Biology subjects

Meiler, J.

Publications and source records attributed to Meiler, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2021

An active learning framework improves tumor variant interpretation

For precision medicine to reach its full potential for treatment of cancer and other diseases, protein variant effect prediction tools are needed that characterize variants of unknown significance (VUS) in a patients genome with respect to their likelihood to influence treatment response and outcomes. However, the performance of most variant prediction tools is limited by the difficulty of acquiring sufficient training and validation data. To overcome these limitations, we applied an iterative active learning approach starting from available biochemical, evolutionary, and functional annotations. The potential of active learning to improve variant interpretation was first demonstrated by applying it to synthetic and deep mutational scanning (DMS) datasets for four cancer-relevant proteins. We then probed its utility to guide interpretation and functional validation of tumor VUS in a potential biomarker for cancer therapy sensitivity, the nucleotide excision repair (NER) protein Xeroderma Pigmentosum Complementation Group A (XPA). A quantitative high-throughput cell-based NER activity assay, fluorescence-based multiplex flow-cytometric host cell reactivation (FM-HCR), was used to validate XPA VUS selected by the active learning strategy. In all cases, selecting VUS for validation by active learning yielded an improvement in performance over traditional learning. These analyses suggest that active learning is well-suited to significantly improve interpretation of VUS and cancer patient genomes.

cancer biology↗

bioRxiv2021

Mapping of the fibrinogen-binding site on the staphylocoagulase C-terminal repeat region

The N-terminus of S. aureus staphylocoagulase (SC) triggers activation of host prothrombin (ProT), and the SC{middle dot}ProT* complex cleaves host fibrinogen (Fbg) to form fibrin (Fbn) deposits, a hallmark of SC-positive endocarditis. The C-terminal domain of the prototypical Newman D2 Tager 104 SC contains 1 pseudo-repeat (PR) and 7 repeats (R1[->]R7) that bind Fbg/Fbn Fragment D (Frag D). This work defines affinities and stoichiometries of Frag D binding to single- and multi-repeat C-terminal constructs, using fluorescence equilibrium binding, NMR titration, Ala scanning, and native PAGE. Constructs containing PR and each single repeat bound Frag D with KD ~50 - 130 nM and a 1:1 stoichiometry, indicating a conserved binding site shared between PR and each repeat. NMR titration of PR-R7 with Frag D revealed that residues 22-49, bridging PR and R7, constituted the minimal peptide (MP) required for binding, corroborated by Ala scanning, and binding of labeled MP to Frag D. MP alignment with the PR-repeat and inter-repeat junctions identified conserved residues critical for binding. Labeled PR-(R1[->]R7) bound Frag D with KD ~7 - 32 nM and stoichiometry of 1:5; and PR-R1R2R3, PR-R1R6R7, PR-R3R4R7, and PR-R3R6R7 competed with PR-(R1[->]R7) for Frag D binding, with a 1:3 stoichiometry and KD ~7 - 42 nM. These findings are consistent with binding at the PR-R junctions with modest inter-repeat sequence variability. Circular dichroism of PR-R7 and PR-(R1[->]R7) suggested a largely disordered structure and conformational flexibility, allowing binding of multiple fibrin(ogen) molecules. This property facilitates pathogen localization on host fibrin networks.

biochemistry↗

bioRxiv2021

The allosteric activation of α7 nAChR by α-conotoxin MrIC is modified by mutations at the vestibular site

