A hybrid NMR/SAXS‐based approach for discriminating oligomeric protein interfaces using Rosetta

Oligomeric proteins are important targets for structure determination in solution. While in most cases the fold of individual subunits can be determined experimentally, or predicted by homology‐based methods, protein–protein interfaces are challenging to determine de novo using conventional NMR structure determination protocols. Here we focus on a member of the bet‐V1 superfamily, Aha1 from Colwellia psychrerythraea. This family displays a broad range of crystallographic interfaces none of which can be reconciled with the NMR and SAXS data collected for Aha1. Unlike conventional methods relying on a dense network of experimental restraints, the sparse data are used to limit conformational search during optimization of a physically realistic energy function. This work highlights a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution. Proteins 2015; 83:309–317. © 2014 Wiley Periodicals, Inc.     

Simultaneous determination of disulphide bridge topology and three-dimensional structure using ambiguous intersulphur distance restraints: Possibilities and limitations

Knowledge of the native disulphide bridge topology allows the introduction of conformational restraints between remote parts of the peptide chain. This information is therefore of great importance for the successful determination of the three-dimensional structure of cysteine-rich proteins by NMR spectroscopy. In this paper we investigate the limitations of using ambiguous intersulphur restraints [Nilges, M. (1995) J. Mol. Biol., 245, 645–660] associated with NMR experimental information to determine the native disulphide bridge pattern. Using these restraints in a simulated annealing protocol we have determined the correct topology of numerous examples, including a protein with seven disulphide bridges (phospholipase A₂) and a protein in which 25% of the total number of residues are cysteines (-conotoxin GIIIB). We have also characterised the behaviour of the method when only limited experimental data is available, and find that the proposed protocol permits disulphide bridge determination even with a small number of restraints (around 5 NOEs – including a long-range restraint – per residue). In addition, we have shown that under these conditions the use of a reduced penalty function allows the identification of misassigned NOE restraints. These results indicate that the use of ambiguous intersulphur distances with the proposed simulated annealing protocol is a general method for the determination of disulphide bridge topology, particularly interesting in the first steps of NMR study of cysteine-rich proteins. Comparison with previously proposed protocols indicates that the presented method is more reliable and the interpretation of results is straightforward.     

Backbone structure of a small helical integral membrane protein: A unique structural characterization

The structural characterization of small integral membrane proteins pose a significant challenge for structural biology because of the multitude of molecular interactions between the protein and its heterogeneous environment. Here, the three‐dimensional backbone structure of Rv1761c from Mycobacterium tuberculosis has been characterized using solution NMR spectroscopy and dodecylphosphocholine (DPC) micelles as a membrane mimetic environment. This 127 residue single transmembrane helix protein has a significant (10 kDa) C‐terminal extramembranous domain. Five hundred and ninety distance, backbone dihedral, and orientational restraints were employed resulting in a 1.16 Å rmsd backbone structure with a transmembrane domain defined at 0.40 Å. The structure determination approach utilized residual dipolar coupling orientation data from partially aligned samples, long‐range paramagnetic relaxation enhancement derived distances, and dihedral restraints from chemical shift indices to determine the global fold. This structural model of Rv1761c displays some influences by the membrane mimetic illustrating that the structure of these membrane proteins is dictated by a combination of the amino acid sequence and the protein's environment. These results demonstrate both the efficacy of the structural approach and the necessity to consider the biophysical properties of membrane mimetics when interpreting structural data of integral membrane proteins and, in particular, small integral membrane proteins.     

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 ‐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.     

Identification and removal of nitroxide spin label contaminant: impact on PRE studies of α-helical membrane proteins in detergent

NMR paramagnetic relaxation enhancement (PRE) provides long‐range distance constraints (～15–25 Å) that can be critical to determining overall protein topology, especially where long‐range NOE information is unavailable such as in the case of larger proteins that require deuteration. However, several challenges currently limit the use of NMR PRE for α‐helical membrane proteins. One challenge is the nonspecific association of the nitroxide spin label to the protein‐detergent complex that can result in spurious PRE derived distance restraints. The effect of the nitroxide spin label contaminant is evaluated and quantified and a robust method for the removal of the contaminant is provided to advance the application of PRE restraints to membrane protein NMR structure determination.     

