Algorithm for selection of optimized EPR distance restraints for de novo protein structure determination

Inference of Joint Conformational Distributions from Separately Acquired Experimental Measurements

Flexible proteins serve vital roles in a multitude of biological processes. However, determining their full conformational ensembles is extremely difficult because this requires detailed knowledge about the heterogeneity of the protein's degrees of freedom. Label-based experiments such as double electron-electron resonance (DEER) are very useful in studying flexible proteins, as they provide distributional data on heterogeneity. These experiments are typically performed separately, so information about correlation between distributions is lost. We have developed a method to recover correlation information using nonequilibrium work estimates in molecular dynamics refinement. We tested this method on a simple model of an alternating-access transporter for which the true joint distributions are known, and it successfully recovered the true joint distribution. We also applied our method to the protein syntaxin-1a, where it discarded physically implausible conformations. Our method thus provides a way to recover correlation structure in separate experimental measurements of conformational ensembles and refines the resulting structural ensemble.

Molecular Dynamics Simulations Based on Newly Developed Force Field Parameters for Cu2+ Spin Labels Provide Insights into Double-Histidine-Based Double Electron-Electron Resonance

Electron paramagnetic resonance (EPR) in combination with the recently developed double-histidine (dHis)-based Cu²⁺ spin labeling has provided valuable insights into protein structure and conformational dynamics. To relate sparse distance constraints measured by EPR to protein fluctuations in solution, modeling techniques are needed. In this work, we have developed force field parameters for Cu²⁺-nitrilotriacetic and Cu²⁺-iminodiacetic acid spin labels. We employed molecular dynamics (MD) simulations to capture the atomic-level details of dHis-labeled protein fluctuations. The interspin distances extracted from 200 ns MD trajectories show good agreement with the experimental results. The MD simulations also illustrate the dramatic rigidity of the Cu²⁺ labels compared to the standard nitroxide spin label. Further, the relative orientations between spin-labeled sites were measured to provide insight into the use of double electron-electron resonance (DEER) methods for such labels. The relative mean angles, as well as the standard deviations of the relative angles, agree well in general with the spectral simulations published previously. The fluctuations of relative orientations help rationalize why orientation selectivity effects are minimal at X-band frequencies, but observable at the Q-band for such labels. In summary, the results show that by combining the experimental results with MD simulations precise information about protein conformations as well as flexibility can be obtained.

Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction

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 to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. These methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and contain uncertainties that may require manual refinement. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating double electron-electron resonance spectroscopy decay traces and distance distributions 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 even when the time window of data collection is truncated. As a result, RosettaDEER can discriminate between poorly folded and native-like models by using decay traces that cannot be accurately converted into distance distributions using regularized fitting approaches. Finally, using two challenging test cases, we demonstrate that RosettaDEER leverages these experimental data for protein fold prediction more effectively than previous methods. 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.

Modeling of spin–spin distance distributions for nitroxide labeled biomacromolecules

Combining CREST and MD simulations based on GFN-FF for the automated computation of distance distributions for nitroxide labeled (metallo-) proteins.

Structural Characterization of Biomolecules through Atomistic Simulations Guided by DEER Measurements

Double electron-electron resonance (DEER) is a popular technique that exploits attached spin labels to probe the collective dynamics of biomolecules in a native environment. Like most spectroscopic approaches, DEER detects an ensemble of states accounting for biomolecular dynamics as well as the labels' intrinsic flexibility. Hence, the DEER data alone do not provide high-resolution structural information. To disentangle this variability, we introduce a minimally biased simulation method to sample a structural ensemble that reproduces multiple experimental signals within the uncertainty. In contrast to previous approaches, our method targets the raw data themselves, and thereby it brings forth an unbiased molecular interpretation of the experiments. After validation on the T4 lysozyme, we apply this technique to interpret recent DEER experiments on a membrane transporter binding protein (VcSiaP). The results highlight the large-scale conformational movement that occurs upon substrate binding and reveal that the unbound VcSiaP is more open in solution than the X-ray structure.

