On the analysis of membrane protein circular dichroism spectra

Analysis of circular dichroism spectra of proteins provides information about protein secondary structure. Analytical methods developed for such an analysis use structures and spectra of a set of reference proteins. The reference protein sets currently in use include soluble proteins with a wide range of secondary structures, and perform quite well in analyzing CD spectra of soluble proteins. The utility of soluble protein reference sets in analyzing membrane protein CD spectra, however, has been questioned in a recent study that found current reference protein sets to be inadequate for analyzing membrane proteins. We have examined the performance of reference protein sets available in the CDPro software package for analyzing CD spectra of 13 membrane proteins with available crystal structures. Our results indicate that the reference protein sets currently available for CD analysis perform reasonably well in analyzing membrane protein CD spectra, with performance indices comparable to those for soluble proteins. Soluble + membrane protein reference sets, which were constructed by combining membrane proteins with soluble protein reference sets, gave improved performance in both soluble and membrane protein CD analysis.     

Estimation of protein secondary structure from circular dichroism spectra: inclusion of denatured proteins with native proteins in the analysis

We have expanded our reference set of proteins used in the estimation of protein secondary structure by CD spectroscopy from 29 to 37 proteins by including 3 additional globular proteins with known X-ray structure and 5 denatured proteins. We have also modified the self-consistent method for analyzing protein CD spectra, SELCON3, by including a new selection criterion developed by W. C. Johnson, Jr. (Proteins Struct. Funct. Genet. 35, 307-312, 1999). The secondary structure corresponding to the denatured proteins was approximated to be 90% unordered, owing to the spectral similarity of the denatured proteins and unordered structures. We examined the thermal denaturation of ribonuclease T1 by CD using both the original and expanded sets of reference proteins and obtained more consistent results with the expanded set. The expanded set of reference proteins will be helpful for the determination of protein secondary structure from protein CD spectra with higher reliability, especially of proteins with significant unordered structure content and/or in the course of denaturation.     

Calculation of standard atomic volumes for RNA and comparison with proteins: RNA is packed more tightly

Traditionally, for biomolecular packing calculations research has focused on proteins. Besides proteins, RNA is the other large biomolecule that has tertiary structure interactions and complex packing. No one has yet quantitatively investigated RNA packing or compared its packing to that of proteins because, until recently, there were no large RNA structures. Here we address this question in detail, using Voronoi volume calculations on a set of high-resolution RNA crystal structures. We do a careful parameterization, taking into account many factors such as atomic radii, crystal packing, structural complexity, solvent, and associated protein to obtain a self-consistent, universal set of volumes that can be applied to both RNA and protein. We report this set of volumes, which we call the NucProt parameter set. Our measured values are consistent across the many different RNA structures and packing environments. When common atom types are compared between proteins and RNA, nine of 12 types show that RNA has a smaller volume and packs more tightly than protein, suggesting that close-packing may be as important for the folding of RNAs as it is for proteins. Moreover, calculated partial specific volumes show that RNA bases pack more densely than corresponding aromatic residues from proteins. Finally, we find that RNA bases have similar packing volumes to DNA bases, despite the absence of tertiary contacts in DNA. Programs, parameter sets and raw data are available online at.     

Predicting experimental properties of integral membrane proteins by a naive Bayes approach

Integral membrane proteins (iMPs) are challenging targets for structure determination because of the substantial experimental difficulties involved in their sample preparation. Accordingly, success rates of large-scale structural genomics consortia are much lower for this class of molecules compared to globular targets, underscoring the pressing need for predictive strategies to identify iMPs that are more likely to overcome laboratory bottlenecks. On the basis of the target status information available in the TargetDB repository, we describe the first large-scale analysis of experimental behavior of iMPs. Using information on recalcitrant and propagating iMP targets as negative and positive sets, respectively, we present naive Bayes classifiers capable of predicting, from sequence alone, those proteins that are more amenable to cloning, expression, and solubilization studies. Protein sequences are represented in the space of 72 features, including amino acid composition, occurrence of amino acid groups, ratios between residue groups, and hydrophobicity measures. Taking into account unequal representation of main taxonomic groups in the TargetDB, sequence database had a beneficial effect on the prediction results. The classifiers achieve accuracies of 70%, 63-70%, and 61% in predicting the amenability of iMPs for cloning, expression, and solubilization, respectively, thus making them useful tools in target selection for structure determination. Our assessment of prediction results clearly demonstrates that classifiers based on single features do not possess acceptable discriminative power and that the experimental behavior of iMPs is imprinted in their primary sequence through relationships between a restricted set of key properties. In most cases, sets of 10-20 protein features were found actually relevant, most notably, the content of isoleucine, valine, and positively-charged residues.     

