Comparison of NMR and crystal structures of membrane proteins and computational refinement to improve model quality

Membrane proteins are challenging to study and restraints for structure determination are typically sparse or of low resolution because the membrane environment that surrounds them leads to a variety of experimental challenges. When membrane protein structures are determined by different techniques in different environments, a natural question is “which structure is most biologically relevant?” Towards answering this question, we compiled a dataset of membrane proteins with known structures determined by both solution NMR and X‐ray crystallography. By investigating differences between the structures, we found that RMSDs between crystal and NMR structures are below 5 Å in the membrane region, NMR ensembles have a higher convergence in the membrane region, crystal structures typically have a straighter transmembrane region, have higher stereo‐chemical correctness, and are more tightly packed. After quantifying these differences, we used high‐resolution refinement of the NMR structures to mitigate them, which paves the way for identifying and improving the structural quality of membrane proteins.     

Improving NMR protein structure quality by Rosetta refinement: a molecular replacement study

The structure of human protein HSPC034 has been determined by both solution nuclear magnetic resonance (NMR) spectroscopy and X-ray crystallography. Refinement of the NMR structure ensemble, using a Rosetta protocol in the absence of NMR restraints, resulted in significant improvements not only in structure quality, but also in molecular replacement (MR) performance with the raw X-ray diffraction data using MOLREP and Phaser. This method has recently been shown to be generally applicable with improved MR performance demonstrated for eight NMR structures refined using Rosetta (Qian et al., Nature 2007;450:259-264). Additionally, NMR structures of HSPC034 calculated by standard methods that include NMR restraints have improvements in the RMSD to the crystal structure and MR performance in the order DYANA, CYANA, XPLOR-NIH, and CNS with explicit water refinement (CNSw). Further Rosetta refinement of the CNSw structures, perhaps due to more thorough conformational sampling and/or a superior force field, was capable of finding alternative low energy protein conformations that were equally consistent with the NMR data according to the Recall, Precision, and F-measure (RPF) scores. On further examination, the additional MR-performance shortfall for NMR refined structures as compared with the X-ray structure were attributed, in part, to crystal-packing effects, real structural differences, and inferior hydrogen bonding in the NMR structures. A good correlation between a decrease in the number of buried unsatisfied hydrogen-bond donors and improved MR performance demonstrates the importance of hydrogen-bond terms in the force field for improving NMR structures. The superior hydrogen-bond network in Rosetta-refined structures demonstrates that correct identification of hydrogen bonds should be a critical goal of NMR structure refinement. Inclusion of nonbivalent hydrogen bonds identified from Rosetta structures as additional restraints in the structure calculation results in NMR structures with improved MR performance.     

Major groove width variations in RNA structures determined by NMR and impact of <Superscript>13</Superscript>C residual chemical shift anisotropy and <Superscript>1</Superscript>H–<Superscript>13</Superscript>C residual dipolar coupling on refinement

Ribonucleic acid structure determination by NMR spectroscopy relies primarily on local structural restraints provided by ¹H– ¹H NOEs and J-couplings. When employed loosely, these restraints are broadly compatible with A- and B-like helical geometries and give rise to calculated structures that are highly sensitive to the force fields employed during refinement. A survey of recently reported NMR structures reveals significant variations in helical parameters, particularly the major groove width. Although helical parameters observed in high-resolution X-ray crystal structures of isolated A-form RNA helices are sensitive to crystal packing effects, variations among the published X-ray structures are significantly smaller than those observed in NMR structures. Here we show that restraints derived from aromatic ¹H– ¹³C residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs) can overcome NMR restraint and force field deficiencies and afford structures with helical properties similar to those observed in high-resolution X-ray structures.     

Refinement of protein structures in explicit solvent

We present a CPU efficient protocol for refinement of protein structures in a thin layer of explicit solvent and energy parameters with completely revised dihedral angle terms. Our approach is suitable for protein structures determined by theoretical (e.g., homology modeling or threading) or experimental methods (e.g., NMR). In contrast to other recently proposed refinement protocols, we put a strong emphasis on consistency with widely accepted covalent parameters and computational efficiency. We illustrate the method for NMR structure calculations of three proteins: interleukin-4, ubiquitin, and crambin. We show a comparison of their structure ensembles before and after refinement in water with and without a force field energy term for the dihedral angles; crambin was also refined in DMSO. Our results demonstrate the significant improvement of structure quality by a short refinement in a thin layer of solvent. Further, they show that a dihedral angle energy term in the force field is beneficial for structure calculation and refinement. We discuss the optimal weight for the energy constant for the backbone angle omega and include an extensive discussion of meaning and relevance of the calculated validation criteria, in particular root mean square Z scores for covalent parameters such as bond lengths.     

