Biology subjects

Sanders, C. R.

Publications and source records attributed to Sanders, C. R..

9 recordsLinked to original sources

bioRxiv2025

Classification models distinguish functional and trafficking effects of KCNQ1 variants to enhance variant interpretation

Missense mutations compromise protein fitness by altering stability and function, which can lead to various clinical disease states. The potassium ion channel KCNQ1 underlies the majority of congenital long QT syndrome (LQTS) cases, one of the most common genetic arrhythmia syndromes. During genetic testing for LQTS, variants of uncertain significance (VUS) confound diagnosis and clinical management. KCNQ1 protein fitness metrics enable mechanistic classification of variants, directly informing the molecular basis for dysfunction and providing clinical interpretation of variants linked to LQTS and other channelopathies. We developed structure-aware random forest classifier models to predict seven metrics of KCNQ1 fitness, four functional electrophysiology measurements (peak current density, voltage-dependence, gating kinetics), and three trafficking values measured by flow cytometry. Our trained models outperformed AlphaMissense in predicting protein fitness, enhancing interpretation of ClinVar VUS and variants classified as ambiguous by AlphaMissense. We demonstrate the classifiers distinguish benign and pathogenic variants from ClinVar and gnomAD and identify systematic patterns of dysfunction and mistrafficking along the functionally critical S4 helix. Our method advances variant effect prediction with a mechanistic classifier that reliably links missense mutations in KCNQ1 to their specific disease-causing mechanisms. As a resource for precision medicine approaches for LQTS or other KCNQ1 channelopathies, we provide the predictions and scores for all KCNQ1 missense variants across the structured region of the protein.

biophysics↗

bioRxiv2025

High Throughput Screening Identifies a Small Molecule Trafficking Corrector for Long-QT Syndrome Associated KCNQ1 Variants

Congenital long QT syndrome (LQTS) promotes risk for life-threatening cardiac arrhythmia and sudden death in children and young adults. Pathogenic variants in the voltage-gated potassium channel KCNQ1 are the most frequently discovered genetic cause. Most LQTS-associated KCNQ1 variants cause loss-of-function secondary to impaired trafficking of the channel to the plasma membrane. There are currently no therapeutic approaches that address this underlying molecular defect. Using a high throughput screening paradigm, we identified VU0494372, a small molecule that increases total and cell surface expression, and trafficking efficiency of wildtype (WT) KCNQ1 as well as three LQTS-associated variants. Additionally, 16-hour treatment of cells with VU0494372 increased current density for WT KCNQ1 and the LQTS-associated variant V207M. VU0494372 had no impact on KCNQ1 transcription, degradation, or thermal stability. We identified a potential direct interaction site with KCNQ1 at or near the binding site of the KCNQ1 potentiator ML277. Together, these findings demonstrate that small molecules can increase the expression levels and cell surface trafficking of KCNQ1 and introduce a potential new pharmacological approach for treating LQTS.

biochemistry↗

bioRxiv2022

Molecular simulations reveal a mechanism for enhanced allosteric coupling between voltage-sensor and pore domains in KCNQ1 explaining its activation by ML277

The voltage-gated potassium channel KCNQ1 (KV7.1) is important for the repolarizing phase of the cardiac action potential. Activators of KCNQ1 may provide a strategy for the pharmacological treatment of congenital long QT syndrome, a genetic disorder caused by pathogenic variants in KCNQ1 that promote arrhythmia susceptibility and elevate risk for sudden cardiac death. The small-molecule agonist ML277 recovers function of mutant KCNQ1 channels in human induced pluripotent stem cell-derived cardiomyocytes and could represent a starting point for drug development. Here we investigated ML277 mode of action by developing a molecular model of the KCNQ1-ML277 interaction corroborated by experimental and computational analyses. Ligand docking and molecular dynamics simulation demonstrated that ML277 binds to the interface between the voltage sensor and pore domains in KCNQ1. Model predicted binding energies for ML277 and 62 chemical analogs of ML277 correlated with EC50 data available for these compounds. We identified novel ML277-interacting residues on the S5 and S6 segments of KCNQ1 by performing MM/PBSA energy calculations and site-directed mutagenesis of KCNQ1 coupled to electrophysiological characterization of the generated channel mutants. Network analysis of the molecular dynamics simulations further showed that ML277 increases the allosteric coupling efficiency between residues in the voltage sensor domain and residues in the pore domain. Derivatives of ML277 that are not active on KCNQ1 fail to increase allosteric coupling efficiency in the computational simulations. Our results reveal atomic details of the ML277 modulation of KCNQ1 activation. These findings may be useful for the design of allosteric modulators of KCNQ1 and other KCNQ channels that bind at the membrane-accessible protein surface. Statement of SignificanceThe potassium ion channel KCNQ1 contributes to the generation of electrical impulses in the heart. Heritable mutations in KCNQ1 can cause channel loss-of-function and predispose to a life-threatening cardiac arrhythmia. Small molecules that bind KCNQ1 and enhance channel function could establish a novel anti-arrhythmic drug paradigm. We used molecular simulations to investigate how a small agonist of KCNQ1 (ML277) binds to the KCNQ1 channel and increases its function. We identified amino acids that are responsible for ML277 binding and show how ML277 promotes signaling in KCNQ1 and channel opening. This work advances our understanding how KCNQ1 and possibly other potassium channels can be activated with small molecules. These data provide a framework for drug development studies.