Assembly of protein structure from sparse experimental data: An efficient Monte Carlo model

A new, efficient method for the assembly of protein tertiary structure from known, loosely encoded secondary structure restraints and sparse information about exact side chain contacts is proposed and evaluated. The method is based on a new, very simple method for the reduced modeling of protein structure and dynamics, where the protein is described as a lattice chain connecting side chain centers of mass rather than Cαs. The model has implicit built-in multibody correlations that simulate short- and long-range packing preferences, hydrogen bonding cooperativity and a mean force potential describing hydrophobic interactions. Due to the simplicity of the protein representation and definition of the model force field, the Monte Carlo algorithm is at least an order of magnitude faster than previously published Monte Carlo algorithms for structure assembly. In contrast to existing algorithms, the new method requires a smaller number of tertiary restraints for successful fold assembly; on average, one for every seven residues as compared to one for every four residues. For example, for smaller proteins such as the B domain of protein G, the resulting structures have a coordinate root mean square deviation (cRMSD), which is about 3 Å from the experimental structure; for myoglobin, structures whose backbone cRMSD is 4.3 Å are produced, and for a 247-residue TIM barrel, the cRMSD of the resulting folds is about 6 Å. As would be expected, increasing the number of tertiary restraints improves the accuracy of the assembled structures. The reliability and robustness of the new method should enable its routine application in model building protocols based on various (very sparse) experimentally derived structural restraints. Proteins 32:475–494, 1998. © 1998 Wiley-Liss, Inc.     

A refinement protocol to determine structure,topology, and depth of insertion of membrane proteins using hybrid solution and solid-state NMR restraints

To fully describe the fold space and ultimately the biological function of membrane proteins, it is necessary to determine the specific interactions of the protein with the membrane. This property of membrane proteins that we refer to as structural topology cannot be resolved using X-ray crystallography or solution NMR alone. In this article, we incorporate into XPLOR-NIH a hybrid objective function for membrane protein structure determination that utilizes solution and solid-state NMR restraints, simultaneously defining structure, topology, and depth of insertion. Distance and angular restraints obtained from solution NMR of membrane proteins solubilized in detergent micelles are combined with backbone orientational restraints (chemical shift anisotropy and dipolar couplings) derived from solid-state NMR in aligned lipid bilayers. In addition, a supplementary knowledge-based potential, E _z (insertion depth potential), is used to ensure the correct positioning of secondary structural elements with respect to a virtual membrane. The hybrid objective function is minimized using a simulated annealing protocol implemented into XPLOR-NIH software for general use. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Tertiary structure prediction of the KIX domain of CBP using Monte Carlo simulations driven by restraints derived from multiple sequence alignments

Using a recently developed protein folding algorithm, a prediction of the tertiary structure of the KIX domain of the CREB binding protein is described. The method incorporates predicted secondary and tertiary restraints derived from multiple sequence alignments in a reduced protein model whose conformational space is explored by Monte Carlo dynamics. Secondary structure restraints are provided by the PHD secondary structure prediction algorithm that was modified for the presence of predicted U-turns, i.e., regions where the chain reverses global direction. Tertiary restraints are obtained via a two-step process: First, seed side-chain contacts are identified from a correlated mutation analysis, and then, a threading-based algorithm expands the number of these seed contacts. Blind predictions indicate that the KIX domain is a putative three-helix bundle, although the chirality of the bundle could not be uniquely determined. The expected root-mean-square deviation for the correct chirality of the KIX domain is between 5.0 and 6.2 Å. This is to be compared with the estimate of 12.9 Å that would be expected by a random prediction, using the model of F. Cohen and M. Sternberg (J. Mol. Biol. 138:321–333, 1980). Proteins 30:287–294, 1998. © 1998 Wiley-Liss, Inc.     

Determination of structural topology of a membrane protein in lipid bilayers using polarization optimized experiments (POE) for static and MAS solid state NMR spectroscopy

The low sensitivity inherent to both the static and magic angle spinning techniques of solid-state NMR (ssNMR) spectroscopy has thus far limited the routine application of multidimensional experiments to determine the structure of membrane proteins in lipid bilayers. Here, we demonstrate the advantage of using a recently developed class of experiments, polarization optimized experiments, for both static and MAS spectroscopy to achieve higher sensitivity and substantial time-savings for 2D and 3D experiments. We used sarcolipin, a single pass membrane protein, reconstituted in oriented bicelles (for oriented ssNMR) and multilamellar vesicles (for MAS ssNMR) as a benchmark. The restraints derived by these experiments are then combined into a hybrid energy function to allow simultaneous determination of structure and topology. The resulting structural ensemble converged to a helical conformation with a backbone RMSD ~0.44 Å, a tilt angle of 24° ± 1°, and an azimuthal angle of 55° ± 6°. This work represents a crucial first step toward obtaining high-resolution structures of large membrane proteins using combined multidimensional oriented solid-state NMR and magic angle spinning solid-state NMR.     