A novel strategy for NMR resonance assignment and protein structure determination

The quality of protein structures determined by nuclear magnetic resonance (NMR) spectroscopy is contingent on the number and quality of experimentally-derived resonance assignments, distance and angular restraints. Two key features of protein NMR data have posed challenges for the routine and automated structure determination of small to medium sized proteins; (1) spectral resolution – especially of crowded nuclear Overhauser effect spectroscopy (NOESY) spectra, and (2) the reliance on a continuous network of weak scalar couplings as part of most common assignment protocols. In order to facilitate NMR structure determination, we developed a semi-automated strategy that utilizes non-uniform sampling (NUS) and multidimensional decomposition (MDD) for optimal data collection and processing of selected, high resolution multidimensional NMR experiments, combined it with an ABACUS protocol for sequential and side chain resonance assignments, and streamlined this procedure to execute structure and refinement calculations in CYANA and CNS, respectively. Two graphical user interfaces (GUIs) were developed to facilitate efficient analysis and compilation of the data and to guide automated structure determination. This integrated method was implemented and refined on over 30 high quality structures of proteins ranging from 5.5 to 16.5 kDa in size.     

Rapid and reliable protein structure determination via chemical shift threading

Protein structure determination using nuclear magnetic resonance (NMR) spectroscopy can be both time-consuming and labor intensive. Here we demonstrate how chemical shift threading can permit rapid, robust, and accurate protein structure determination using only chemical shift data. Threading is a relatively old bioinformatics technique that uses a combination of sequence information and predicted (or experimentally acquired) low-resolution structural data to generate high-resolution 3D protein structures. The key motivations behind using NMR chemical shifts for protein threading lie in the fact that they are easy to measure, they are available prior to 3D structure determination, and they contain vital structural information. The method we have developed uses not only sequence and chemical shift similarity but also chemical shift-derived secondary structure, shift-derived super-secondary structure, and shift-derived accessible surface area to generate a high quality protein structure regardless of the sequence similarity (or lack thereof) to a known structure already in the PDB. The method (called E-Thrifty) was found to be very fast (often?<?10 min/structure) and to significantly outperform other shift-based or threading-based structure determination methods (in terms of top template model accuracy)—with an average TM-score performance of 0.68 (vs. 0.50–0.62 for other methods). Coupled with recent developments in chemical shift refinement, these results suggest that protein structure determination, using only NMR chemical shifts, is becoming increasingly practical and reliable. E-Thrifty is available as a web server at http://ethrifty.ca.     

Time- and ensemble-averaged direct NOE restraints

Summary NMR data are collected as time- and ensemble-averaged quantities. Yet, in commonly used methods for structure determination of biomolecules, structures are required to satisfy simultaneously a large number of constrainsts. Recently, however, methods have been developed that allow a better fit of the experimental data by the use of time- or ensemble-averaged restraints. Thus far, these methods have been applied to structure refinement using distance and J-coupling restraints. In this paper, time and ensemble averaging is extended to the direct refinement with experimental NOE data. The implementation of time- and ensemble-averaged NOE restraints in DINOSAUR is described and illustrated with experimental NMR data for crambin, a 46-residue protein. Structure refinement with both time- and ensemble-averaged NOE restraints results in lower R-factors, indicating a better fit of the experimental NOE data.     

Influence of non-bonded parameters on the quality of NMR structures: A new force field for NMR structure calculation

The effects of different non-bonded parameters of force fields for NMR structure calculation on the quality of the resulting NMR solution structures were investigated using Interleukin 4 as a model system. NMR structure ensembles were calculated with an ab initio protocol using torsion angle dynamics. The calculations were repeated with five different non-bonded energy functions and parameters. The resulting ensembles were compared with the available X-ray structures, and their quality was assessed with common structure validation programs. In addition, the impact of torsion angle restraints and dihedral energy terms for the sidechains and the backbone was studied. The further improvement of the quality by refinement in explicit solvent was demonstrated. The optimal parameters, including those necessary for water refinement, are available in the new version of the PARALLHDG force field.     

Accurate ab initio prediction of NMR chemical shifts of nucleic acids and nucleic acids/protein complexes

NMR chemical shift predictions based on empirical methods are nowadays indispensable tools during resonance assignment and 3D structure calculation of proteins. However, owing to the very limited statistical data basis, such methods are still in their infancy in the field of nucleic acids, especially when non-canonical structures and nucleic acid complexes are considered. Here, we present an ab initio approach for predicting proton chemical shifts of arbitrary nucleic acid structures based on state-of-the-art fragment-based quantum chemical calculations. We tested our prediction method on a diverse set of nucleic acid structures including double-stranded DNA, hairpins, DNA/protein complexes and chemically-modified DNA. Overall, our quantum chemical calculations yield highly/very accurate predictions with mean absolute deviations of 0.3–0.6 ppm and correlation coefficients (r²) usually above 0.9. This will allow for identifying misassignments and validating 3D structures. Furthermore, our calculations reveal that chemical shifts of protons involved in hydrogen bonding are predicted significantly less accurately. This is in part caused by insufficient inclusion of solvation effects. However, it also points toward shortcomings of current force fields used for structure determination of nucleic acids. Our quantum chemical calculations could therefore provide input for force field optimization.     

