De novo high-resolution protein structure determination from sparse spin-labeling EPR data

As many key proteins evade crystallization and remain too large for nuclear magnetic resonance spectroscopy, electron paramagnetic resonance (EPR) spectroscopy combined with site-directed spin labeling offers an alternative approach for obtaining structural information. Such information must be translated into geometric restraints to be used in computer simulations. Here, distances between spin labels are converted into distance ranges between beta carbons by using a "motion-on-a-cone" model, and a linear-correlation model links spin-label accessibility to the number of neighboring residues. This approach was tested on T4-lysozyme and alphaA-crystallin with the de novo structure prediction algorithm Rosetta. The results demonstrate the feasibility of obtaining highly accurate, atomic-detail models from EPR data by yielding 1.0 A and 2.6 A full-atom models, respectively. Distance restraints between amino acids far apart in sequence but close in space are most valuable for structure determination. The approach can be extended to other experimental techniques such as fluorescence spectroscopy, substituted cysteine accessibility method, or mutational studies.     

Characterizing RNA ensembles from NMR data with kinematic models

Functional mechanisms of biomolecules often manifest themselves precisely in transient conformational substates. Researchers have long sought to structurally characterize dynamic processes in non-coding RNA, combining experimental data with computer algorithms. However, adequate exploration of conformational space for these highly dynamic molecules, starting from static crystal structures, remains challenging. Here, we report a new conformational sampling procedure, KGSrna, which can efficiently probe the native ensemble of RNA molecules in solution. We found that KGSrna ensembles accurately represent the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans-activation response element stem–loop. Ensemble-based interpretations of averaged data can aid in formulating and testing dynamic, motion-based hypotheses of functional mechanisms in RNAs with broad implications for RNA engineering and therapeutic intervention.     

Structural insights for designed alanine-rich helices: comparing NMR helicity measures and conformational ensembles from molecular dynamics simulation

The temperature dependence of helical propensities for the peptides Ac-ZGG-(KAAAA)(3)X-NH(2) (Z = Y or G, X = A, K, and D-Arg) were studied both experimentally and by MD simulations. Good agreement is observed in both the absolute helical propensities as well as relative helical content along the sequence; the global minimum on the calculated free energy landscape corresponds to a single alpha-helical conformation running from K4 to A18 with some terminal fraying, particularly at the C-terminus. Energy component analysis shows that the single helix state has favorable intramolecular electrostatic energy due to hydrogen bonds, and that less-favorable two-helix globular states have favorable solvation energy. The central lysine residues do not appear to increase helicity; however, both experimental and simulation studies show increasing helicity in the series X = Ala --> Lys --> D-Arg. This C-capping preference was also experimentally confirmed in Ac-(KAAAA)(3)X-GY-NH(2) and (KAAAA)(3)X-GY-NH(2) sequences. The roles of the C-capping groups, and of lysines throughout the sequence, in the MD-derived ensembles are analyzed in detail.     

Automated protein structure calculation from NMR data

Current software is almost at the stage to permit completely automatic structure determination of small proteins of <15 kDa, from NMR spectra to structure validation with minimal user interaction. This goal is welcome, as it makes structure calculation more objective and therefore more easily validated, without any loss in the quality of the structures generated. Moreover, it releases expert spectroscopists to carry out research that cannot be automated. It should not take much further effort to extend automation to ca 20 kDa. However, there are technological barriers to further automation, of which the biggest are identified as: routines for peak picking; adoption and sharing of a common framework for structure calculation, including the assembly of an automated and trusted package for structure validation; and sample preparation, particularly for larger proteins. These barriers should be the main target for development of methodology for protein structure determination, particularly by structural genomics consortia.

KinImmerse: Macromolecular VR for NMR ensembles

Background  

In molecular applications, virtual reality (VR) and immersive virtual environments have generally been used and valued for the visual and interactive experience – to enhance intuition and communicate excitement – rather than as part of the actual research process. In contrast, this work develops a software infrastructure for research use and illustrates such use on a specific case.     

Refining conformational ensembles of flexible proteins against small-angle x-ray scattering data

Intrinsically disordered proteins and flexible regions in multidomain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining one or more biophysical techniques with computational modeling or simulations. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. In both cases, one generally needs a so-called forward model (i.e., an algorithm to calculate experimental observables from individual conformations or ensembles). In many cases, this involves one or more parameters that need to be set, and it is not always trivial to determine the optimal values or to understand the impact on the choice of parameters. For example, in the case of small-angle x-ray scattering (SAXS) experiments, many forward models include parameters that describe the contribution of the hydration layer and displaced solvent to the background-subtracted experimental data. Often, one also needs to fit a scale factor and a constant background for the SAXS data but across the entire ensemble. Here, we present a protocol to dissect the effect of the free parameters on the calculated SAXS intensities and to identify a reliable set of values. We have implemented this procedure in our Bayesian/maximum entropy framework for ensemble refinement and demonstrate the results on four intrinsically disordered proteins and a protein with three domains connected by flexible linkers. Our results show that the resulting ensembles can depend on the parameters used for solvent effects and suggest that these should be chosen carefully. We also find a set of parameters that work robustly across all proteins.     