Confidence Analysis of DEER Data and Its Structural Interpretation with Ensemble-Biased Metadynamics

Given its ability to measure multicomponent distance distributions between electron-spin probes, double electron-electron resonance (DEER) spectroscopy has become a leading technique to assess the structural dynamics of biomolecules. However, methodologies to evaluate the statistical error of these distributions are not standard, often hampering a rigorous interpretation of the experimental results. Distance distributions are often determined from the experimental DEER data through a mathematical method known as Tikhonov regularization, but this approach makes rigorous error estimates difficult. Here, we build upon an alternative, model-based approach in which the distance probability distribution is represented as a sum of Gaussian components, and use propagation of errors to calculate an associated confidence band. Our approach considers all sources of uncertainty, including the experimental noise, the uncertainty in the fitted background signal, and the limited time span of the data collection. The resulting confidence band reveals the most and least reliable features of the probability distribution, thereby informing the structural interpretation of DEER experiments. To facilitate this interpretation, we also generalize the molecular simulation method known as ensemble-biased metadynamics (EBMetaD). This method, originally designed to generate maximal-entropy structural ensembles consistent with one or more probability distributions, now also accounts for the uncertainty in those target distributions exactly as dictated by their confidence bands. After careful benchmarks, we demonstrate the proposed techniques using DEER results from spin-labeled T4 lysozyme.

The contribution of modern EPR to structural biology

Electron paramagnetic resonance (EPR) spectroscopy combined with site-directed spin labelling is applicable to biomolecules and their complexes irrespective of system size and in a broad range of environments. Neither short-range nor long-range order is required to obtain structural restraints on accessibility of sites to water or oxygen, on secondary structure, and on distances between sites. Many of the experiments characterize a static ensemble obtained by shock-freezing. Compared with characterizing the dynamic ensemble at ambient temperature, analysis is simplified and information loss due to overlapping timescales of measurement and system dynamics is avoided. The necessity for labelling leads to sparse restraint sets that require integration with data from other methodologies for building models. The double electron–electron resonance experiment provides distance distributions in the nanometre range that carry information not only on the mean conformation but also on the width of the native ensemble. The distribution widths are often inconsistent with Anfinsen's concept that a sequence encodes a single native conformation defined at atomic resolution under physiological conditions.

Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints

We critically test and validate the CS-Rosetta methodology for de novo structure prediction of α-helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase-1 (mPGES-1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X-ray structures are available, resolution-adapted structural recombination (RASREC) CS-Rosetta yields structures that are as close to the X-ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES-1 and Bacillus cereus TSPO, where only X-ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS-Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close-to-native structures were obtained with one randomly picked long-range NOEs for every 14, 31, 38, and 8 residues for full-length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES-1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS-Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812-826. © 2016 Wiley Periodicals, Inc.

Investigating the dynamic nature of the ABC transporters: ABCB1 and MsbA as examples for the potential synergies of MD theory and EPR applications

ABC transporters are primary active transporters found in all kingdoms of life. Human multidrug resistance transporter ABCB1, or P-glycoprotein, has an extremely broad substrate spectrum and confers resistance against chemotherapy drug treatment in cancer cells. The bacterial ABC transporter MsbA is a lipid A flippase and a homolog to the human ABCB1 transporter, with which it partially shares its substrate spectrum. Crystal structures of MsbA and ABCB1 have been solved in multiple conformations, providing a glimpse into the possible conformational changes the transporter could be going through during the transport cycle. Crystal structures are inherently static, while a dynamic picture of the transporter in motion is needed for a complete understanding of transporter function. Molecular dynamics (MD) simulations and electron paramagnetic resonance (EPR) spectroscopy can provide structural information on ABC transporters, but the strength of these two methods lies in the potential to characterise the dynamic regime of these transporters. Information from the two methods is quite complementary. MD simulations provide an all atom dynamic picture of the time evolution of the molecular system, though with a narrow time window. EPR spectroscopy can probe structural, environmental and dynamic properties of the transporter in several time regimes, but only through the attachment sites of an exogenous spin label. In this review the synergistic effects that can be achieved by combining the two methods are highlighted, and a brief methodological background is also presented.

Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX

Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9Å to 3.9Å and from 5.7Å to 3.3Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively.

Structure of an E. coli integral membrane sulfurtransferase and its structural transition upon SCN(-) binding defined by EPR-based hybrid method

Electron paramagnetic resonance (EPR)-based hybrid experimental and computational approaches were applied to determine the structure of a full-length E. coli integral membrane sulfurtransferase, dimeric YgaP, and its structural and dynamic changes upon ligand binding. The solution NMR structures of the YgaP transmembrane domain (TMD) and cytosolic catalytic rhodanese domain were reported recently, but the tertiary fold of full-length YgaP was not yet available. Here, systematic site-specific EPR analysis defined a helix-loop-helix secondary structure of the YagP-TMD monomers using mobility, accessibility and membrane immersion measurements. The tertiary folds of dimeric YgaP-TMD and full-length YgaP in detergent micelles were determined through inter- and intra-monomer distance mapping and rigid-body computation. Further EPR analysis demonstrated the tight packing of the two YgaP second transmembrane helices upon binding of the catalytic product SCN⁻, which provides insight into the thiocyanate exportation mechanism of YgaP in the E. coli membrane.

BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints

For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 Å for 27, better than 6 Å for 22, and better than 4 Å for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data.

A Straightforward Approach to the Analysis of Double Electron-Electron Resonance Data

Double electron-electron resonance (DEER) is now widely utilized to measure distance distributions in the 20-70Å range. DEER is frequently applied to biological systems that have multiple conformational states leading to complex distance distributions. These complex distributions raise issues regarding the best approach to analyze DEER data. A widely used method utilizes a priori background correction followed by Tikhonov regularization. Unfortunately, the underlying assumptions of this approach can impact the analysis. In this chapter, a method of analyzing DEER data is presented that is ideally suited to obtain these complex distance distributions. The approach allows the fitting of raw experimental data without a priori background correction as well as the rigorous determination of uncertainties for all fitting parameters. This same methodological approach can be used for the simultaneous or global analysis of multiple DEER data sets using variable ratios of a common set of components, thus allowing direct correlation of distance components with functionally relevant conformational and biochemical states. Examples are given throughout to highlight this robust fitting approach.

Navigating Membrane Protein Structure, Dynamics, and Energy Landscapes Using Spin Labeling and EPR Spectroscopy

A detailed understanding of the functional mechanism of a protein entails the characterization of its energy landscape. Achieving this ambitious goal requires the integration of multiple approaches including determination of high-resolution crystal structures, uncovering conformational sampling under distinct biochemical conditions, characterizing the kinetics and thermodynamics of transitions between functional intermediates using spectroscopic techniques, and interpreting and harmonizing the data into novel computational models. With increasing sophistication in solution-based and ensemble-oriented biophysical approaches such as electron paramagnetic resonance (EPR) spectroscopy, atomic resolution structural information can be directly linked to conformational sampling in solution. Here, we detail how recent methodological and technological advances in EPR spectroscopy have contributed to the elucidation of membrane protein mechanisms. Furthermore, we aim to assist investigators interested in pursuing EPR studies by providing an introduction to the technique, a primer on experimental design, and a description of the practical considerations of the method toward generating high quality data.

Tau binds to lipid membrane surfaces via short amphipathic helices located in its microtubule-binding repeats

