Biology subjects

Kuenze, G.

Publications and source records attributed to Kuenze, G..

16 recordsLinked to original sources

bioRxiv2026

SNPstar: A Web Server Linking Allelic Variation to Protein Structure and Function in Arabidopsis thaliana

Understanding how natural genetic variants affect protein structure and function is central to plant biology. The 1001 Genomes Project has catalogued millions of single nucleotide polymorphisms (SNPs) across more than a thousand Arabidopsis thaliana accessions, offering an unprecedented opportunity to relate sequence variation to three-dimensional protein structure and population context. Yet, realizing it requires tools that integrate these scales in one accessible framework. Here we present SNPstar, a web server that links allelic variation in A. thaliana to AlphaFold3-predicted structures through a gene-centric, interactive interface. SNPstar annotates each variant with descriptive features, thermodynamic stability estimates, protein domain context, and genome-wide association results, and computes haplotypes and proteotypes that group accessions by shared DNA or protein sequence. Researchers can characterize variants, visualize their structural context, map their geographic distribution, and prioritize accessions for experimental validation without local computational infrastructure. We demonstrate SNPstar with two case studies. The first recapitulates known loss-of-function variation in the cadmium transporter HMA3, validating that SNPstar prioritizes functionally consequential alleles. The second uses SNPstar-defined proteotypes to identify an N-terminal SNP combination in ARGONAUTE 2 that distinguishes accessions differing in in vitro siRNA-directed target cleavage, linking protein-coding variation to a measurable molecular phenotype. SNPstar thus helps translate natural variation into mechanistic insight.

plant biology↗

bioRxiv2026

Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks

Enzyme activity and stability are strongly modulated by pH, making the catalytic pH optimum (pH opt) a key parameter in enzyme development and biotechnological applications. Experimental determination of pH opt is, however, labor-intensive and time-consuming, motivating the development of accurate computational prediction methods. Here, we introduce pHoptNN, an E(n)-equivariant graph neural network designed to predict enzyme pH opt directly from three-dimensional protein structures. pHoptNN was trained on a curated dataset comprising nearly 12,000 enzymes with experimentally determined pH opt values and high-confidence structural models obtained from the Protein Data Bank and AlphaFold3. The model represents enzymes as atomic-level molecular graphs, integrating structural, chemical, and electrostatic features. Model development was guided by extensive hyperparameter optimization using genetic and Bayesian search strategies. On a held-out test set, pHoptNN achieved a root-mean-square error (RMSE) of 0.588 pH units, substantially outperforming the sequence-based method EpHod (RMSE = 0.879). Moreover, pHoptNN maintains robust predictive performance across different enzyme classes and pH ranges. These results demonstrate the utility of structure-based equivariant deep learning for enzyme pH opt prediction and highlight the potential of pHoptNN to accelerate enzyme discovery and engineering workflows.

bioinformatics↗

bioRxiv2024

Computer-guided design of Z domain peptides with improved inhibition of VEGF

Computational protein design is becoming increasingly helpful in the development of new protein therapeutics with enhanced efficacy, specificity, and minimal side effects, for precise modulation of biological pathways. In vascular biology, the interaction between vascular endothelial growth factor A (VEGFA) and its receptors (VEGFR1-R3) is a pivotal process underlying blood vessel growth. Dysregulation of this pathway contributes to diseases such as cancer and diabetic retinopathy. Existing VEGFA inhibitors are effective but have limitations, driving interest in peptide-based therapeutics. Peptide inhibitors offer advantages, including reduced toxicity, improved formulation flexibility, and enhanced stability. This study leverages computational tools, particularly ProteinMPNN and Rosetta, to design optimized peptide-based VEGFA inhibitors. Building on the existing peptide templates mini-Z-1 and Z-1-2, new sequences were computationally predicted and experimentally validated. A novel peptide with improved affinity (KD = 6.2 {micro}M) compared to mini-Z-1 (KD = 9.3 {micro}M) was found, requiring only one round of design and testing. The integration of ProteinMPNN and Rosetta enabled a rapid and cost-effective pipeline for designing potent VEGFA inhibitors, underscoring the potential of computational peptide design in developing next-generation therapeutics targeting angiogenesis-dependent diseases.