biophysics↗

bioRxiv2021

Predicting the Functional Impact of KCNQ1 Variants with Artificial Neural Networks

Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasing applied to problems in biology, biophysical understanding will be critical with respect to explainable AI, i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org. Author summaryHeartbeat is maintained by electrical impulses generated by ion-conducting channel proteins in the heart such as the KCNQ1 potassium channel. Pathogenic variants in KCNQ1 can lead to channel loss-of-function and predisposition to fatal life-threatening irregularities of heart rhythm (arrhythmia). Machine learning methods that can predict the outcome of a mutation on KCNQ1 structure and function would be of great value in helping to assess the risk of a heart rhythm disorder. Recently, machine learning has made great progress in predicting the structures of proteins from their sequences. However, there are limited studies that link the effect of a mutation and change in protein structure with its function. This work presents the development of neural network models designed to predict mutation-induced changes in KCNQ1 functional parameters such as peak current density and voltage dependence of activation. We compare the predictive ability of features extracted from sequence, structure, and physicochemical properties of KCNQ1. Moreover, input sensitivity analysis connects biophysical features with specific functional parameters that provides insight into underlying molecular mechanisms for KCNQ1 channels. The best performing neural network model is publicly available as a webserver, called Q1VarPredBio, that delivers predictions about the functional phenotype of KCNQ1 variants.

biophysics↗

bioRxiv2021

Delivery of recombinant SARS-CoV-2 envelope protein into human cells

SARS-CoV-2 envelope protein (S2-E) is a conserved membrane protein that is essential to coronavirus assembly and budding. Here, we describe the recombinant expression and purification of S2-E into amphipol-class amphipathic polymer solutions. The physical properties of amphipols underpin their ability to solubilize and stabilize membrane proteins without disrupting membranes. Amphipol delivery of S2-E to pre-formed planar bilayers results in spontaneous membrane integration and formation of viroporin ion channels. Amphipol delivery of the S2-E protein to human cells results in membrane integration followed by retrograde trafficking to a location adjacent to the endoplasmic reticulum-to-Golgi intermediate compartment (ERGIC) and the Golgi, which are the sites of coronavirus replication. Delivery of S2-E to cells enables both chemical biological approaches for future studies of SARS-CoV-2 pathogenesis and development of "Trojan Horse" anti-viral therapies. This work also establishes a paradigm for amphipol-mediated delivery of membrane proteins to cells.

biochemistry↗

bioRxiv2020

The C99 domain of the amyloid precursor protein is a disordered membrane phase-preferring protein

Processing of the amyloid precursor protein (APP) via the amyloidogenic pathway is associated with the etiology of Alzheimers disease. The cleavage of APP by {beta}-secretase to generate the transmembrane 99-residue C-terminal fragment (C99) and subsequent processing of C99 by {gamma}-secretase to yield amyloid-{beta} (A{beta}) peptides are essential steps in this pathway. Biochemical evidence suggests amyloidogenic processing of C99 occurs in cholesterol- and sphingolipid-enriched liquid ordered phase membrane raft domains. However, direct evidence that C99 preferentially associates with rafts has remained elusive. Here, we test this idea by quantifying the affinity of C99-GFP for raft domains in cell-derived giant plasma membrane vesicles. We find that C99 is essentially excluded from ordered domains in HeLa cells, SH-SY5Y cells and neurons, instead exhibiting a strong (roughly 90%) affinity for disordered domains. The strong association of C99 with disordered domains occurs independently of its cholesterol binding activity, homodimerization, or the familial Alzheimer disease Arctic mutation. Finally, we confirm previous studies suggesting that C99 is processed in the plasma membrane by -secretase, in addition to the well-known {gamma}-secretase. These findings suggest that C99 itself lacks an intrinsic affinity for raft domains, implying either that amyloidogenic processing of the protein occurs in disordered regions of the membrane, that processing involves a marginal sub-population of C99 found in rafts, or that as-yet-unidentified protein-protein interactions involving C99 in living cells drive it into rafts to promote its cleavage therein.

neuroscience↗

bioRxiv2020

Peripheral Myelin Protein 22 Preferentially Partitions into Ordered Phase Membrane Domains

