Biology subjects

Meiler, J.

Publications and source records attributed to Meiler, J..

80 records · Page 5Linked to original sources

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↗

bioRxiv2019

Protein fold prediction using simulated DEER distance distributions and decay traces

Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy (SDSL-EPR) to improve protein structure prediction and refinement outcomes. However, existing computational implementations must choose between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. Existing methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and may contain artifacts introduced during this processing step. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating distance distributions and echo decay traces between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters, allowing RosettaDEER to discriminate between poorly-folded and native-like models even when the time window of EPR data collection is truncated, rendering them unsuitable for accurate transformation into distance distributions. Finally, we demonstrate that one decay trace per nine residues is sufficient to predict the folds of Bax and the C-terminus of ExoU, two soluble proteins with surface-exposed amphipathic structural features that prevent the Rosetta energy function from correctly identifying native-like models in the absence of experimental data. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.

biophysics↗

bioRxiv2018

The impact of natural selection on the evolution and function of placentally expressed galectins

Immunity genes have repeatedly experienced natural selection during mammalian evolution. Galectins are carbohydrate-binding proteins that regulate diverse immune responses, including maternal-fetal immune tolerance in placental pregnancy. Seven human galectins, four conserved across vertebrates and three specific to primates, are involved in placental development. To comprehensively study the molecular evolution of these galectins both across mammals and within humans, we conducted a series of between-and within-species evolutionary analyses. By examining patterns of sequence evolution between species, we found that primate-specific galectins showed uniformly high substitution rates, whereas two of the four other galectins experienced accelerated evolution in primates. By examining human population genomic variation, we found that galectin genes and variants, including variants previously linked to immune diseases, showed signatures of recent positive selection in specific human populations. By examining one nonsynonymous variant in Galectin-8 previously associated with autoimmune diseases, we further discovered that it is tightly linked to three other nonsynonymous variants; surprisingly, the global frequency of this four-variant haplotype is [~]50%. To begin understanding the impact of this major haplotype on Galectin-8 protein structure, we modeled its 3D protein structure and found that it differed substantially from the reference protein structure. These results suggest that placentally expressed galectins experienced both ancient and more recent selection in a lineage-and population-specific manner. Furthermore, our discovery that the major Galectin-8 haplotype is structurally distinct from and more commonly found than the reference haplotype illustrates the significance of understanding the evolutionary processes that sculpted variants associated with human genetic disease.

evolutionary biology↗

bioRxiv2018

Protein-Ligand Docking with Protein-based and Ligand-based Structure Activity Relationships

Protein-small molecule docking programs predict the interaction interface and energy between a given protein target and a small molecule ligand. The accuracy of docking predictions generally improve with the guidance of experimentally derived restraints. One available source of such restraints is structure-activity relationships (SARs). SARs provide information on changes in binding affinity or biological response corresponding to a chemical change in the protein and/or ligand. These chemical changes frequently refer to amino acid mutations on the protein side and functional group modifications on the ligand side. Theoretically, predicted interaction energies should correlate with SARs though in practice, this is challenging due to the difficulties in scoring protein-ligand interactions. We have previously developed RosettaLigandEnsemble (RLE), a protein-ligand docking method that simultaneously docks a congeneric ligand series to a single protein target. RLE is capable of identifying native-like binding modes for a ligand series that match the available ligand SARs. This work in progress reports on the extension of RLE to factor in SARs derived from protein mutagenesis data. The new method, ProteinLigEnsemble (PLE), is also part of the Rosetta Biomolecular Modeling Suite available at https://www.rosettacommons.org/. We have also developed protein ensemble docking features that allow for docking or screening against multiple receptor variants at the same time. We have included a proof of concept study and a tutorial for interested users.

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↗

bioRxiv2017

High Throughput Functional Evaluation of KCNQ1 Decrypts Variants of Unknown Significance

BackgroundThe explosive growth in known human gene variation presents enormous challenges to current approaches for variant classification that impact diagnosis and treatment of many genetic diseases. For disorders caused by mutations in cardiac ion channels, such as congenital long-QT syndrome (LQTS), in vitro electrophysiological evidence has high value in discriminating pathogenic from benign variants, but these data are often lacking because assays are cost-, time- and labor-intensive.\n\nMethods and ResultsWe implemented a strategy for performing high throughput, functional evaluations of ion channel variants that repurposed an automated electrophysiology platform developed previously for drug discovery. We demonstrated success of this approach by evaluating 78 variants in KCNQ1, a major LQTS gene. We benchmarked our results with traditional electrophysiological approaches and observed a high level of concordance. Our results provided functional data useful for classifying ~70% of previously unstudied KCNQ1 variants annotated with uninformative descriptions in the public database ClinVar. Further, we show that rare and ultra-rare KCNQ1 variants in the general population exhibit functional properties ranging from normal to severe loss-of-function indicating that allele frequency is not a reliable predictor of channel function.\n\nConclusionsOur results illustrate an efficient and high throughput paradigm linking genotype to function for a human cardiac channelopathy that will enable data-driven classification of large numbers of variants and create new opportunities for precision medicine.

genetics↗

bioRxiv2017

Comprehensive Analysis of Constraint on the Spatial Distribution of Missense Variants in Human Protein Structures

The spatial distribution of genetic variation within proteins is shaped by evolutionary constraint and thus can provide insights into the functional importance of protein regions and the potential pathogenicity of protein alterations. Here, we comprehensively evaluate the 3D spatial patterns of constraint on human germline and somatic variation in 4,568 solved protein structures. Different classes of coding variants have significantly different spatial distributions. Neutral missense variants exhibit a range of 3D constraint patterns, with a general trend of spatial dispersion driven by constraint on core residues. In contrast, germline and somatic disease-causing variants are significantly more likely to be clustered in protein structure space. We demonstrate that this difference in the spatial distributions of disease-associated and benign germline variants provides a signature for accurately classifying variants of unknown significance (VUS) that is complementary to current approaches for VUS classification. We further illustrate the clinical utility of our approach by classifying new mutations identified from patients with familial idiopathic pneumonia (FIP) that segregate with disease.

genetics↗