-conotoxins are 13-19 amino acid toxin peptides that bind various nicotinic acetylcholine receptor (nAChR) subtypes. -conotoxin Mr1.7c (MrIC) is a 17 amino acid peptide that targets 7 nAChR. Although MrIC has no activating effect on 7 nAChR when applied by itself, it evokes a large response when co-applied with the type II positive allosteric modulator PNU-120596, which potentiates 7 nAChR response by recovering it from a desensitized state. Lack of standalone activity despite activation upon co-application with a positive allosteric modulator was previously observed for molecules that bind to an extracellular domain allosteric activation (AA) site at the vestibule of the receptor. We hypothesized that MrIC may activate 7 nAChR allosterically through this site. We ran voltage-clamp electrophysiology experiments and in silico peptide docking calculations to gather evidence in support of 7 nAChR activation by MrIC through the AA site. The experiments with the wild-type 7 nAChR supported an allosteric mode of action, which was confirmed by the increased MrIC + PNU-120596 responses of three 7 nAChR AA site mutants that were designed in silico to improve MrIC binding. Overall, our results shed light on allosteric activation of 7 nAChR by MrIC and suggest involvement of the AA site. Significance Statement-conotoxin MrIC (MrIC) is an allosteric agonist of the 7 nicotinic acetylcholine receptor (nAChR). This mode of action is unique among -conotoxins since these peptides typically act as orthosteric antagonists of nAChR. However, the mechanism of 7 nAChR activation by MrIC has been elusive so far. This work demonstrates that activation by MrIC is independent of the 7 nAChR orthosteric site and is related to a vestibular allosteric activation site at the extracellular domain of the receptor. Our experimental and computational studies identified the residues that play a role in allosteric activation and confirmed the utility of ensemble docking methods in understanding peptide - nAChR interactions, thus providing a basis for the design of peptides for allosteric modulation of nAChR.

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↗

bioRxiv2021

Systematic Profiling of Temperature- and Retinal-Sensitive Rhodopsin Variants by Deep Mutational Scanning

Membrane protein variants with diminished conformational stability often exhibit enhanced cellular expression at reduced growth temperatures. The expression of "temperature-sensitive" variants is also typically sensitive to corrector molecules that bind and stabilize the native conformation. In this work, we employ deep mutational scanning to compare the effects of reduced growth temperature and an investigational corrector (9-cis-retinal) on the plasma membrane expression of 700 rhodopsin variants in HEK293T cells. We find that the change in expression at reduced growth temperatures is correlated with the response to retinal among variants bearing mutations within a hydrophobic transmembrane domain (TM2). The most sensitive variants within this helix appear to disrupt a network of hydrogen bonds that stabilizes a native helical kink. By comparison, mutants that alter a polar transmembrane domain (TM7) exhibit weaker responses to temperature and retinal that are poorly correlated. Statistical analyses suggest this insensitivity primarily arises from an abundance of mutations that enhance its membrane integration, stabilize its native conformation, and/ or perturb the retinal binding pocket. Finally, we show that the characteristics of purified temperature- and retinal-sensitive variants suggest that the proteostatic effects of retinal may be manifested during translation and cotranslational folding. Together, our findings elucidate various factors that mediate the sensitivity of genetic variants to temperature and to small molecule correctors.

biophysics↗

bioRxiv2021

Psychomotor impairments and therapeutic implications revealed by a mutation linked with Infantile Parkinsonism-Dystonia

Parkinson disease (PD) is a progressive, neurodegenerative disorder affecting over 6.1 million people worldwide. Although the cause of PD remains unclear, studies of highly-penetrant mutations identified in early-onset familial parkinsonism have contributed to our understanding of the molecular mechanisms underlying disease pathology. Dopamine (DA) transporter (DAT) deficiency syndrome (DTDS) is a distinct type of infantile parkinsonism-dystonia that shares key clinical features with PD, including motor deficits (progressive bradykinesia, tremor, hypomimia) and altered DA neurotransmission. Here, we define structural, functional, and behavioral consequences of a Cys substitution at R445 in human DAT (hDAT R445C), identified in a patient with DTDS. We found that this R445 substitution disrupts a phylogenetically conserved intracellular (IC) network of interactions that compromise the hDAT IC gate. This is demonstrated by both Rosetta molecular modeling and fine-grained simulations using hDAT R445C, as well as EPR analysis and X-ray crystallography of the bacterial homolog leucine transporter. Notably, the disruption of this IC network of interactions supported a channel-like intermediate of hDAT and compromised hDAT function. We demonstrate that Drosophila melanogaster expressing hDAT R445C show impaired hDAT activity, which is associated with DA dysfunction in isolated brains and with abnormal behaviors monitored at high-speed time resolution. We show that hDAT R445C Drosophila exhibit motor deficits, lack of motor coordination (i.e. flight coordination) and phenotypic heterogeneity in these behaviors that is typically associated with DTDS and PD. These behaviors are linked with altered dopaminergic signaling stemming from loss of DA neurons and decreased DA availability. We rescued flight coordination through enhanced DAT surface expression via the lysosomal inhibitor chloroquine. Together, these studies shed light on how a DTDS-linked DAT mutation underlies DA dysfunction and, more broadly, the clinical phenotypes shared by DTDS and PD.

