CPMDS Publications
BoltzOmics: Predicting genetic variant effects on drug binding with Boltz-2
July 2026
Summary
A mechanistic understanding of how genetic variants alter drug-receptor binding is central to precision medicine, drug response prediction, and drug development. Yet, experimental mutation-drug profiling remains slow and expensive, while existing computational approaches often trade accuracy for scalability. We developed BoltzOmics, an interactive, open-source platform that integrates Boltz-2, a deep learning model for biomolecular structure prediction, to rapidly assess mutation effects on drug binding. Starting from amino acid sequences, the workflow queries databases for genetic variants, generates wild-type and mutant protein structures, and screens multiple drugs across variants to predict binding affinity changes. We evaluated BoltzOmics across four targets: hERG, NaV1.5, HER2, and CYP3A4. Predictions achieved Pearson correlations with experimental drug IC50 data up to 0.76 for wild-type proteins and 0.60 for mutants. By enabling scalable, high-throughput assessment of drug-variant interactions, BoltzOmics establishes a practical AI-driven framework for accelerating computational drug discovery and advancing precision medicine research.
From Lipid Dynamics to Precision Predictions: A New Approach Methodology for Precision Modeling of Phosphoinositide Signaling
July 2026
Abstract
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.
Digital twins in nuclear medicine: Science fiction or reality?
July 2026
Abstract
Digital twins (DTs) are gaining attention in nuclear medicine, particularly in radiopharmaceutical therapy (RPT), yet their clinical relevance is often questioned. Drawing on a live debate at the 2025 SNMMI Annual Meeting, this Editorial is organized around the motion “Digital Twins in Nuclear Medicine: Science Fiction or Reality?” and three practice-facing questions: whether DTs address a distinct clinical need, whether current data and models are mature enough, and what validation, uncertainty quantification, and workflow requirements are needed for responsible deployment. We summarize supportive and skeptical positions and propose a practical synthesis for RPT. We emphasize that near-term DTs should be viewed not as AI agents decision-makers, but as task-specific, continuously updated decision-support systems that extend established pharmacokinetic and dosimetry methods.
Large-scale synthetic data enable digital twins of human excitable cells
July 2026
Abstract
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.
Decoding disease complexity: Multi-Omics integration and AI in precision medicine
June 2026
Introduction
Recent advances in artificial intelligence (AI) have substantially transformed the analysis of complex biological systems. The application of AI models to each layer of biological and omics data independently, including genomics, transcriptomics, proteomics, and metabolomics, has generated unprecedented insights into disease mechanisms, molecular interactions, and regulatory networks. By enabling the integration and interpretation of high-dimensional datasets, these computational approaches have improved our ability to identify biomarkers, uncover novel therapeutic targets, and characterize the genetic and environmental factors underlying complex traits.
Prioritizing Discovery and Advancements in Arrhythmia Therapies: NIH/NHLBI Workshop
June 2026
Abstract
Cardiac arrhythmias affect >2% of the US population and are a major cause of morbidity and mortality that results in >250,000 deaths annually in the United States. Arrhythmias can be intermittent and may be without symptoms, leading them to be undetected and underdiagnosed, which makes their true prevalence difficult to assess. Because atrial fibrillation (AF) is often detected after a stroke or after development of left ventricular dysfunction, it is associated with substantial mortality and morbidity, posing a significant burden to patients and health care services.
Reactome pathway enrichment analysis identifies biological processes associated with biochemical recurrence following radical prostatectomy
May 2026
Abstract
Prostate cancer recurrence following radical prostatectomy (RP) affects 30–50% of men within 10 years, yet existing biomarkers show variable prognostic performance. Pathway-level approaches may offer additional prognostic insight by capturing coordinated biological processes. We applied Reactome-based pathway enrichment to tumor transcriptomes to identify biological processes associated with time to biochemical recurrence (BCR).