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. Proteins 2015; 83:1–24. © 2014 Wiley Periodicals, Inc.     

Application of sparse NMR restraints to large-scale protein structure prediction

The protein structure prediction algorithm TOUCHSTONEX that uses sparse distance restraints derived from NMR nuclear Overhauser enhancement (NOE) data to predict protein structures at low-to-medium resolution was evaluated as follows: First, a representative benchmark set of the Protein Data Bank library consisting of 1365 proteins up to 200 residues was employed. Using N/8 simulated long-range restraints, where N is the number of residues, 1023 (75%) proteins were folded to a C(alpha) root-mean-square deviation (RMSD) from native <6.5 A in one of the top five models. The average RMSD of the models for all 1365 proteins is 5.0 A. Using N/4 simulated restraints, 1206 (88%) proteins were folded to a RMSD <6.5 A and the average RMSD improved to 4.1 A. Then, 69 proteins with experimental NMR data were used. Using long-range NOE-derived restraints, 47 proteins were folded to a RMSD <6.5 A with N/8 restraints and 61 proteins were folded to a RMSD <6.5 A with N/4 restraints. Thus, TOUCHSTONEX can be a tool for NMR-based rapid structure determination, as well as used in other experimental methods that can provide tertiary restraint information.     

Generation of native-like protein structures from limited NMR data,modern force fields and advanced conformational sampling

Determining an accurate initial native-like protein fold is one of the most important and time-consuming steps of de novo NMR structure determination. Here we demonstrate that high-quality native-like models can be rapidly generated from initial structures obtained using limited NOE assignments, through replica exchange molecular dynamics refinement with a generalized Born implicit solvent (REX/GB). Conventional structure calculations using an initial sparse NOE set were unable to identify a unique topology for the zinc-bound C-terminal domain of E. coli chaperone Hsp33, due to a lack of unambiguous long range NOEs. An accurate overall topology was eventually obtained through laborious hand identification of long range NOEs. However we were able to obtain high-quality models with backbone RMSD values of about 2 Å with respect to the final structures, using REX/GB refinement with the original limited set of initial NOE restraints. These models could then be used to make further assignments of ambiguous NOEs and thereby speed up the structure determination process. The ability to calculate accurate starting structures from the limited unambiguous NOE set available at the beginning of a structure calculation offers the potential of a much more rapid and automated process for NMR structure determination. Jianhan Chen: Authors contributed equally to this work.Hyung-Sik Won: Authors contributed equally to this work.     

On the computation of the tertiary structure of globular proteins—IV. Use of secondary structure information

The distance geometry approach for computing the tertiary structure of globular proteins emphasized in this series of papers (Goelet al., J. theor. Biol. 99, 705–757, 1982) is developed further. This development includes incorporation of some secondary structure information—the location of alpha helices in the primary sequence—in the algorithm to compute the tertiary structure of alpha helical globular proteins. An algorithm is developed which estimates the interresidue distances between chain-proximate helices. These distances, in conjunction with the global statistical average distances obtainable from a database of real proteins and determined by the primary sequence of the protein under study, are used to determine the tertiary structure. Five proteins, parvalbumin, hemerythrin, human hemoglobin, lamprey hemoglobin, and sperm whale myoglobin, are investigated. The root mean square (RMS) errors between the calculated structures and those determined by X-ray diffraction range from 4.78 to 7.56 Å. These RMSs are 0.21–2.76 Å lower than those estimated without the secondary structure information. Contact maps and three-dimensional backbone representations also show considerable improvements with the introduction of secondary structure information.     

Protein folding simulation with genetic algorithm and supersecondary structure constraints

We describe an algorithm to compute native structures of proteins from their primary sequences. The novel aspects of this method are: 1) The hydrophobic potential was set to be proportional to the nonpolar solvent accessible surface. To make computation feasible, we developed a new algorithm to compute the solvent accessible surface areas rapidly. 2) The supersecondary structures of each protein were predicted and used as restraints during the conformation searching processes. This algorithm was applied to five proteins. The overall fold of these proteins can be computed from their sequences, with deviations from crystal structures of 1.48–4.48 Å for C_α atoms. Proteins 31:247–257, 1998. © 1998 Wiley-Liss, Inc.     