biochemistry↗

bioRxiv2023

Directing Protein Design Choices by Per-Residue Energy Breakdown Analysis with an Interactive Web Application

Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org.

bioinformatics↗

bioRxiv2022

Pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction

A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural information with atomic detail. We combine RosettaNMR with experimentally-derived paramagnetic NMR restraints to guide membrane protein structure prediction. We demonstrate this approach using the disulfide bond formation protein B (DsbB), an -helical IMP. We attached a cyclen-based paramagnetic lanthanide tag to an engineered noncanonical amino acid (ncAA) using a copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry reaction. Using this tagging strategy, we collected 203 backbone HN pseudocontact shifts (PCSs) for three different labeling sites and used these as input to guide de novo membrane protein structure prediction protocols in Rosetta. We find that this sparse PCS dataset combined with 44 long-range NOEs as restraints in our calculations improves structure prediction of DsbB by enhancements in model accuracy, sampling, and scoring. The most accurate DsbB models generated in this case gave C-RMSD values over the transmembrane region of 2.11 [A] (best-RMSD) and 3.23 [A] (best-scoring).

biophysics↗

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↗

bioRxiv2022

First crystal structure of a non-canonical amino acid linked to a paramagnetic lanthanide tag facilitates protein structure determination using NMR-derived restraints

Site-directed spin labeling of proteins via non-canonical amino acids (ncAAs) is a non-traditional method for the measurement of pseudocontact shifts (PCSs) by nuclear magnetic resonance (NMR) spectroscopy. PCSs provide long-range distance and orientational information between a paramagnetic center and protein nuclei that can be used as restraints for computational structural modeling techniques. Here, we present the first experimental structure of an ncAA chemically linked to a lanthanide tag conjugated to the protein, T4-Lysozyme (T4L). T4L was crystallized with a cyclen-based C3 tag coordinated to the paramagnetic ion terbium (Tb3+). The paramagnetic C3-lanthanide tag generated PCSs measured at four different ncAA sites. We show that the addition of these restraints improves structure prediction protocols for T4L using the RosettaNMR framework. Generated models provide insight into T4L conformational flexibility sampled in solution. This integrative modeling protocol is readily transferable to larger proteins. Methods to predict protein structures are advancing into an exciting arena such that reliable experimental data will play important roles for evaluating the biophysical relevance of predicted structural models. Our contribution here caters to the growing interest in using ncAAs for a range of biophysical studies, and these methods can be readily transferred to larger protein systems of interest.

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

Paramagnetic spin labeling of a bacterial DnaB helicase for solid-state NMR

Labeling of biomolecules with a paramagnetic probe for nuclear magnetic resonance (NMR) spectroscopy enables determining long-range distance restraints, which are otherwise not accessible by classically used dipolar coupling-based NMR approaches. Distance restraints derived from paramagnetic relaxation enhancements (PREs) can facilitate the structure determination of large proteins and protein complexes. We herein present the site-directed labeling of the large oligomeric bacterial DnaB helicase from Helicobacter pylori with cysteine-reactive maleimide tags carrying either a nitroxide radical or a lanthanide ion. The success of the labeling reaction was followed by quantitative continuous-wave electron paramagnetic resonance (EPR) experiments performed on the nitroxide-labeled protein. PREs were extracted site-specifically from 2D and 3D solid-state NMR spectra. A good agreement with predicted PRE values, derived by computational modeling of nitroxide and Gd3+ tags in the low-resolution DnaB crystal structure, was found. Comparison of experimental PREs and model-predicted spin label-nucleus distances indicated that the size of the "blind sphere" around the paramagnetic center, in which NMR resonances are not detected, is slightly larger for Gd3+ (~14 [A]) than for nitroxide (~11 [A]) in 13C-detected 2D spectra of DnaB. We also present Gd3+-Gd3+ dipolar electron-electron resonance EPR experiments on DnaB supporting the conclusion that DnaB was present as a hexameric assembly.