Multi-organ crosstalk in HFpEF: Reframing a cardiac syndrome as a widespread systemic disease
May 2026
Introduction
Heart failure (HF) with preserved ejection fraction (HFpEF) has emerged as one of the most challenging syndromes in cardiovascular medicine due to its heterogenous phenotype and limited therapies (Mishra & Kass, 2021). Despite embodying half of all HF cases and continuing to increase in prevalence with an aging population, HFpEF has historically been non-responsive to many therapeutic strategies that proved effective in HF with reduced ejection fraction (HFrEF) (Redfield & Borlaug, 2023). One reason for this persistent therapeutic deficiency may be that HFpEF has been long conceptualized primarily as a confined cardiac disease of left ventricular diastolic dysfunction. Increasing evidence now suggests that this perspective is incomplete (Cohen et al., 2020). Hence, a precision medicine approach may help to illuminate the complex pathophysiology and heterogeneous phenotype of HFpEF.
Cellular mechanisms of radiation-induced myocyte dysfunction: effects on calcium handling, ion channel regulation and mitochondrial energetics
April 2026
Abstract
Ionizing radiation induces a range of cellular responses in cardiomyocytes that vary with the dose, duration of exposure and metabolic state. Although historically attributed to microvascular injury and fibrosis, radiation-induced cardiac dysfunction is now recognized to originate from direct perturbations of myocyte calcium handling, ion channel regulation and mitochondrial energetics. Low to moderate radiation doses generate sustained reactive oxygen species (ROS) that activate oxidation-dependent calcium/calmodulin-dependent protein kinase II (CaMKII) signalling, leading to disrupted sarcoplasmic reticulum calcium cycling, altered sodium and calcium currents and increased susceptibility to early and delayed after-depolarizations. Mitochondrial structural and energetic instability further amplifies ROS–CaMKII feedback, promoting a pro-arrhythmic electrophysiological substrate. High-dose radiation exposures, such as those used in cardiac stereotactic body radiotherapy, lead to a distinct electrical reprogramming phenotype characterized by coordinated upregulation of sodium channels, calcium channels, potassium channels and gap junction proteins. The resulting emergent effects are to enhance conduction velocity and electrical homogeneity that together provide a mechanistic explanation for the rapid anti-arrhythmic effects observed clinically, even independent of fibrosis. Across the radiation dose spectrum, the mitochondria serve as key integrators of redox stress and calcium overload, shaping the transition from reversible signalling alterations to persistent remodelling. This review synthesizes mechanistic patterns underlying radiation-induced myocyte dysfunction, highlights unresolved discrepancies across experimental models and discusses how computational modelling might be the ideal tool to predict optimal therapeutic radiation delivery while mitigating long-term cardiotoxicity.
Natural language processing of biomedical text to map and prioritize protein–disease associations in HFpEF
March 2026
Abstract
The validation of promising clinical biomarkers, molecular mechanisms, and novel drug targets in cardiovascular disease (CVD) is hindered by a vast and fragmented biomedical literature, which now exceeds 38 million publications indexed in PubMed. To address the central challenge of navigating and synthesizing a huge fragmented biomedical literature base, we applied our validated machine learning–based text-mining algorithm containing natural language processing (NLP) and incorporated this into a ValIdated Text-mining using Advanced Language model (VITAL) as a complementary framework.
Towards credible digital twins for basic and preclinical research
January 2026
Abstract
Digital twins are well established in industrial settings, but there has not been wide adoption in biomedical settings. Digital twins for biomedical applications are now possible with the inclusion of artificial intelligence and the potential to combine mechanistic and clinical models that learn and adjust for human variability.