Characterization of conformational and dynamic properties of natively unfolded human and mouse alpha-synuclein ensembles by NMR: implication for aggregation

Conversion of human α-synuclein (aS) from the free soluble state to the insoluble fibrillar state has been implicated in the etiology of Parkinson's disease. Human aS is highly homologous in amino acid sequence to mouse aS, which contains seven substitutions including the A53T that has been linked to familial Parkinson's disease, and including five substitutions in the C-terminal region. It has been shown that the rate of fibrillation is highly dependent on the exact sequence of the protein, and mouse aS is reported to aggregate more rapidly than human aS in vitro. Nuclear magnetic resonance experiments of mouse and human aS at supercooled temperatures (263 K) are used to understand the effect of sequence on conformational fluctuations in the disordered ensembles and to relate these to differences in propensities to aggregate. We show that both aS are natively unfolded at low temperature with different propensities to secondary structure, backbone dynamics and long-range contacts across the protein. Mouse aS exhibits a higher propensity to helical conformation around the C-terminal substitutions as well as the loss of transient long-range contacts from the C- to the N-terminal end and hydrophobic central regions of the protein relative to human aS. Lack of back-folding from the C-terminal end of mouse aS exposes the N-terminal region, which is shown, by ¹⁵N relaxation experiments, to be very restricted in mobility relative to human aS. We propose that the restricted mobility in the N-terminal region may arise from transient interchain interactions, suggesting that the N-terminal KTK(E/Q)GV repeats may serve as initiation sites for aggregation in mouse aS. These transient interchain interactions coupled with a non-Aβ amyloid component (NAC) region that is both more exposed and has a higher propensity to β structure may accelerate the rate of fibril formation of aS.     

Modeling protein conformational ensembles: from missing loops to equilibrium fluctuations

Characterizing protein flexibility is an important goal for understanding the physical-chemical principles governing biological function. This paper presents a Fragment Ensemble Method to capture the mobility of a protein fragment such as a missing loop and its extension into a Protein Ensemble Method to characterize the mobility of an entire protein at equilibrium. The underlying approach in both methods is to combine a geometric exploration of conformational space with a statistical mechanics formulation to generate an ensemble of physical conformations on which thermodynamic quantities can be measured as ensemble averages. The Fragment Ensemble Method is validated by applying it to characterize loop mobility in both instances of strongly stable and disordered loop fragments. In each instance, fluctuations measured over generated ensembles are consistent with data from experiment and simulation. The Protein Ensemble Method captures the mobility of an entire protein by generating and combining ensembles of conformations for consecutive overlapping fragments defined over the protein sequence. This method is validated by applying it to characterize flexibility in ubiquitin and protein G. Thermodynamic quantities measured over the ensembles generated for both proteins are fully consistent with available experimental data. On these proteins, the method recovers nontrivial data such as order parameters, residual dipolar couplings, and scalar couplings. Results presented in this work suggest that the proposed methods can provide insight into the interplay between protein flexibility and function.     

Evidence for methionine sulfoximine as a transition-state analog for glutamine synthetase from NMR and EPR data.

Manganese(II)bound at the “tight” metal ion site of unadenylylated glutamine synthetase (E. coli W) has two rapidly exchanging first coordination sphere water molecules. The solvation number was evaluated from a study of the frequency dependence of $\text{1}\text{pT}{}_{\mathrm{<mtext>1p</mtext>}}$, the paramagnetic contribution to the longitudinal relaxation rate of solvent protons. The number of rapidly exchanging water molecules is reduced to one in the presence of saturating L-glutamate and to ～0.2 when L-methionine SR-sulfoximine (MSOX) is present. MSOX is a linear competitive inhibitor (K_I=3μM) of glutamine synthetase when L-Glu is the substrate. The dissociation constant of MSOX measured by following the 18 fold decrease in $\text{1}\text{pT}{}_{\mathrm{1p}}$ (at 48 MHz) is 30μM and is lowered to ～9μM in the presence of ADP. The high affinity of MSOX for the enzyme suggests that this compound mimicks the “transition-state” for the glutamine synthetase reaction. Further evidence for this postulate is found from the dramatic sharpening of the epr spectrum of enzyme-bound Mn(II) in the presence of MSOX and MSOX plus ADP. The intense change in the epr spectrum arises from reduced solvent accessibility to bound Mn(II) and conformational changes produced by binding MSOX and ADP. The suggestion is made from these data that L-Glu and MSOX bind near or directly to the Mn(II) at the “tight” metal ion site in glutamine synthetase isolated from E. coli W.     