neuroscience↗

bioRxiv2021

Mapping the Promiscuous Binding Interface of HOX-A11 with KIX by Experimentally Guided in-silico docking

Transcription factors (TFs) regulate levels of transcription through a complex array of protein-protein interactions, thereby controlling key physiological processes such as development, stress response and cell growth. The transcription factor HOXA11 contains an intrinsically disordered regions (IDR) through which it interacts with CREB binding protein (CBP) and regulates endometrial development and function in eutherian mammals. The interaction between the IDR of HOXA11 and CBP was analyzed using computational docking guided by experimental constraints. HOXA11 IDR interacts with the KIX domain of CBP at two discrete sites - MLL and cMyb, mediated by sticky hydrophobic grooves on the surface of KIX. A five residue motif FDQFF on HOXA11 can interact both at cMyb and MLL site of KIX resulting in a promiscuous binding. Author SummaryWe demonstrate how the intrinsically disordered region (IDR) of transcription factor HOXA11 interacts at two distinct sites of the transcription coactivator CREB binding protein (CBP). By combining computational docking with limited experimental data we construct models of the complex of the KIX domain within CBP and a short helical segment within the IDR of HOXA11. The interaction between HOXA11 and CBP is believed to trigger the downstream expression of genes important in embryonic development.

biophysics↗

bioRxiv2020

A multi-task deep-learning system for predicting membrane associations and secondary structures of proteins

Accurate prediction of secondary structures and transmembrane segments is often the first step towards modeling the tertiary structure of a protein. Existing methods are either specialized in one class of proteins or developed to predict one type of 1D structural attributes (secondary structure, topology, or transmembrane segment). In this work, we develop a new method for simultaneous prediction of secondary structure, transmembrane segment, and transmembrane topology with no a priori assumption on the class of the input protein sequence. The new method, Membrane Association and Secondary Structures of Proteins (MASSP) predictor, uses multi-tiered neural networks that incorporate recent innovations in machine learning. The first tier is a multi-task multi-layer convolutional neural network (CNN) that learns patterns in image-like input position-specific-scoring matrices (PSSMs) and predicts residue-level 1D structural attributes. The second tier is a long short-term memory (LSTM) neural network that treats the predictions of the first tier from the perspective of natural language processing and predicts the class of the input protein sequence. We curated a non-redundant data set consisting of 54 bitopic, 241 multi-spanning TM-alpha, 77 TM-beta, and 372 soluble proteins, respectively for training and testing MASSP. For secondary structure prediction, the mean three-state accuracy (Q3) of MASSP is 0.830, better than the Q3 of PSIPRED (0.829) and that of SPINE-X (0.813) and substantially better than that of Jufo9D (0.762) and RaptorX-Property (0.741). The mean segment overlap score (SOV) of MASSP is 0.752, gaining at least 7.7% improvement over all the other four methods. For transmembrane topology prediction, MASSP has a performance comparable to OCTOPUS and substantially better than MEMSAT3 and TMHMM2 on TM-alpha proteins, and on TM-beta proteins, MASSP is significantly better than both BOCTOPUS2 and PRED-TMBB2. By integrating prediction of secondary structure and transmembrane segments in a deep-learning framework, MASSP improves performance over previous methods, has broader applicability, and enables proteome scale predictions.