Mapping the Conformational Landscape of Human Voltage-Gated Sodium Channels with AlphaFold
December 2025
Abstract
Voltage-gated sodium (NaV) channels are central players in electrical signaling across biological systems, shaping excitability, information processing, and physiological function in health and disease; understanding their molecular, structural, and energetic mechanisms is therefore essential for deciphering channel dysfunction and enabling the development of next-generation, more precise and personalized therapeutic strategies. Researchers at UC Davis’s Center for Precision Medicine and Data Sciences report a new AlphaFold2-based computational modeling framework for moving beyond single “snapshot” structures toward a more dynamic view of the conformational landscape that underlies NaV channel gating, regulation, and pharmacology. By enhancing conformational sampling using subsampled multiple-sequence alignments and varying the number of recycles, the team generated ensembles of full-length models across the human NaV1.1–NaV1.9 family that recapitulate experimentally observed conformations, propose additional states not yet described experimentally, and suggest potential intermediate conformations. Correlation and clustering analyses then organize these large ensembles into recurrent, interpretable state groups and reveal coordinated behavior across key functional regions. In parallel, the study benchmarks AlphaFold Multimer for NaV protein–protein interactions, showing high-accuracy models of complexes with auxiliary β subunits and calmodulin, and demonstrating that these partners can reshape the modeled conformational landscape and coupling between functional states. Together, the work provides a reproducible, hypothesis-generating workflow to model and analyze NaV structural ensembles, providing a foundational step toward capturing the full complexity of NaV channel dynamics and modulation, a key requirement for developing next-generation, structure-guided, and ultimately more personalized therapeutic strategies.
Health Care Quality and Patient Safety in the Era of Artificial Intelligence
December 2025
Abstract
The integration of digital health technologies is revolutionizing health care quality and patient safety by shifting from reactive to proactive care models. Advances in electronic health records, clinical decision support systems, data analytics, artificial intelligence (AI), mobile applications, and tele-health are enhancing clinical decision-making, patient engagement, and care coordination. While these technologies offer significant benefits, challenges related to data privacy, integration into workflows, and health disparities require ongoing attention to ensure that all patients can benefit from these innovations. Adoption of digital health and AI to bring systemness and real time information delivery can support delivery of high-quality care.
Harnessing AlphaFold to reveal hERG channel conformational state secrets
June 2025
Abstract
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. However, the structural details of multiple conformational states have remained elusive. Here, we guided AlphaFold2 to predict plausible hERG inactivated and closed conformations, obtaining results consistent with multiple available experimental data. Drug docking simulations demonstrated hERG state-specific drug interactions in good agreement with experimental results, revealing that most drugs bind more effectively in the inactivated state and are trapped in the closed state. Molecular dynamics simulations demonstrated ion conduction for an open but not AlphaFold2 predicted inactivated state that aligned with earlier studies. Finally, we identified key molecular determinants of state transitions by analyzing interaction networks across closed, open, and inactivated states in agreement with earlier mutagenesis studies. Here, we demonstrate a readily generalizable application of AlphaFold2 as an effective and robust method to predict discrete protein conformations, reconcile seemingly disparate data and identify novel linkages from structure to function.
An overview of drug-induced sodium channel blockade and changes in cardiac conduction: Implications for drug safety
December 2024
Abstract
The human voltage-gated sodium channel Nav1.5 (hNav1.5/SCN5A) plays a critical role in the initiation and propagation of action potentials in cardiac myocytes, and its modulation by various drugs has significant implications for cardiac safety. Drug-dependent block of Nav1.5 current (INa) can lead to significant alterations in cardiac electrophysiology, potentially resulting in conduction slowing and an increased risk of proarrhythmic events. This review aims to provide a comprehensive overview of the mechanisms by which various pharmacological agents interact with Nav1.5, focusing on the molecular determinants of drug binding and the resultant electrophysiological effects. We discuss the structural features of Nav1.5 that influence drug affinity and specificity. Special attention is given to the concept of state-dependent block, where drug binding is influenced by the conformational state of the channel, and its relevance to therapeutic efficacy and safety. The review also examines the clinical implications of INa block, highlighting case studies of drugs that have been associated with adverse cardiac events, and how the Vaughan-Williams Classification system has been employed to qualify “unsafe” sodium channel block. Furthermore, we explore the methodologies currently used to assess INa block in nonclinical and clinical settings, with the hope of providing a weight of evidence approach including in silico modeling, in vitro electrophysiological assays and in vivo cardiac safety studies for mitigating proarrhythmic risk early in drug discovery. This review underscores the importance of understanding Nav1.5 pharmacology in the context of drug development and cardiac risk assessment.