biophysics↗

bioRxiv2021

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

bioinformatics↗

bioRxiv2020

Disease-Linked Super-Trafficking of a Mutant Potassium Channel

Gain-of-function (GOF) mutations in the KCNQ1 voltage-gated potassium channel can induce cardiac arrhythmia. We tested whether any of the known GOF disease mutations in KCNQ1 act by increasing the amount of KCNQ1 that reaches the cell surface--"super-trafficking". We found that levels of R231C KCNQ1 in the plasma membrane are 5-fold higher than wild type KCNQ1. This arises from both enhanced translocon-mediated membrane integration of the S4 voltage-sensor helix and an energetic linkage of C231 with the V129 and F166 side chains. Whole-cell electrophysiology recordings confirmed that R231C KCNQ1 in complex with KCNE1 is constitutively active, but also revealed the single channel activity of this mutant to be only 20% that of WT. The GOF phenotype associated with R231C therefore reflects the net effects of super-trafficking, reduced single channel activity, and constitutive channel activation. These investigations document membrane protein super-trafficking as a contributing mechanism to human disease.

biophysics↗

bioRxiv2020

Structural Determinants of Cholesterol Recognition in Helical Integral Membrane Proteins

Cholesterol (CLR) is an integral component of mammalian membranes. It has been shown to modulate membrane dynamics and alter integral membrane protein (IMP) function. However, understanding the molecular mechanisms of these processes is complicated by limited and conflicting structural data: Specifically, in co-crystal structures of CLR-IMP complexes it is difficult to distinguish specific and biologically relevant CLR-IMP interactions from a nonspecific association captured by the crystallization process. The only widely recognized search algorithm for CLR-IMP interaction sites is sequence-based, i.e. searching for the so-called CRAC or CARC motifs. While these motifs are present in numerous IMPs, there is inconclusive evidence to support their necessity or sufficiency for CLR binding. Here we leverage the increasing number of experimental CLR-IMP structures to systematically analyze putative interaction sites based on their spatial arrangement and evolutionary conservation. From this analysis we create three-dimensional representations of general CLR interaction sites that form clusters across multiple IMP classes and classify them as being either specific or nonspecific. Information gleaned from our characterization will eventually enable a structure-based approach for prediction and design of CLR-IMP interaction sites. SIGNIFICANCECLR plays an important role in composition and function of membranes and often surrounds and interacts with IMPs. It is a daunting challenge to disentangle CLRs dual roles as a direct modulator of IMP function through binding or indirect actor as a modulator of membrane plasticity. Only recently studies have delved into characterizing specific CLR-IMP interactions. We build on this previous work by using a combination of structural and evolutionary characteristics to distinguish specific from nonspecific CLR interaction sites. Understanding how CLR interacts with IMPs will underpin future development towards detecting and engineering CLR-IMP interaction sites.

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

Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13

Computational methods that produce accurate protein structure models from limited experimental data, e.g. from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a blind test to explore the capabilities and limitations of current modeling techniques in leveraging NMR data which had high sparsity, ambiguity and error rate for protein structure prediction. We describe our approach to predict the structure of these proteins leveraging the Rosetta software suite. Protein structure models were predicted de novo using a two-stage protocol. First, low-resolution models were generated with the Rosetta de novo method guided by non-ambiguous nuclear Overhauser effect (NOE) contacts and residual dipolar coupling (RDC) restraints. Second, iterative model hybridization and fragment insertion with the Rosetta comparative modeling method was used to refine and regularize models guided by all ambiguous and non-ambiguous NOE contacts and RDCs. Nine out of 16 of the Rosetta de novo models had the correct fold (GDT-TS score >45) and in three cases high-resolution models were achieved (RMSD <3.5 [A]). We also show that a meta-approach applying iterative Rosetta+NMR refinement on server-predicted models which employed non-NMR-contacts and structural templates leads to substantial improvement in model quality. Integrating these data-assisted refinement strategies with innovative non-data-assisted approaches which became possible in CASP13 such as high precision contact prediction will in the near future enable structure determination for large proteins that are outside of the realm of conventional NMR.