Tau is a microtubule-associated protein that is genetically linked to dementia and linked to Alzheimer's disease via its presence in intraneuronal neurofibrillary tangle deposits, where it takes the form of aggregated paired helical and straight filaments. Although the precise mechanisms by which tau contributes to neurodegeneration remain unclear, tau aggregation is commonly considered to be a critical component of tau-mediated pathogenicity. Nevertheless, the context in which tau aggregation begins in vivo is unknown. Tau is enriched in membrane-rich neuronal structures such as axons and growth cones, and can interact with membranes both via intermediary proteins and directly via its microtubule-binding domain (MBD). Membranes efficiently facilitate tau aggregation in vitro, and may therefore provide a physiologically relevant context for nucleating tau aggregation in vivo. Furthermore, tau-membrane interactions may potentially play a role in tau's poorly understood normal physiological functions. Despite the potential importance of direct tau-membrane interactions for tau pathology and physiology, the structural mechanisms that underlie such interactions remain to be elucidated. Here, we employ electron spin resonance spectroscopy to investigate the secondary and long-range structural properties of the MBD of three-repeat tau isoforms when bound to lipid vesicles and membrane mimetics. We show that the membrane interactions of the tau MBD are mediated by short amphipathic helices formed within each of the MBD repeats in the membrane-bound state. To our knowledge, this is the first detailed elucidation of helical tau structure in the context of intact lipid bilayers. We further show, for the first time (to our knowledge), that these individual helical regions behave as independent membrane-binding sites linked by flexible connecting regions. These results represent the first (to our knowledge) detailed structural view of membrane-bound tau and provide insights into potential mechanisms for membrane-mediated tau aggregation. Furthermore, the results may have implications for the structural basis of tau-microtubule interactions and microtubule-mediated tau aggregation.

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

Assembly states of FliM and FliG within the flagellar switch complex

At the base of the bacterial flagella, a cytoplasmic rotor (the C-ring) generates torque and reverses rotation sense in response to stimuli. The bulk of the C-ring forms from many copies of the proteins FliG, FliM, and FliN, which together constitute the switch complex. To help resolve outstanding issues regarding C-ring architecture, we have investigated interactions between FliM and FliG from Thermotoga maritima with X-ray crystallography and pulsed dipolar ESR spectroscopy (PDS). A new crystal structure of an 11-unit FliG:FliM complex produces a large arc with a curvature consistent with the dimensions of the C-ring. Previously determined structures along with this new structure provided a basis to test switch complex assembly models. PDS combined with mutational studies and targeted cross-linking reveal that FliM and FliG interact through their middle domains to form both parallel and antiparallel arrangements in solution. Residue substitutions at predicted interfaces disrupt higher-order complexes that are primarily mediated by contacts between the C-terminal domain of FliG and the middle domain of a neighboring FliG molecule. Spin separations among multi-labeled components fit a self-consistent model that agree well with electron microscopy images of the C-ring. An activated form of the response regulator CheY destabilizes the parallel arrangement of FliM molecules to perturb FliG alignment in a process that may reflect the onset of rotation switching. These data suggest a model of C-ring assembly in which intermolecular contacts among FliG domains provide a template for FliM assembly and cooperative transitions.

RosettaEPR: rotamer library for spin label structure and dynamics

An increasingly used parameter in structural biology is the measurement of distances between spin labels bound to a protein. One limitation to these measurements is the unknown position of the spin label relative to the protein backbone. To overcome this drawback, we introduce a rotamer library of the methanethiosulfonate spin label (MTSSL) into the protein modeling program Rosetta. Spin label rotamers were derived from conformations observed in crystal structures of spin labeled T4 lysozyme and previously published molecular dynamics simulations. Rosetta’s ability to accurately recover spin label conformations and EPR measured distance distributions was evaluated against 19 experimentally determined MTSSL labeled structures of T4 lysozyme and the membrane protein LeuT and 73 distance distributions from T4 lysozyme and the membrane protein MsbA. For a site in the core of T4 lysozyme, the correct spin label conformation (Χ₁ and Χ₂) is recovered in 99.8% of trials. In surface positions 53% of the trajectories agree with crystallized conformations in Χ₁ and Χ₂. This level of recovery is on par with Rosetta performance for the 20 natural amino acids. In addition, Rosetta predicts the distance between two spin labels with a mean error of 4.4 Å. The width of the experimental distance distribution, which reflects the flexibility of the two spin labels, is predicted with a mean error of 1.3 Å. RosettaEPR makes full-atom spin label modeling available to a wide scientific community in conjunction with the powerful suite of modeling methods within Rosetta.