Mechanisms of Chemical Atrial Defibrillation by Flecainide and Ibutilide
December 2024
Abstract
Pharmacological approaches to atrial defibrillation offer noninvasive, low-risk, and cost-effective alternatives to ablation, promoting health equity. This study investigates how ibutilide-mediated action potential prolongation enhances use-dependent effects of flecainide by shortening the diastolic interval, reducing drug unbinding, and suppressing atrial excitability to terminate re-entrant arrhythmia. Using computational modeling, we predict optimal sodium- and potassium-channel blocker combinations for chemical atrial defibrillation. The results suggest that acute flecainide-ibutilide application is a viable drug-repurposing strategy. Additionally, we assess the safety pharmacology of this combination on ventricular electrophysiology.
An artificial intelligence accelerated virtual screening platform for drug discovery
September 2024
Abstract
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 develop 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 screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is 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.
Advances in Induced Pluripotent Stem Cell-derived Cardiac Myocytes: Technological Breakthroughs, Key Discoveries and New Applications
July 2024
Abstract
A transformation is underway in precision and patient-specific medicine. Rapid progress has been enabled by multiple new technologies including induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs). Here, we delve into these advancements and their future promise, focusing on the efficiency of reprogramming techniques, the fidelity of differentiation into the cardiac lineage, the functional characterization of the resulting cardiac myocytes, and the many applications of in silico models to understand general and patient-specific mechanisms controlling excitation–contraction coupling in health and disease. Furthermore, we explore the current and potential applications of iPSC-CMs in both research and clinical settings, underscoring the far-reaching implications of this rapidly evolving field.
Structure-Activity Relationship Study Identifies a Novel Lipophilic Amiloride Derivative that Efficiently Kills Chemoresistant Breast Cancer Cells
July 2024
Abstract
Derivatives of the potassium-sparing diuretic amiloride are preferentially cytotoxic toward tumor cells relative to normal cells, and have the capacity to target tumor cell populations resistant to currently employed therapeutic agents. However, a major barrier to clinical translation of the amilorides is their modest cytotoxic potency, with estimated IC50 values in the high micromolar range. Here we report the synthesis of ten novel amiloride derivatives and the characterization of their cytotoxic potency toward MCF7 (ER/PR-positive), SKBR3 (HER2-positive) and MDA-MB-231 (triple negative) cell line models of breast cancer. Comparisons of derivative structure with cytotoxic potency toward these cell lines underscore the importance of an intact guanidine group, and uncover a strong link between drug-induced cytotoxicity and drug lipophilicity. We demonstrate that our most potent derivative called LLC1 is preferentially cytotoxic toward mouse mammary tumor over normal epithelial organoids, acts in the single digit micromolar range on breast cancer cell line models representing all major subtypes, acts on cell lines that exhibit both transient and sustained resistance to chemotherapeutic agents, but exhibits limited anti-tumor effects in a mouse model of metastatic breast cancer. Nonetheless, our observations offer a roadmap for the future optimization of amiloride-based compounds with preferential cytotoxicity toward breast tumor cells.
Association of Neighborhood and Environmental Factors With Clinical Phenotypes and Outcomes in Heart Failure With Preserved Ejection Fraction
July 2024 — Heart failure with preserved ejection fraction (HFpEF) is heterogeneous with multiple comorbidities and limited therapeutic options.1 Multiple pathologies contribute to the development of distinct clinical HFpEF phenogroups. Evidence suggests that social determinants of health (SDoH) are pivotal in the pathogenesis of cardiovascular disease. Defined by the Centers for Disease Control and Prevention and the World Health Organization, SDoH refers to the conditions in the environments where people are born, live, learn, work, play, worship, and age, influencing health outcomes and quality-of-life risks. SDoH encompasses 5 domains: (1) Education Access and Quality, (2) Economic Stability, (3) Social and Community Context, (4) Health Care Access and Quality, (5) Neighborhood and Built Environment.