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

Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions

Computer-aided drug discovery is an essential component of modern drug development. Therein, deep learning has become an important tool for rapid screening of billions of molecules in silico for potential hits containing desired chemical features. Despite its importance, substantial challenges persist in training these models, such as severe class imbalance, high decision thresholds, and lack of ground truth labels in some datasets. In this work we argue in favor of directly optimizing the receiver operating characteristic (ROC) in such cases, due to its robustness to class imbalance, its ability to compromise over different decision thresholds, certain freedom to influence the relative weights in this compromise, fidelity to typical benchmarking measures, and equivalence to positive/unlabeled learning. We also propose new training schemes (coherent mini-batch arrangement, and usage of out-of-batch samples) for cost functions based on the ROC, as well as a cost function based on the logAUC metric that facilitates early enrichment (i.e. improves performance at high decision thresholds, as often desired when synthesizing predicted hit compounds). We demonstrate that these approaches outperform standard deep learning approaches on a series of PubChem high-throughput screening datasets that represent realistic and diverse drug discovery campaigns on major drug target families.

pharmacology and toxicology↗

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↗

bioRxiv2020

Prediction of amphipathic helix - membrane interactions with Rosetta

Amphipathic helices have hydrophobic and hydrophilic/charged residues situated on opposite faces of the helix. They can anchor peripheral membrane proteins to the membrane, be attached to integral membrane proteins, or exist as independent peptides. Despite the widespread presence of membrane-interacting amphipathic helices, there is no computational tool within Rosetta to model their interactions with membranes. In order to address this need, we developed the AmphiScan protocol with PyRosetta, which runs a grid search to find the most favorable position of an amphipathic helix with respect to the membrane. The performance of the algorithm was tested in benchmarks with the RosettaMembrane, ref2015_memb, and franklin2019 score functions on six engineered and 44 naturally-occurring amphipathic helices using membrane coordinates from the OPM and PDBTM databases, OREMPRO server, and MD simulations for comparison. The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3[A] of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. Overall, AmphiScan stands as fast, accurate, and highly-customizable protocol that can be pipelined with other Rosetta and Python applications.

biophysics↗

bioRxiv2020

Identifying digenic disease genes using machine learning in the undiagnosed diseases network

Rare diseases affect hundreds of millions of people worldwide, and diagnosing their genetic causes is challenging. The Undiagnosed Diseases Network (UDN) was formed in 2014 to identify and treat novel rare genetic diseases, and despite many successes, more than half of UDN patients remain undiagnosed. The central hypothesis of this work is that many unsolved rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating even just pairs of variants for potential to cause disease is currently infeasible. To address this challenge, we developed DiGePred, a random forest classifier for identifying candidate digenic disease gene pairs using features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier using DIDA, the largest available database of known digenic disease causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN patients. DiGePred achieved high precision and recall in cross-validation and on a held out test set (PR area under the curve >77%), and we further demonstrate its utility using novel digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings like the UDN. Finally, to facilitate the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work facilitates the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

bioinformatics↗

bioRxiv2020

Critical Residues of Gβγ for the interaction with the SNARE Complex

The mechanisms and regulation of neurotransmitter release is a complex process involving many co-factors and proteins. One critical interaction is the regulation of exocytosis when G-protein {beta}{gamma} (G{beta}{gamma}) dimers bind to the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) protein complex. The complex is comprised of N-ethylmaleimide-sensitive factor attachment protein-25 (SNAP-25), syntaxin 1A, and synaptobrevin. Herein we probe across the entire family of human G{beta} and G{gamma} proteins for residues critical for the interaction with SNARE, by systematically screening peptide sequences for their ability to bind to tSNARE. The coiled-coil region of G{beta}{gamma} showed high affinity to tSNARE, along with the propeller region of G{beta} on the opposite side from the coiled-coil region. Peptides based on G{beta}1{gamma}2, shown to have high affinity to SNARE, tSNARE were screened further by alanine scanning to probe for residues critical for binding to tSNARE. Full length G{beta}1{gamma}2 and SNARE were docked computationally using Rosetta, to examine the experimentally determined binding sites. Docking converged on two possible sites of interaction using two distinct regions of both G{beta}1{gamma}2 and SNARE.