Conformational dynamics and distribution of nitroxide spin labels

Long-range distance measurements based on paramagnetic relaxation enhancement (PRE) in NMR, quantification of surface water dynamics near biomacromolecules by Overhauser dynamic nuclear polarization (DNP) and sensitivity enhancement by solid-state DNP all depend on introducing paramagnetic species into an otherwise diamagnetic NMR sample. The species can be introduced by site-directed spin labeling, which offers precise control for positioning the label in the sequence of a biopolymer. However, internal flexibility of the spin label gives rise to dynamic processes that potentially influence PRE and DNP behavior and leads to a spatial distribution of the electron spin even in solid samples. Internal dynamics of spin labels and their static conformational distributions have been studied mainly by electron paramagnetic resonance spectroscopy and molecular dynamics simulations, with a large body of results for the most widely applied methanethiosulfonate spin label MTSL. These results are critically discussed in a unifying picture based on rotameric states of the group that carries the spin label. Deficiencies in our current understanding of dynamics and conformations of spin labeled groups and of their influence on NMR observables are highlighted and directions for further research suggested.

Structural refinement from restrained-ensemble simulations based on EPR/DEER data: application to T4 lysozyme

DEER (double electron-electron resonance) is a powerful pulsed ESR (electron spin resonance) technique allowing the determination of distance histograms between pairs of nitroxide spin-labels linked to a protein in a native-like solution environment. However, exploiting the huge amount of information provided by ESR/DEER histograms to refine structural models is extremely challenging. In this study, a restrained ensemble (RE) molecular dynamics (MD) simulation methodology is developed to address this issue. In RE simulation, the spin-spin distance distribution histograms calculated from a multiple-copy MD simulation are enforced, via a global ensemble-based energy restraint, to match those obtained from ESR/DEER experiments. The RE simulation is applied to 51 ESR/DEER distance histogram data from spin-labels inserted at 37 different positions in T4 lysozyme (T4L). The rotamer population distribution along the five dihedral angles connecting the nitroxide ring to the protein backbone is determined and shown to be consistent with available information from X-ray crystallography. For the purpose of structural refinement, the concept of a simplified nitroxide dummy spin-label is designed and parametrized on the basis of these all-atom RE simulations with explicit solvent. It is demonstrated that RE simulations with the dummy nitroxide spin-labels imposing the ESR/DEER experimental distance distribution data are able to systematically correct and refine a series of distorted T4L structures, while simple harmonic distance restraints are unsuccessful. This computationally efficient approach allows experimental restraints from DEER experiments to be incorporated into RE simulations for efficient structural refinement.

Restrained-ensemble molecular dynamics simulations based on distance histograms from double electron-electron resonance spectroscopy

DEER (double electron-electron resonance) spectroscopy is a powerful pulsed ESR (electron spin resonance) technique allowing the determination of spin-spin distance histograms between site-directed nitroxide label sites on a protein in their native environment. However, incorporating ESR/DEER data in structural refinement is challenging because the information from the large number of distance histograms is complex and highly coupled. Here, a novel restrained-ensemble molecular dynamics simulation method is developed to incorporate the information from multiple ESR/DEER distance histograms simultaneously. Illustrative tests on three coupled spin-labels inserted in T4 lysozyme show that the method efficiently imposes the experimental distance distribution in this system. Different rotameric states of the χ1 and χ2 dihedrals in the spin-labels are also explored by restrained ensemble simulations. Using this method, it is hoped that experimental restraints from ESR/DEER experiments can be used to refine structural properties of biological systems.

Simultaneous prediction of protein secondary structure and transmembrane spans

Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α-helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three-state secondary structure prediction, and 94.8% for three-state transmembrane span prediction. These accuracies are comparable to state-of-the-art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org.

