Biology subjects

Clancy, C. E.

Publications and source records attributed to Clancy, C. E..

14 recordsLinked to original sources

bioRxiv2026

A Computational Model of Oxidative Stress in a Human Ventricular Myocyte

Mitochondrial reactive oxygen species (ROS) are implicated in cardiac dysfunction, but complex dynamic interactions between ROS, intracellular calcium, and electrophysiology make it difficult to resolve mechanisms experimentally. We developed the Zukowski excitation-contraction-mitochondrial-ROS (ECM-ROS) model, a computational model that couples human ventricular electrophysiology with mitochondrial calcium handling, energetics, ROS production, and ROS dependent modulation of the ryanodine receptor, SERCA, L-type calcium current, and late sodium current. Model predictions were evaluated against independent experimental measurements of ROS induced changes in action potential duration and intracellular calcium. We then used the model to characterize ROS calcium feedback across physiological pacing rates and applied it to doxorubicin induced oxidative stress. Increasing cytosolic calcium produced nonlinear amplification of mitochondrial activity and cytosolic ROS, with greater amplification at faster pacing rates. Moderate ROS elevation initially enhanced calcium release, whereas greater oxidative stress depleted sarcoplasmic reticulum calcium stores. During simulated doxorubicin exposure, faster pacing lowered the level of oxidative stress required to produce calcium dysregulation and action potential prolongation. Mitochondrial ROS and calcium accumulation preceded detectable electrophysiological remodeling. In populations incorporating variability in ion channel expression, neither doxorubicin induced oxidative stress nor subtle pharmacological IKr block alone produced early afterdepolarizations. However, early afterdepolarizations were predicted to emerge with combined perturbations. These findings establish a computational framework for investigating bidirectional coupling among mitochondrial function, ROS, calcium handling, and human cardiac electrophysiology. The model generates testable predictions regarding the early detection of oxidative cardiac injury and the physiological conditions that increase susceptibility to arrhythmia.

physiology↗

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

Digital Twin for the Win: Personalized Cardiac Electrophysiology

Individual variability shapes how diseases manifest, how patients respond to therapy, and how rare phenotypes arise. Conventional experimental approaches obscure variation by averaging, which limits mechanistic insight and predictive accuracy. We present a computational framework that builds digital twins of human-induced pluripotent stem cell-derived cardiomyocytes from a single optimized voltage clamp experiment. The framework depends on massive synthetic datasets comprising simulated cells that span broad ionic and electrophysiological ranges. These synthetic data make it possible to control parameters precisely, explore biological variability comprehensively, and train models beyond the limits of experimental data. A neural network trained on synthetic data then inferred biophysical parameters from experimental recordings from live cells, reproducing distinct electrophysiological features. Our study unites computational modeling, data simulation, and learning to enable scalable, precise, individualized cardiac electrophysiology modeling and can be readily extended to any electrically active cell type.

physiology↗

bioRxiv2025

Parameter Optimization of Biochemical Models for Precision Medicine: A Case Study in PI(4,5)P2 Synthesis

Precision medicine requires models that can translate rich molecular measurements into individualized predictions of biological response. This challenge is particularly acute for phosphoinositide signaling disorders that often exhibit cell-type-specific responses to identical genetic or pharmacological perturbations. Here, we develop a New Approach Methodology (NAM) demonstrating that basal phosphoinositide pool composition, determined by the size of the PI(4)P reserve, determines the robustness of lipid signaling. The NAM comprises a core kinetic model of phosphatidylinositol (PI), phosphatidylinositol 4-phosphate (PI(4)P), phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2), and inositol 1,4,5-trisphosphate (IP3) dynamics. The model also incorporates phospholipase C (PLC)-mediated hydrolysis and phosphatase-mediated turnover and explicitly accounts for IP3 biosensor binding during parameter optimization. Parameters were optimized using experimental measurements from superior cervical ganglion (SCG) neurons and validated against independent dose-dependent PI(4,5)P2 depletion data. Local and global sensitivity analyses were performed to identify the dominant parameter drivers of pathway behavior. These sensitivity relationships were then used to generate a population of model variants that captured phosphoinositide dynamics observed in tsA201 cells, human neuroblastoma cells, and hippocampal neurons. To infer cell-specific models, we developed two complementary inverse methods: sensitivity fingerprinting derived from mechanistic model sensitivities and a neural network trained on synthetic phosphoinositide time series. Both approaches reproduced experimental PI(4)P, PI(4,5)P2, and IP3 dynamics across cell types while preserving the baseline model structure. Importantly, the inferred models predicted experimentally observed differential vulnerability to kinase perturbation without additional fitting. Hippocampal neurons with large basal pools of PI(4)P maintained PI(4,5)P2 and IP3 signaling under phosphatidylinositol 4-kinase alpha (PI4KA) inhibition, whereas cells with small basal PI(4)P pools exhibited signaling failure. Simulations of PI4KA and phosphatidylinositol-4-phosphate 5-kinase type 1 gamma (PIP5K1C) loss-of-function mutations under repeated stimulation further revealed progressive signaling collapse in small-pool neurons but sustained function in large-pool neurons, demonstrating that basal lipid composition can determine genetic vulnerability. Together, this NAM provides a predictive, cell-specific framework for translating dynamic lipid measurements into mechanistic models that support precision medicine applications in phosphoinositide-related disorders.