Principal components analysis of protein structure ensembles calculated using NMR data

One important problem when calculating structures of biomolecules from NMR data is distinguishing converged structures from outlier structures. This paper describes how Principal Components Analysis (PCA) has the potential to classify calculated structures automatically, according to correlated structural variation across the population. PCA analysis has the additional advantage that it highlights regions of proteins which are varying across the population. To apply PCA, protein structures have to be reduced in complexity and this paper describes two different representations of protein structures which achieve this. The calculated structures of a 28 amino acid peptide are used to demonstrate the methods. The two different representations of protein structure are shown to give equivalent results, and correct results are obtained even though the ensemble of structures used as an example contains two different protein conformations. The PCA analysis also correctly identifies the structural differences between the two conformations.     

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 ?-80 ?) 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.     

Limited sampling of conformational space by the distance geometry algorithm: implications for structures generated from NMR data

Calculations with a metric matrix distance geometry algorithm were performed that show that the standard implementation of the algorithm generally samples a very limited region of conformational space. This problem is most severe when only a small amount of distance information is used as input for the algorithm. Control calculations were performed on linear peptides, disulfide-linked peptides, and a double-stranded DNA decamer where only distances defining the covalent structures of the molecules (as well as the hydrogen bonds for the base pairs in the DNA) were included as input. Since the distance geometry algorithm is commonly used to generate structures of biopolymers from distance data obtained from NMR experiments, simulations were performed on the small globular protein basic pancreatic trypsin inhibitor (BPTI) that mimic calculations performed with actual NMR data. The results on BPTI and on the control peptides indicate that the standard implementation of the algorithm has two main problems: first, that it generates extended structures; second, that it has a tendency to consistently produce similar structures instead of sampling all structures consistent with the input distance information. These results also show that use of a simple root-mean-square deviation for evaluating the quality of the structures generated from NMR data may not be generally appropriate. The main sources of these problems are identified, and our results indicate that the problems are not a fundamental property of the distance geometry algorithm but arise from the implementations presently used to generate structures from NMR data. Several possible methods for alleviating these problems are discussed.     

Time-resolved site-directed spin-labeling studies of bacteriorhodopsin: loop-specific conformational changes in M

A spin-label at site 101 in the C-D loop of bacteriorhodopsin was previously found to detect a conformational change during the M --> N transition [Steinhoff, H. -J., Mollaaghababa, R., Altenbach, C., Hideg, K., Krebs, M. P., Khorana, H. G., and Hubbell, W. L. (1994) Science 266, 105-107]. We have extended these time-resolved electron paramagnetic resonance studies in purple membranes by analyzing conformational changes detected by a spin-label at another site in the C-D loop (103), and at sites in the A-B loop (35), the D-E loop (130), and the E-F loop (160). In addition, we have investigated the motion detected by a spin-label at site 101 in a D96A mutant background that has a prolonged M intermediate. We find that among the examined sites, only spin-labels in the C-D loop detect a significant change in the local environment after the rise of M. Although the D96A mutation dramatically prolongs the lifetime of the M intermediate, it does not perturb either the structure of bacteriorhodopsin or the nature of the light-activated conformational change detected by a spin-label at site 101. In this mutant, a conformational change is detected during the lifetime of M, when no change in the 410 nm absorbance is observed. These results provide direct structural evidence for the heterogeneity of the M population in real time, and demonstrate that the motion detected at site 101 occurs in M, prior to Schiff base reprotonation.     

The determination of the conformational properties of nucleic acids in solution from NMR data

A program, NUCFIT, has been written for simulating the effects of conformational averaging on nuclear Overhauser enhancement (NOE) intensities for the spin systems found in nucleic acids. Arbitrary structures can be generated, and the NOE time courses can be calculated for truncated one-dimensional NOEs, two-dimensional NOE and rotating frame NOE spectroscopy (NOESY and ROESY) experiments. Both isotropic and anisotropic molecular rotation can be treated, using Woessner's formalism (J. Chem. Phys. (1962) 37, 647-654). The effects of slow conformational averaging are simulated by taking population-weighted means of the conformations present. Rapid motions are allowed for by using order parameters which can be supplied by the user, or calculated for specific motional models using the formalism of Tropp (J. Chem. Phys. (1980) 72, 6035-6043). NOE time courses have been simulated for a wide variety of conformations and used to determine the quality of structure determinations using NMR data for nucleic acids. The program also allows grid-searching with least-squares fitting of structures to experimental data, including the effects of spin-diffusion, conformational averaging and rapid internal motions. The effects of variation of intra and internucleotide conformational parameters on NOE intensities has been systematically explored. It is found that (i) the conformation of nucleotides is well determined by realistic NOE data sets, (ii) some of the helical parameters, particularly the base pair roll, are poorly determined even for extensive, noise-free data sets, (iii) conformational averaging of the sugars by pseudorotation has at most second-order influence on the determination of other parameters and (iv) averaging about the glycosidic torsion bond also has, in most cases, an insignificant effect on the determination of the conformation of nucleotides.