Conformational ensemble of the sodium-coupled aspartate transporter

Sodium and aspartate symporter from Pyrococcus horikoshii, Glt_Ph, is a homologue of the mammalian glutamate transporters, homotrimeric integral membrane proteins controlling the neurotransmitter levels in brain synapses. These transporters function by alternating between outward and inward facing states, in which the substrate binding site is oriented toward the extracellular space and the cytoplasm, respectively. Here we employ double electron-electron resonance (DEER) spectroscopy to probe the structure and the state distribution of the subunits in the trimer within distinct hydrophobic environments of detergent micelles and lipid bilayers. Our experiments reveal a conformational ensemble of protomers sampling the outward and inward facing states with nearly equal probabilities, indicative of comparable energies, and independently of each other. On average, the distributions vary only modestly in detergent and in bilayers, but in several mutants unique conformations are stabilized by the latter.

BCL::Fold--de novo prediction of complex and large protein topologies by assembly of secondary structure elements

Computational de novo protein structure prediction is limited to small proteins of simple topology. The present work explores an approach to extend beyond the current limitations through assembling protein topologies from idealized α-helices and β-strands. The algorithm performs a Monte Carlo Metropolis simulated annealing folding simulation. It optimizes a knowledge-based potential that analyzes radius of gyration, β-strand pairing, secondary structure element (SSE) packing, amino acid pair distance, amino acid environment, contact order, secondary structure prediction agreement and loop closure. Discontinuation of the protein chain favors sampling of non-local contacts and thereby creation of complex protein topologies. The folding simulation is accelerated through exclusion of flexible loop regions further reducing the size of the conformational search space. The algorithm is benchmarked on 66 proteins with lengths between 83 and 293 amino acids. For 61 out of these proteins, the best SSE-only models obtained have an RMSD100 below 8.0 Å and recover more than 20% of the native contacts. The algorithm assembles protein topologies with up to 215 residues and a relative contact order of 0.46. The method is tailored to be used in conjunction with low-resolution or sparse experimental data sets which often provide restraints for regions of defined secondary structure.

Optimization of Algorithms for Modeling Protein Structural Transitions from Sparse Long-Range Spin-Label Distance Constraints

Function-related structural transitions of proteins are often large-scale conformational changes that are related to essential dynamics of a protein, which in turn proceeds along a small number of slow normal modes. Hence, it should be possible to characterize such transitions by an equally small number of distance constraints. These constraints should contain the information how the backbone coordinates move along the active normal modes. Such an approach based on residue-level elastic network models for the protein backbone is optimized with respect to the fit algorithm, constraint selection, and parametrization of the elastic network model. A stable fitting algorithm can be based on energy equipartitioning among the modes in the active space. This stabilization allows for extending active space dimension beyond the number of available constraints, which improves fit quality for transitions with a normal mode spectrum of only slowly increasing frequencies. In constraint selection, discrimination between the active normal modes appears to be more important than achieving large distance changes between initial and final structure. Parametrization of the network model has only a small influence on fit quality, as long as scaling of force constants with the inverse sixth power of the distance between network nodes is maintained. Elastic network models with a uniform force constant below a cutoff distance perform significantly worse. With 50 distance constraints, the optimized approach covers more than 50% of the structural change for 44% of all test cases, between 25 and 50% for 22% of the cases, and it fails for 33%.

Characterization of Protein Conformational Changes with Sparse Spin-Label Distance Constraints

The combination of site-directed spin labeling with pulse EPR distance measurements can provide a moderate number of distance constraints on the nanometer length scale for proteins in different states. By adapting an existing algorithm (Zheng, W.; Brooks, B. R. Biophys. J. 2006, 90, 4327) to the problem, we address the question to what extent conformational change can be characterized when the protein structure is known for one of the states, whereas only a sparse set of distance constraints between spin labels is available for the other state. We find that the type and general direction of the conformational change can be recognized, while the amplitude may be uncertain.