MOTOR: Model assisted software for NMR structure determination

Eukaryotic proteins with important biological function can be partially unstructured, conformational flexible, or heterogenic. Crystallization trials often fail for such proteins. In NMR spectroscopy, parts of the polypeptide chain undergoing dynamics in unfavorable time regimes cannot be observed. De novo NMR structure determination is seriously hampered when missing signals lead to an incomplete chemical shift assignment resulting in an information content of the NOE data insufficient to determine the structure ab initio. We developed a new protein structure determination strategy for such cases based on a novel NOE assignment strategy utilizing a number of model structures but no explicit reference structure as it is used for bootstrapping like algorithms. The software distinguishes in detail between consistent and mutually exclusive pairs of possible NOE assignments on the basis of different precision levels of measured chemical shifts searching for a set of maximum number of consistent NOE assignments in agreement with 3D space. Validation of the method using the structure of the low molecular‐weight‐protein tyrosine phosphatase A (MptpA) showed robust results utilizing protein structures with 30–45% sequence identity and 70% of the chemical shift assignments. About 60% of the resonance assignments are sufficient to identify those structural models with highest conformational similarity to the real structure. The software was benchmarked by de novo solution structures of fibroblast growth factor 21 (FGF21) and the extracellular fibroblast growth factor receptor domain FGFR4 D2, which both failed in crystallization trials and in classical NMR structure determination. Proteins 2013; 81:2007–2022. © 2013 Wiley Periodicals, Inc.     

Biomolecular magic-angle spinning solid-state NMR: recent methods and applications

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Highlights► Recent progress in biomolecular magic angle spinning solid state NMR is reviewed. ► MAS NMR is applied to study non-crystalline, non-soluble and high molecular-weight systems. ► New methods rely on sparse isotope enrichment, proton detection, fast spinning and signal enhancements. ► New structure calculations are based on ¹H–¹H restraints, paramagnetic effects and tensor analysis. ► Applications to protein aggregates, assemblies, fibrils, membrane proteins and enzymes are described.     

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. Proteins 2015; 83:1947–1962. © 2015 Wiley Periodicals, Inc     

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and ¹⁵N-¹H residual dipolar coupling data, typical of that obtained for ¹⁵N,¹³C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.     

Accurate geometries for “Mountain pass” regions of the Ramachandran plot using quantum chemical calculations

Unusual local arrangements of protein in Ramachandran space are not well represented by standard geometry tools used in either protein structure refinement using simple harmonic geometry restraints or in protein simulations using molecular mechanics force fields. In contrast, quantum chemical computations using small poly‐peptide molecular models can predict accurate geometries for any well‐defined backbone Ramachandran orientation. For conformations along transition regions—ϕ from −60 to 60°—a very good agreement with representative high‐resolution experimental X‐ray (≤1.5 Å) protein structures is obtained for both backbone C⁻¹‐N‐C_α angle and the nonbonded O⁻¹…C distance, while “standard geometry” leads to the “clashing” of O…C atoms and Amber FF99SB predicts distances too large by about 0.15 Å. These results confirm that quantum chemistry computations add valuable support for detailed analysis of local structural arrangements in proteins, providing improved or missing data for less understood high‐energy or unusual regions.     

Recent excitements in protein NMR: Large proteins and biologically relevant dynamics

The advent of Transverse Relaxation Optimized SpectroscopY (TROSY) and perdeuteration allowed biomolecular NMR spectroscopists to overcome the size limitation barrier (~20 kDa) in de novo structure determination of proteins. The utility of these techniques was immediately demonstrated on large proteins and protein complexes (e.g. GroEL-GroES, ClpP protease, Hsp90-p53, 20S proteasome, etc.). Further, recent methodological developments such as Residual Dipolar Couplings and Paramagnetic Relaxation Enhancement allowed accurate measurement of long-range structural restraints. Additionally, Carr-Purcell-Meiboom-Gill (CPMG), rotating frame relaxation experiments (R₁ρ) and saturation transfer experiments (CEST and DEST) created never-before accessibility to the μs–ms timescale dynamic parameters that led to the deeper understanding of biological processes. Meanwhile, the excitement in the field continued with a series of developments in the fast data acquisition methods allowing rapid structural studies on less stable proteins. This review aims to discuss important developments in the field of biomolecular NMR spectroscopy in the recent past, i.e., in the post TROSY era. These developments not only gave access to the structural studies of large protein assemblies, but also revolutionized tools in the arsenal of today’s biomolecular NMR and point to a bright future of biomolecular NMR spectroscopy.