potential. Gating of 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 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 F627 side chain in the selectivity filter into the central channel axis along the ion conduction pathway and 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 correlate well with existing experimental evidence involving interactions of these drugs with key hERG residues Y652 and F656 inside the pore and reveal potential ligand binding interactions within fenestration region in an inactivated state. Significance StatementComputational models of hERG potassium channel provide structural insights into an inactivated state and associated drug interactions. Our computational approach will be useful to study ion channel modulation by small molecules.

biophysics↗

bioRxiv2020

An unorthodox mechanism underlying voltage sensitivity of TRPV1 ion channel

While the capsaicin receptor TRPV1 channel is a polymodal nociceptor for heat, capsaicin, and proton, the channels responses to each of these stimuli are profoundly regulated by membrane potential, damping or even prohibiting its response at negative voltages and amplifying its response at positive voltages. Though voltage sensitivity plays an important role is shaping pain responses, how voltage regulates TRPV1 activation remains unknown. Here we showed that the voltage sensitivity of TRPV1 does not originate from the S4 segment like classic voltage-gated ion channels; instead, outer pore acidic residues directly partake in voltage-sensitive activation, with their negative charges collectively constituting the observed gating charges. Voltage-sensitive outer pore movement is titratable by extracellular pH and is allosterically coupled to channel activation, likely by influencing the upper gate in the ion selectivity filter. Elucidating this unorthodox voltage-gating process provides a mechanistic foundation for understanding polymodal gating and opens the door to novel approaches regulating channel activity for pain managements.

biophysics↗

bioRxiv2019

Alpha-Actinin-1 promotes activity of the L-type Ca2+ Channel CaV1.2

The L-type Ca2+ channel CaV1.2 governs gene expression, cardiac contraction, and neuronal activity. Binding of -actinin to the IQ motif of CaV1.2 supports its surface localization and postsynaptic targeting in neurons. We report a bi-functional mechanism that restricts CaV1.2 activity to its target sites. We solved separate NMR structures of the IQ motif (residues 1646-1664) bound to -actinin-1 and to apo-calmodulin (apoCaM). The CaV1.2 K1647A and Y1649A mutations, which impair -actinin-1 but not apoCaM binding, but not the F1658A and K1662E mutations, which impair apoCaM but not -actinin-1 binding, decreased single channel open probability, gating charge movement, and its coupling to channel opening. Thus, -actinin recruits CaV1.2 to defined surface regions and simultaneously boosts its open probability so that CaV1.2 is mostly active when appropriately localized.

molecular biology↗

bioRxiv2019

Molecular Determinants of μ-Conotoxin KIIIA interaction with the Voltage-Gated Sodium Channel Nav1.7

The voltage-gated sodium (NaV) channel subtype NaV1.7 plays a critical role in pain signaling, making it an important drug target. Here we studied the molecular interactions between -conotoxin KIIIA (KIIIA) and the human NaV1.7 channel (hNaV1.7). We developed a structural model of hNaV1.7 using Rosetta computational modeling and performed in silico docking of KIIIA using RosettaDock to predict residues forming specific pairwise contacts between KIIIA and hNaV1.7. We experimentally validated these contacts using mutant cycle analysis. Comparison between our KIIIA-hNaV1.7 model and the cryo-EM structure of KIIIA-hNaV1.2 revealed key similarities and differences between NaV channel subtypes with potential implications for the molecular mechanism of toxin block. The accuracy of our integrative approach, combining structural data with computational modeling, experimental validation, and molecular dynamics simulations, suggests that Rosetta structural predictions will be useful for rational design of novel biologics targeting specific NaV channels.

biophysics↗