biophysics↗

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

The formation of KV2.1 macro-clusters is required for sex-specific differences in L-type CaV1.2 clustering and function in arterial myocytes

In arterial myocytes, the canonical function of voltage-gated CaV1.2 and KV2.1 channels is to induce myocyte contraction and relaxation through their responses to membrane depolarization, respectively. Paradoxically, KV2.1 also plays a sex-specific role by promoting the clustering and activity of CaV1.2 channels. However, the impact of KV2.1 protein organization on CaV1.2 function remains poorly understood. We discovered that KV2.1 forms micro-clusters, which can transform into large macro-clusters when a critical clustering site (S590) in the channel is phosphorylated in arterial myocytes. Notably, female myocytes exhibit greater phosphorylation of S590, and macro-cluster formation compared to males. Contrary to current models, the activity of KV2.1 channels seems unrelated to density or macro-clustering in arterial myocytes. Disrupting the KV2.1 clustering site (KV2.1S590A) eliminated KV2.1 macro-clustering and sex-specific differences in CaV1.2 cluster size and activity. We propose that the degree of KV2.1 clustering tunes CaV1.2 channel function in a sex-specific manner in arterial myocytes.

physiology↗

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↗

bioRxiv2022

Facilitation by a hERG blocker is induced by pore opening and operates by pore reopening

A drug that blocks the cardiac myocyte voltage-gated K+ channels encoded by the human Ether-a-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 of the D540K hERG channel enables testing of hypotheses about 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, facilitates not only current activation by depolarization but also current activation by hyperpolarization. Our results indicate that induction of facilitation is coupled to pore opening, not voltage per se. We propose that a gated-access mechanism is involved in the voltage-dependence of induction of facilitation. This study provides a molecular mechanism for modulation of hERG channels by nifekalant, a clinically important antiarrhythmic agent. Significance statementNifekalant 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.

pharmacology and toxicology↗

bioRxiv2020

Sex-specific classification of drug-induced Torsade de Pointes susceptibility using cardiac simulations and machine learning

Torsade de Pointes (TdP), a rare but lethal ventricular arrhythmia, is a toxic side effect of many drugs. To assess TdP risk, safety regulatory guidelines require quantification of hERG channel block in vitro and QT interval prolongation in vivo for all new therapeutic compounds. Unfortunately, these have proven to be poor predictors of torsadogenic risk, and are likely to have prevented safe compounds from reaching clinical phases. While this has stimulated numerous efforts to define new paradigms for cardiac safety, none of the recently developed strategies accounts for patient conditions. In particular, despite being a well-established independent risk factor for TdP, female sex is vastly underrepresented in both basic research and clinical studies, and thus current TdP metrics are likely biased toward the male sex. Here, we apply statistical learning to synthetic data, generated by simulating drug effects on cardiac myocyte models capturing male and female electrophysiology, to develop new sex-specific classification frameworks for TdP risk. We show that (1) TdP classifiers require different features in females vs. males; (2) malebased classifiers perform more poorly when applied to female data; (3) female-based classifier performance is largely unaffected by acute effects of hormones (i.e., during various phases of the menstrual cycle). Notably, when predicting TdP risk of intermediate drugs on female simulated data, male-biased predictive models consistently underestimate TdP risk in women. Therefore, we conclude that pipelines for preclinical cardiotoxicity risk assessment should consider sex as a key variable to avoid potentially lifethreatening consequences for the female population.

pharmacology and toxicology↗

bioRxiv2020

A deep learning algorithm to translate and classify cardiac electrophysiology: From iPSC-CMs to adult cardiac cells

The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology and pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability and immature phenotype of the iPSC-CM platform. It was trained using simulated action potential (AP) data and applied to classify cells into the drug-free and drugged categories and to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CMs to the adult ventricular myocytes. The phase of the AP extremely sensitive to perturbation due to a steep rise of the membrane resistance was found to contain the key information required for successful network multitasking. We also demonstrated successful translation of both experimental and simulated iPSC-CM AP data validating our network by prediction of experimental drug-induced effects on adult cardiomyocyte APs by the latter.