Toward High-resolution Modeling of Small Molecule–ion Channel Interactions
June 2024
Abstract
Ion channels are critical drug targets for a range of pathologies, such as epilepsy, 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 RosettaLigand docking methods, RosettaLigand and GALigandDock, on the 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 frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1–2 Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates 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.
Reaching Across the Causeway Award 2024
Co-PIs: Kermit L. Carraway, James M. Angelastro
2024
Abstract
The non-canonical (β-catenin-independent) Wnt/planar cell polarity (Wnt/PCP) pathway plays central roles in embryonic development by mediating cellular motility events required for proper tissue structuring. Accumulating evidence suggests that the Wnt/PCP pathway is exploited by some solid tumors to promote their growth and invasiveness. Glioblastoma (GBM) is the most common and lethal form of brain cancer in humans. Efforts to thwart GBM malignancy with therapeutic agents targeting presumed oncogenic drivers have not been successful, suggesting that unknown or unexplored pathways might additionally contribute to malignancy. We have found that key components of the Wnt/PCP pathway are overexpressed in GBM relative to normal brain tissue independent of GBM subtype, and overexpression correlates with poorer patient outcomes. Moreover, we have observed that suppression of Fzd7 and Vangl1, core components of the Wnt/PCP signaling complex, reduce GBM cell growth and invasiveness in vitro and in vivo. Together these observations suggest that aberrant Wnt/PCP engagement may contribute to the malignant properties of a wide range of GBM patients’ tumors. The driving hypothesis for the project is that Wnt/PCP signaling contributes to the malignant properties of GBM largely independent of specific subtypes and suspected molecular drivers, and that mechanistic vulnerabilities may be identified that could ultimately aid in the development of novel therapeutic strategies and agents. Our overarching aims for the project are to validate Wnt/PCP as a viable target for multiple GBM subtypes using a series of patient-derived xenograft (PDX) models, and to develop a more thorough mechanistic understanding of the interactions among pathway components. In Aim 1 we will assess the Wnt/PCP dependence of five patient-derived xenograft models of primary GBM from the Mayo Brain Tumor PDX National Resource that represent an array of oncogenic drivers using cellular proliferation and invasiveness assays. In addition, we will determine the impact of Wnt/PCP component ablation on the growth of orthotopically xenografted tumors derived from the five genetically diverse PDX lines in NOD SCID mice, as well as in tumors from a retrovirus-induced model of GBM in immune-intact mice. We anticipate that these studies will begin to highlight the GBM subtype-agnostic nature of Wnt/PCP dependence. In Aim 2, we will unravel the molecular mechanisms underlying Wnt/PCP-induced motility/invasiveness and proliferation. Specifically, we will assess the role of a newly identified Vangl1/Fzd7 complex in signaling to the actin cytoskeleton, and examine the mechanism of Wnt/PCP-induced cross-regulation of the Akt kinase in mediating GBM cell proliferation. The successful completion of the proposed studies will validate Wnt/PCP as target for multiple GBM subtypes, and will develop a mechanistic platform upon which Wnt-PCP targeting therapeutics may ultimately be developed.
A Computational Model Predicts Sex-specific Responses to Calcium Channel Blockers in Mammalian Mesenteric Vascular Smooth Muscle
February 2024
Abstract
The function of the smooth muscle cells lining the walls of mammalian systemic arteries and arterioles is to regulate the diameter of the vessels to control blood flow and blood pressure. Here, we describe an in silico model, which we call the 'Hernandez-Hernandez model', of electrical and Ca2+ signaling in arterial myocytes based on new experimental data indicating sex-specific differences in male and female arterial myocytes from murine resistance arteries. The model suggests the fundamental ionic mechanisms underlying membrane potential and intracellular Ca2+ signaling during the development of myogenic tone in arterial blood vessels.