biochemistry↗

bioRxiv2019

RosettaGPCR: Multiple Template Homology Modeling of GPCRs with Rosetta

G-protein coupled receptors (GPCRs) represent a significant target class for pharmaceutical therapies. However, to date, only about 10% of druggable GPCRs have had their structures characterized at atomic resolution. Further, because of the flexibility of GPCRs, alternative conformations remain to be modeled, even after an experimental structure is available. Thus, computational modeling of GPCRs is a crucial component for understanding biological function and to aid development of new therapeutics. Previous single- and multi-template homology modeling protocols in Rosetta often generated non-native-like conformations of transmembrane -helices and/or extracellular loops. Here we present a new Rosetta protocol for modeling GPCRs that is improved in two critical ways: Firstly, it uses a blended sequence- and structure-based alignment that now accounts for structure conservation in extracellular loops. Secondly, by merging multiple template structures into one comparative model, the best possible template for every region of a target GPCR can be used expanding the conformational space sampled in a meaningful way. This new method allows for accurate modeling of receptors using templates as low as 20% sequence identity, which accounts for nearly the entire druggable space of GPCRs. A model database of all non-odorant GPCRs is made available at www.rosettagpcr.org. Author SummaryStructure-based drug discovery is among the new technologies driving the development of next generation therapeutics. Inherent to this process is the availability of a protein structure for virtual screening. The most heavily drugged protein family, G-protein coupled receptors (GPCRs), however suffers from a lack of experimental structures that could hinder drug development. Technical challenges prevent the determination of every protein structure, so we turn to computational modeling to predict the structures of the remaining proteins. Again, traditional techniques fail due to the high divergence of this family. Here, we build on available methods specifically for the challenge of modeling GPCRs. This new method outperforms other methods and allows for the ability to accurately model nearly 90% of the entire GPCR family. We therefore generate a model database of all GPCRs (www.rosettagpcr.org) for use in future drug development.

bioinformatics↗

bioRxiv2019

Multi-State Design of Flexible Proteins Predicts Sequences Optimal for Conformational Change

Computational protein design of an ensemble of conformations for one protein - i.e., multi-state design - determines the side chain identity by optimizing the energetic contributions of that side chain in each of the backbone conformations. Sampling the resulting large sequence-structure search space limits the number of conformations and the size of proteins in multi-state design algorithms. Here, we demonstrated that the REstrained CONvergence (RECON) algorithm can simultaneously evaluate the sequence of large proteins that undergo substantial conformational changes, such as viral surface glycoproteins. Simultaneous optimization of side chain conformations across all conformations resulted in an increase of 30% to 40% in sequence conservation when compared to single-state designs. More importantly, the sampled sequence space of RECON designs resembled the evolutionary sequence space of functional proteins. This finding was especially true for sequence positions that require substantial changes in their local environment across an ensemble of conformations. To quantify this rewiring of contacts at a certain position in sequence and structure, we introduced a new metric designated contact proximity deviation that enumerates contact map changes. This measure allows mapping of global conformational changes into local side chain proximity adjustments, a property not captured by traditional global similarity metrics such as RMSD or local similarity metrics such as changes in {varphi} and {psi} angles.\n\nAuthor SummaryMulti-state design can be used to engineer proteins that need to exist in multiple conformations or that bind to multiple partner molecules. In essence, multi-state design selects a compromise of protein sequences that allow for an ensemble of protein conformations, or states, associated with a particular biological function. In this paper, we used the REstrained CONvergence (RECON) algorithm with Rosetta to show that multi-state design of flexible proteins predicts sequences optimal for conformational change, mimicking mutation preferences sampled in evolution. Modeling optimal local side chain physicochemical environments within an ensemble selected significantly more native-like sequences than selections performed when all conformations states are designed independently. This outcome was particularly true for amino acids whose local side chain environment change between conformations. To quantify such contact map changes, we introduced a novel metric to show that sequence conservation is dependent on protein flexibility, i.e., changes in local side chain environments between stated limit the space of tolerated mutations. Additionally, such positions in sequence and structure are more likely to be energetically frustrated, at least in some states. Importantly, we showed that multi-state design over an ensemble of conformations (space) can explore evolutionary tolerated sequence space (time), thus enabling RECON to not only design proteins that require multiple states for function but also predict mutations that might be tolerated in native proteins but have not yet been explored by evolution. The latter aspect can be important to anticipate escape mutations, for example in pathogens or oncoproteins.

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↗