The ordered environment of membrane rafts is thought to exclude many transmembrane proteins. Nevertheless, some multi-pass transmembrane proteins have been proposed to partition into ordered domains. Here, giant plasma membrane vesicles (GPMVs) were employed to quantitatively show that the tetraspan peripheral myelin protein 22 (PMP22) exhibits a pronounced preference for, promotes the formation of, and stabilizes ordered membrane domains. Neither S-palmitoylation of PMP22 nor its putative cholesterol binding motifs are required for partitioning to ordered domains. In contrast, disruption of its unusual first transmembrane helix (TM1) reduced ordered phase preference. Charcot-Marie-Tooth disease-causing mutations that destabilize PMP22 also reduced or eliminated this preference in favor of the disordered phase. These studies demonstrate PMP22s ordered phase preference derives both from the distinctive properties of TM1 and global structural features associated with its transmembrane domain, providing a first glimpse at the structural factors that promote raft partitioning for multi-pass proteins. Significance StatementThe preferential partitioning of single span membrane proteins for the ordered phase of ordered/disordered phase-separated membranes is now reasonably well understood, but little is known about this phase preferences of multi-pass membrane proteins. Here, it is shown that the disease-linked tetraspan integral membrane protein, PMP22, displays a pronounced preference to partition into the ordered phase, a preference that is reversed by disease mutations. This phase preference may be related to the role of PMP22 in cholesterol homeostasis in myelinating Schwann cells, a role that is also known to be disrupted under conditions of CMTD peripheral neuropathy caused by pmp22 mutations.

biophysics↗

bioRxiv2019

Upgraded molecular models of the human KCNQ1 potassium channel

The voltage-gated potassium channel KCNQ1 (KV7.1) assembles with the KCNE1 accessory protein to generate the slow delayed rectifier current, IKS, which is critical for membrane repolarization as part of the cardiac action potential. Loss-of-function (LOF) mutations in KCNQ1 are the most common cause of congenital long QT syndrome (LQTS), type 1 LQTS, an inherited genetic predisposition to cardiac arrhythmia and sudden cardiac death. A detailed structural understanding of KCNQ1 is needed to elucidate the molecular basis for KCNQ1 LOF in disease and to enable structure-guided design of new anti-arrhythmic drugs. In this work, advanced structural models of human KCNQ1 in the resting/closed and activated/open states were developed by Rosetta homology modeling guided by newly available experimentally-based templates: X. leavis KCNQ1 and resting voltage sensor structures. Using molecular dynamics (MD) simulations, the models capability to describe experimentally established channel properties including state-dependent voltage sensor gating charge interactions and pore conformations, PIP2 binding sites, and voltage sensor - pore domain interactions were validated. Rosetta energy calculations were applied to assess the models utility in interpreting mutation-evoked KCNQ1 dysfunction by predicting the change in protein thermodynamic stability for 50 characterized KCNQ1 variants with mutations located in the voltage-sensing domain. Energetic destabilization was successfully predicted for folding-defective KCNQ1 LOF mutants whereas wild type-like mutants had no significant energetic frustrations, which supports growing evidence that mutation-induced protein destabilization is an especially common cause of KCNQ1 dysfunction. The new KCNQ1 Rosetta models provide helpful tools in the study of the structural mechanisms of KCNQ1 function and can be used to generate structure-based hypotheses to explain KCNQ1 dysfunction.\n\nAuthor SummaryCardiac rhythm is maintained by synchronized electrical impulses conducted throughout the heart. The potassium ion channel KCNQ1 is important for the repolarization phase of the cardiac action potential that underlies these electrical impulses. Heritable mutations in KCNQ1 can lead to channel loss-of-function (LOF) and predisposition to a life-threatening cardiac arrhythmia. Knowledge of the three-dimensional structure of KCNQ1 is important to understand how mutations lead to LOF and to support structurally-guided design of new anti-arrhythmic drugs. In this work, we present the development and validation of molecular models of human KCNQ1 inferred by homology from the structure of frog KCNQ1. Models were developed for the open channel state in which potassium ions can pass through the channel and the closed state in which the channel is not conductive. Using molecular dynamics simulations, interactions in the voltage-sensing and pore domain of KCNQ1 and with the membrane lipid PIP2 were analyzed. Energy calculations for KCNQ1 mutations in the voltage-sensing domain reveled that most of the mutations that lead to LOF cause energetic destabilization of the KCNQ1 protein. The results support both the utility of the new models and growing evidence that mutation-induced protein destabilization is a common cause of KCNQ1 dysfunction.

biophysics↗

bioRxiv2019

Do available protein 3D structures reflect human genetic and functional diversity?

Genomic databases are substantially biased towards European ancestry populations, and this bias contributes to health disparities. Here, we quantify how well 66,971 experimentally characterized human protein 3D structures represent the diversity of protein sequences observed across the 1000 Genomes Project. More than 85% of available structures do not match a sequence observed in at least one individual, and on average structures match the sequence of 74% of individuals. Nearly 23% of human structures do not match any observed sequences; however, after masking engineered/known mutations, this decreases to ~4%. African ancestry sequences are modestly, but significantly, less likely to be represented by structures (73.5% vs. 74.0%). These differences are mainly driven by the greater genetic diversity of African populations. We identify thousands of variants unrepresented in available structures that influence protein structure and function. Thus, the use of a single structure as representative of \"the wild type\" protein will often bias results against many individuals. The diversity of protein sequence and structure must be considered to enable accurate, reproducible, and generalizable conclusions from structural analyses.

genetics↗