Elucidating Molecular Mechanisms of Protoxin-II State-specific Binding to the Human NaV1.7 Channel
February 2024
Abstract
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-II (PTx2) has high selectivity for hNaV1.7 and is a valuable scaffold for designing 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).
Toward Digital Twin Technology for Precision Pharmacology
February 2024
Abstract
The authors demonstrate the feasibility of technological innovation for personalized medicine in the context of drug-induced arrhythmia. The authors use atomistic-scale structural models to predict rates of drug interaction with ion channels and make predictions of their effects in digital twins of induced pluripotent stem cell-derived cardiac myocytes. The authors construct a simplified multilayer, 1-dimensional ring model with sufficient path length to enable the prediction of arrhythmogenic dispersion of repolarization. Finally, the authors validate the computational pipeline prediction of drug effects with data and quantify drug-induced propensity to repolarization abnormalities in cardiac tissue. The technology is high throughput, computationally efficient, and low cost toward personalized pharmacologic prediction.
Structural Modeling of Ion Channels Using AlphaFold2, RoseTTAFold2, and ESMFold
September 2023
Abstract
Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel – CaV1.1, and human voltage-gated potassium (KV) channel – KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.
A Multiscale Predictive Digital Twin for Neurocardiac Modulation
August 2023
Abstract
Cardiac function is tightly regulated by the autonomic nervous system (ANS). Activation of the sympathetic nervous system increases cardiac output by increasing heart rate and stroke volume, while parasympathetic nerve stimulation instantly slows heart rate. Importantly, imbalance in autonomic control of the heart has been implicated in the development of arrhythmias and heart failure. Understanding of the mechanisms and effects of autonomic stimulation is a major challenge because synapses in different regions of the heart result in multiple changes to heart function. For example, nerve synapses on the sinoatrial node (SAN) impact pacemaking, while synapses on contractile cells alter contraction and arrhythmia vulnerability. Here, we present a multiscale neurocardiac modelling and simulator tool that predicts the effect of efferent stimulation of the sympathetic and parasympathetic branches of the ANS on the cardiac SAN and ventricular myocardium. The model includes a layered representation of the ANS and reproduces firing properties measured experimentally. Model parameters are derived from experiments and atomistic simulations. The model is a first prototype of a digital twin that is applied to make predictions across all system scales, from subcellular signalling to pacemaker frequency to tissue level responses. We predict conditions under which autonomic imbalance induces proarrhythmia and can be modified to prevent or inhibit arrhythmia. In summary, the multiscale model constitutes a predictive digital twin framework to test and guide high-throughput prediction of novel neuromodulatory therapy.
Right Heart Remodeling Assessed by Cardiac Magnetic Resonance Imaging Following Transcatheter Tricuspid Valve Annuloplasty
June 2023
Transcatheter tricuspid valve interventions (TTVIs) have evolved to become of paramount importance for the treatment of tricuspid regurgitation (TR), because drug-based therapy is ineffective in advanced disease stages, and surgery remains associated with high mortality rates. Edge-to-edge repair and direct transcatheter tricuspid valve annuloplasty (TTVA) are the most favored treatment strategies in TTVI and have so far shown promising results. However, uncertainty regarding the role and timing of intervention for TR remains high. The impact of right ventricular (RV) geometry and function as well as the coupling to systolic artery pressure with regard to patient outcome has been shown.1,2 Overt RV and atrial dilation and impaired right heart function are negatively affecting patients’ prognoses. Yet, knowledge of the effect of TTVI on RV remodeling is still scarce, especially considering that patients with TR are a heterogeneous population, presenting with varying levels of right heart impairment. Most studies on TTVI and TR itself are based on echocardiography. However, echocardiography is severely limited in assessing the right heart. Cardiac magnetic resonance (CMR) remains the noninvasive gold standard and the preferred tool for assessing right heart dimension and function. It also allows for a direct comparison of the effects of TTVI on the left heart without limiting image acquisition by medical staff and allows for excellent interobserver variability.