MtsslWizard: In Silico Spin-Labeling and Generation of Distance Distributions in PyMOL

MtsslWizard is a computer program, which operates as a plugin for the PyMOL molecular graphics system. MtsslWizard estimates distances between spin labels on proteins quickly with user-configurable options through a simple graphical interface. In default mode, the program searches for ensembles of possible MTSSL conformations that do not clash with a static model of the protein. Once conformations are assigned, distance distributions between two or more ensembles are calculated, displayed, and can be exported to other software. The program's use is evaluated in a number of challenging test cases and its strengths and weaknesses evaluated. The benefits of the program are its accuracy and simplicity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00723-012-0314-0) contains supplementary material, which is available to authorized users.

Toward the fourth dimension of membrane protein structure: insight into dynamics from spin-labeling EPR spectroscopy

Trapping membrane proteins in the confines of a crystal lattice obscures dynamic modes essential for interconversion between multiple conformations in the functional cycle. Moreover, lattice forces could conspire with detergent solubilization to stabilize a minor conformer in an ensemble thus confounding mechanistic interpretation. Spin labeling in conjunction with electron paramagnetic resonance (EPR) spectroscopy offers an exquisite window into membrane protein dynamics in the native-like environment of a lipid bilayer. Systematic application of spin labeling and EPR identifies sequence-specific secondary structures, defines their topology and their packing in the tertiary fold. Long range distance measurements (60 Å-80 Å) between pairs of spin labels enable quantitative analysis of equilibrium dynamics and triggered conformational changes. This review highlights the contribution of spin labeling to bridging structure and mechanism. Efforts to develop methods for determining structures from EPR restraints and to increase sensitivity and throughput promise to expand spin labeling applications in membrane protein structural biology.

DEER distance measurements on proteins

Distance distributions between paramagnetic centers in the range of 1.8 to 6 nm in membrane proteins and up to 10 nm in deuterated soluble proteins can be measured by the DEER technique. The number of paramagnetic centers and their relative orientation can be characterized. DEER does not require crystallization and is not limited with respect to the size of the protein or protein complex. Diamagnetic proteins are accessible by site-directed spin labeling. To characterize structure or structural changes, experimental protocols were optimized and techniques for artifact suppression were introduced. Data analysis programs were developed, and it was realized that interpretation of the distance distributions must take into account the conformational distribution of spin labels. First methods have appeared for deriving structural models from a small number of distance constraints. The present scope and limitations of the technique are illustrated.

Global structure of a three-way junction in a phi29 packaging RNA dimer determined using site-directed spin labeling

The condensation of bacteriophage phi29 genomic DNA into its preformed procapsid requires the DNA packaging motor, which is the strongest known biological motor. The packaging motor is an intricate ring-shaped protein/RNA complex, and its function requires an RNA component called packaging RNA (pRNA). Current structural information on pRNA is limited, which hinders studies of motor function. Here, we used site-directed spin labeling to map the conformation of a pRNA three-way junction that bridges binding sites for the motor ATPase and the procapsid. The studies were carried out on a pRNA dimer, which is the simplest ring-shaped pRNA complex and serves as a functional intermediate during motor assembly. Using a nucleotide-independent labeling scheme, stable nitroxide radicals were attached to eight specific pRNA sites without perturbing RNA folding and dimer formation, and a total of 17 internitroxide distances spanning the three-way junction were measured using Double Electron-Electron Resonance spectroscopy. The measured distances, together with steric chemical constraints, were used to select 3662 viable three-way junction models from a pool of 65 billion. The results reveal a similar conformation among the viable models, with two of the helices (H(T) and H(L)) adopting an acute bend. This is in contrast to a recently reported pRNA tetramer crystal structure, in which H(T) and H(L) stack onto each other linearly. The studies establish a new method for mapping global structures of complex RNA molecules, and provide information on pRNA conformation that aids investigations of phi29 packaging motor and developments of pRNA-based nanomedicine and nanomaterial.

Membrane protein structure determination using paramagnetic tags

The combination of paramagnetic tagging strategies with NMR or EPR spectroscopic techniques can revolutionize de novo structure determination of helical membrane proteins. Leveraging the full potential of this approach requires optimal labeling strategies and prediction of membrane protein topology from sparse and low-resolution distance restraints, as addressed by Chen et al. (2011).