Conjoined Use of EM and NMR in RNA Structure Refinement

More than 40% of the RNA structures have been determined using nuclear magnetic resonance (NMR) technique. NMR mainly provides local structural information of protons and works most effectively on relatively small biomacromolecules. Hence structural characterization of large RNAs can be difficult for NMR alone. Electron microscopy (EM) provides global shape information of macromolecules at nanometer resolution, which should be complementary to NMR for RNA structure determination. Here we developed a new energy term in Xplor-NIH against the density map obtained by EM. We conjointly used NMR and map restraints for the structure refinement of three RNA systems — U2/U6 small-nuclear RNA, genome-packing motif (Ψ^CD)₂ from Moloney murine leukemia virus, and ribosome-binding element from turnip crinkle virus. In all three systems, we showed that the incorporation of a map restraint, either experimental or generated from known PDB structure, greatly improves structural precision and accuracy. Importantly, our method does not rely on an initial model assembled from RNA duplexes, and allows full torsional freedom for each nucleotide in the torsion angle simulated annealing refinement. As increasing number of macromolecules can be characterized by both NMR and EM, the marriage between the two techniques would enable better characterization of RNA three-dimensional structures.     

Protein NMR Structures Refined without NOE Data

The refinement of low-quality structures is an important challenge in protein structure prediction. Many studies have been conducted on protein structure refinement; the refinement of structures derived from NMR spectroscopy has been especially intensively studied. In this study, we generated flat-bottom distance potential instead of NOE data because NOE data have ambiguity and uncertainty. The potential was derived from distance information from given structures and prevented structural dislocation during the refinement process. A simulated annealing protocol was used to minimize the potential energy of the structure. The protocol was tested on 134 NMR structures in the Protein Data Bank (PDB) that also have X-ray structures. Among them, 50 structures were used as a training set to find the optimal “width” parameter in the flat-bottom distance potential functions. In the validation set (the other 84 structures), most of the 12 quality assessment scores of the refined structures were significantly improved (total score increased from 1.215 to 2.044). Moreover, the secondary structure similarity of the refined structure was improved over that of the original structure. Finally, we demonstrate that the combination of two energy potentials, statistical torsion angle potential (STAP) and the flat-bottom distance potential, can drive the refinement of NMR structures.     

De novo protein structure determination using sparse NMR data

We describe a method for generating moderate to high-resolution protein structures using limited NMR data combined with the ab initio protein structure prediction method Rosetta. Peptide fragments are selected from proteins of known structure based on sequence similarity and consistency with chemical shift and NOE data. Models are built from these fragments by minimizing an energy function that favors hydrophobic burial, strand pairing, and satisfaction of NOE constraints. Models generated using this procedure with 1 NOE constraint per residue are in some cases closer to the corresponding X-ray structures than the published NMR solution structures. The method requires only the sparse constraints available during initial stages of NMR structure determination, and thus holds promise for increasing the speed with which protein solution structures can be determined.     

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.     

Deriving correlated motions in proteins from X-ray structure refinement by using TLS parameters

Dynamic information in proteins may provide valuable information for understanding allosteric regulation of protein complexes or long-range effects of the mutations on enzyme activity. Experimental data such as X-ray B-factors or NMR order parameters provide a convenient estimate of atomic fluctuations (or atomic auto-correlated motions) in proteins. However, it is not as straightforward to obtain atomic cross-correlated motions in proteins — one usually resorts to more sophisticated computational methods such as Molecular Dynamics, normal mode analysis or atomic network models. In this report, we show that atomic cross-correlations can be reliably obtained directly from protein structure using X-ray refinement data. We have derived an analytic form of atomic correlated motions in terms of the original TLS parameters used to refine the B-factors of X-ray structures. The correlated maps computed using this equation are well correlated with those of the method based on a mechanical model (the correlation coefficient is 0.75) for a non-homologous dataset comprising 100 structures. We have developed an approach to compute atomic cross-correlations directly from X-ray protein structure. Being in analytic form, it is fast and provides a feasible way to compute correlated motions in proteins in a high throughput way. In addition, avoiding sophisticated computational operations; it provides a quick, reliable way, especially for non-computational biologists, to obtain dynamics information directly from protein structure relevant to its function.     