bioinformatics↗

bioRxiv2019

Atomistic modeling towards predictive cardiotoxicity

Current methods for assessing safety pharmacology in the context of cardiac arrhythmia risk are unable to distinguish between drugs that cause cardiac rhythm disturbances and benign drugs. Drugs deemed likely to be unsafe share the common property of blocking the human Ether-a-go-go-Related Gene (hERG) encoded cardiac potassium channel and consequent prolongation of QT interval on the ECG. However, hERG block and QT prolongation alone are not selective indicators for cardiac arrhythmia. Here we present a prototype computational framework to distinguish between safe and unsafe hERG blockers. We used recent cryo-EM hERG structure to build and validate an atomistic structural model of the channel open conducting state. We also developed structural atomistic models of dofetilide, a hERG blocking drug with high pro-arrhythmia risk, in both charged and neutral ionization states. Next, we employed unbiased and enhanced sampling all-atom molecular dynamics (MD) simulations to probe atomic-scale mechanisms of dofetilide interaction with open-state hERG. Multi-microsecond drug \"flooding\" simulations revealed spontaneous dofetilide binding to the channel pore through the intracellular gate. Umbrella sampling MD was used to compute dofetilide affinity to hERG, in good agreement with experiment, as well as ingress and egress rates, which in a novel linkage between the atomistic and functional scale are utilized in our companion paper (Yang P-C et al. 2019 bioRxiv:635433) to parameterize functional kinetic models of dofetilide - hERG interactions used to predict emergent drug effects on the cardiac rhythm. This study represents the first necessary components of a computational framework for virtual cardiac safety pharmacology screening from the atom to the rhythm.

biophysics↗

bioRxiv2019

A computational pipeline to predict cardiotoxicity:From the atom to the rhythm

We simulate and predict cardiotoxicity over multiple temporal and spatial scales from the drug chemistry to the cardiac rhythm.\n\nABSTRACTDrug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation has become widely accepted as a surrogate marker for arrhythmia. The problem is that QT interval as an arrhythmia indicator is too sensitive and not selective, resulting in many potentially useful drugs eliminated early in the drug discovery process. We first set out to predict the fundamental mode of binding for the proarrhythmic drug dofetilide with the promiscuous cardiac drug target, the hERG potassium channel. In a novel linkage between the atomistic and functional scales, computed binding affinities and rates from atomistic simulation are utilized here to parameterize function scale kinetic models of dofetilide interactions with the hERG channel. The kinetic model components are then integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data from published studies were used to validate model framework and showed excellent agreement, demonstrating feasibility of the approach. The model predictions show that a clinically relevant dose of dofetilide increased arrhythmia vulnerability in all emergent TRIaD-linked parameters including Triangulation, Reverse use-dependence, beat-to-beat Instability and temporal and spatial action potential duration Dispersion. Application of machine learning demonstrated redundancy in the TRIaD linked parameters and suggested that changes in beat-to-beat instability were highly predictive of arrhythmia vulnerability in this setting. Here, we demonstrate the development and validation of a prototype multiscale model framework to predict electro-toxicity in the heart for the proarrhythmic drug dofetilide from the atom to the rhythm.\n\nSIGNIFICANCE STATEMENTCardiotoxicity in the form of deadly abnormal rhythms is one of the most common and dangerous risks for drugs in development and clinical use. There is an urgent need for new approaches to screen and predict the effects of chemically similar drugs on the cardiac rhythm and to move beyond the QT interval as a diagnostic indicator for arrhythmia. To this end, we present a computational pipeline to predict cardiotoxicity over multiple temporal and spatial scales from the drug chemistry to the cardiac rhythm. We utilize predicted quantitative estimates of ion channel-drug interactions from our companion paper to simulate cardiotoxicity over multiple temporal and spatial scales from the drug chemistry to the cardiac rhythm.

systems biology↗

bioRxiv2018

Structural Basis for Antiarrhythmic Drug Interactions with the Human Cardiac Sodium Channel

The human voltage-gated sodium channel, hNav1.5, is responsible for the rapid upstroke of the cardiac action potential and is target for antiarrhythmic therapy. Despite the clinical relevance of hNav1.5 targeting drugs, structure-based molecular mechanisms of promising or problematic drugs have not been investigated at atomic scale to inform drug design. Here, we used Rosetta structural modeling and docking as well as molecular dynamics simulations to study the interactions of antiarrhythmic and local anesthetic drugs with hNav1.5. These calculations revealed several key drug binding sites formed within the pore lumen that can simultaneously accommodate up to two drug molecules. Molecular dynamics simulations identified a hydrophilic access pathway through the intracellular gate and a hydrophobic access pathway through a fenestration between domains III and IV. Our results advance the understanding of molecular mechanisms of antiarrhythmic and local anesthetic drug interactions with hNav1.5 and will be useful for rational design of novel therapeutics.

biophysics↗