biophysics↗

bioRxiv2019

Integrative protein modeling in RosettaNMR from sparse paramagnetic restraints

Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals hold great potential to study larger proteins and complexes. Additionally, computational methods designed to work with sparse data add atomic detail that is missing in the experimental restraints, allowing application to systems that are difficult to investigate. While specific frameworks in the Rosetta macromolecular modeling suite support the use of certain NMR restraint types, use of all commonly measured restraint types together is precluded. Here, we introduce a comprehensive framework into Rosetta that reconciles CS-Rosetta, PCS-Rosetta and RosettaNMR into a single framework, that, in addition to backbone chemical shifts and nuclear Overhauser effect distance restraints, leverages NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements, measured at multiple tagging sites. We further showcase the generality of RosettaNMR for various modeling challenges and benchmark it on 28 structure prediction cases, eight symmetric assemblies, two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models ([≤] 2[A]) in 50% of the cases.\n\nSignificance StatementComputational methods such as Rosetta can assist NMR structure determination by employing efficient conformational search algorithms alongside physically realistic energy functions to model protein structure from sparse experimental data. We have developed a framework in Rosetta that leverages paramagnetic NMR data in addition to chemical shift and nuclear Overhauser effect restraints and extends RosettaNMR calculations to the prediction of symmetric assemblies, protein-protein and protein-ligand complexes. RosettaNMR generated high-accuracy models ([≤] 2[A]) in 50% of cases for a benchmark set of 28 monomeric and eight symmetric proteins and predicted protein-protein and protein-ligand interfaces with up to 1[A] accuracy. The method expands Rosettas rich toolbox for integrative data-driven modeling and promises to be broadly useful in structural biology.

biophysics↗

bioRxiv2017

Mechanisms of KCNQ1 Channel Dysfunction in Long QT Syndrome Involving Voltage Sensor Domain Mutations

Loss-of-function (LOF) mutations in human KCNQ1 are responsible for susceptibility to a life-threatening heart rhythm disorder, the congenital long-QT syndrome (LQTS). Hundreds of KCNQ1 mutations have been identified, but the molecular mechanisms responsible for impaired function are poorly understood. Here, we investigated the impact of 51 KCNQ1 variants located within the voltage sensor domain (VSD), with an emphasis on elucidating effects on cell surface expression, protein folding and structure. For each variant, the efficiency of trafficking to the plasma membrane, the impact of proteasome inhibition, and protein stability were assayed. The results of these experiments, combined with channel functional data, provided the basis for classifying each mutation into one of 6 mechanistic categories. More than half of the KCNQ1 LOF mutations destabilize the structure of the VSD, resulting in mistrafficking and degradation by the proteasome, an observation that underscores the growing appreciation that mutation-induced destabilization of membrane proteins may be a common human disease mechanism. Finally, we observed that 5 of the folding-defective LQTS mutants are located in the VSD S0 helix, where they interact with a number of other LOF mutation sites in other segments of the VSD. These observations reveal a critical role for the S0 helix as a central scaffold to help organize and stabilize the KCNQ1 VSD and, most likely, the corresponding domain of many other ion channels.\n\nOne Sentence SummaryLong QT syndrome-associated mutations in KCNQ1 most often destabilize the protein, leading to mistrafficking and degradation.

biochemistry↗