High quality NMR structures: a new force field with implicit water and membrane solvation for Xplor-NIH

Structure determination of proteins by NMR is unique in its ability to measure restraints, very accurately, in environments and under conditions that closely mimic those encountered in vivo. For example, advances in solid-state NMR methods enable structure determination of membrane proteins in detergent-free lipid bilayers, and of large soluble proteins prepared by sedimentation, while parallel advances in solution NMR methods and optimization of detergent-free lipid nanodiscs are rapidly pushing the envelope of the size limit for both soluble and membrane proteins. These experimental advantages, however, are partially squandered during structure calculation, because the commonly used force fields are purely repulsive and neglect solvation, Van der Waals forces and electrostatic energy. Here we describe a new force field, and updated energy functions, for protein structure calculations with EEFx implicit solvation, electrostatics, and Van der Waals Lennard-Jones forces, in the widely used program Xplor-NIH. The new force field is based primarily on CHARMM22, facilitating calculations with a wider range of biomolecules. The new EEFx energy function has been rewritten to enable OpenMP parallelism, and optimized to enhance computation efficiency. It implements solvation, electrostatics, and Van der Waals energy terms together, thus ensuring more consistent and efficient computation of the complete nonbonded energy lists. Updates in the related python module allow detailed analysis of the interaction energies and associated parameters. The new force field and energy function work with both soluble proteins and membrane proteins, including those with cofactors or engineered tags, and are very effective in situations where there are sparse experimental restraints. Results obtained for NMR-restrained calculations with a set of five soluble proteins and five membrane proteins show that structures calculated with EEFx have significant improvements in accuracy, precision, and conformation, and that structure refinement can be obtained by short relaxation with EEFx to obtain improvements in these key metrics. These developments broaden the range of biomolecular structures that can be calculated with high fidelity from NMR restraints.     

Atomic resolution crystal structures of HIV-1 protease and mutants V82A and I84V with saquinavir

Due to the limited distance data available from the experiments, the structures determined by NMR Spectroscopy may not always be as accurate as desired. Further refinement of the structures is often required and sometimes critical. With the increase of high quality protein structures determined and deposited in PDB Data Bank, commonly shared protein conformational properties can be extracted based on the statistical distributions of the properties in the structural database and used to improve the outcomes of the NMR-determined structures. Here we examine the distributions of protein interatomic distances in known protein structures. We show that based on these distributions, a set of mean-force potentials can be defined for proteins and employed to refine the NMR-determined structures. We report the test results on 70 NMR-determined structures and compare the potential energy, the Ramachandran plot, and the ensemble RMSD of the structures refined with and without using the derived mean-force potentials.     

A target function for quaternary structural refinement from small angle scattering and NMR orientational restraints

We present a novel target function based on atomic coordinates that permits quaternary structural refinement of multi-domain protein–protein or protein–RNA complexes. It requires that the high-resolution structures of the individual domains are known and that small angle scattering (SAS) data as well as NMR orientational restraints from residual dipolar couplings (RDCs) of the complex are available. We show that, when used in combination, the translational and rotational restraints contained in SAS intensities and RDCs, respectively, define a target potential function that permits to determine the overall topology of complexes made up of domains with low internal symmetry. We apply the target function on a modestly anisotropic model system, the Barnase/Barstar complex, and discuss factors that influence the structural refinement such as data errors and the geometrical properties of the individual domains.     

An integrative NMR-SAXS approach for structural determination of large RNAs defines the substrate-free state of a trans-cleaving Neurospora Varkud Satellite ribozyme

The divide-and-conquer strategy is commonly used for protein structure determination, but its applications to high-resolution structure determination of RNAs have been limited. Here, we introduce an integrative approach based on the divide-and-conquer strategy that was undertaken to determine the solution structure of an RNA model system, the Neurospora VS ribozyme. NMR and SAXS studies were conducted on a minimal trans VS ribozyme as well as several isolated subdomains. A multi-step procedure was used for structure determination that first involved pairing refined NMR structures with SAXS data to obtain structural subensembles of the various subdomains. These subdomain structures were then assembled to build a large set of structural models of the ribozyme, which was subsequently filtered using SAXS data. The resulting NMR-SAXS structural ensemble shares several similarities with the reported crystal structures of the VS ribozyme. However, a local structural difference is observed that affects the global fold by shifting the relative orientation of the two three-way junctions. Thus, this finding highlights a global conformational change associated with substrate binding in the VS ribozyme that is likely critical for its enzymatic activity. Structural studies of other large RNAs should benefit from similar integrative approaches that allow conformational sampling of assembled fragments.