Crystal structures of fusion proteins with large-affinity tags

The fusion of a protein of interest to a large-affinity tag, such as the maltose-binding protein (MBP), thioredoxin (TRX), or glutathione-S-transferase (GST), can be advantageous in terms of increased expression, enhanced solubility, protection from proteolysis, improved folding, and protein purification via affinity chromatography. Unfortunately, crystal growth is hindered by the conformational heterogeneity induced by the fusion tag, requiring that the tag is removed by a potentially problematic cleavage step. The first three crystal structures of fusion proteins with large-affinity tags have been reported recently. All three structures used a novel strategy to rigidly fuse the protein of interest to MBP via a short three- to five-amino acid spacer. This strategy has the potential to aid structure determination of proteins that present particular experimental challenges and are not conducive to more conventional crystallization strategies (e.g., membrane proteins). Structural genomics initiatives may also benefit from this approach as a way to crystallize problematic proteins of significant interest.     

The use of soluble protein structures in modeling helical proteins in a layered membrane

Major advances have been made in the prediction of soluble protein structures, led by the knowledge-based modeling methods that extract useful structural trends from known protein structures and incorporate them into scoring functions. The same cannot be reported for the class of transmembrane proteins, primarily due to the lack of high-resolution structural data for transmembrane proteins, which render many of the knowledge-based method unreliable or invalid. We have developed a method that harnesses the vast structural knowledge available in soluble protein data for use in the modeling of transmembrane proteins. At the core of the method, a set of transmembrane protein decoy sets that allow us to filter and train features recognized from soluble proteins for transmembrane protein modeling into a set of scoring functions. We have demonstrated that structures of soluble proteins can provide significant insight into transmembrane protein structures. A complementary novel two-stage modeling/selection process that mimics the two-stage helical membrane protein folding was developed. Combined with the scoring function, the method was successfully applied to model 5 transmembrane proteins. The root mean square deviations of the predicted models ranged from 5.0 to 8.8?Å to the native structures.     

Sequence-structure mapping errors in the PDB: OB-fold domains

The Protein Data Bank (PDB) is the single most important repository of structural data for proteins and other biologically relevant molecules. Therefore, it is critically important to keep the PDB data, as much as possible, error-free. In this study, we have analyzed PDB crystal structures possessing oligonucleotide/oligosaccharide binding (OB)-fold, one of the highly populated folds, for the presence of sequence-structure mapping errors. Using energy-based structure quality assessment coupled with sequence analyses, we have found that there are at least five OB-structures in the PDB that have regions where sequences have been incorrectly mapped onto the structure. We have demonstrated that the combination of these computation techniques is effective not only in detecting sequence-structure mapping errors, but also in providing guidance to correct them. Namely, we have used results of computational analysis to direct a revision of X-ray data for one of the PDB entries containing a fairly inconspicuous sequence-structure mapping error. The revised structure has been deposited with the PDB. We suggest use of computational energy assessment and sequence analysis techniques to facilitate structure determination when homologs having known structure are available to use as a reference. Such computational analysis may be useful in either guiding the sequence-structure assignment process or verifying the sequence mapping within poorly defined regions.     

Common occurrence of internal repeat symmetry in membrane proteins

Symmetry plays significant roles in protein structure and function. Particularly, symmetric interfaces are known to act as switches for two-state conformational change. Membrane proteins often undergo two-state conformational change during the transport process of ion channels or the active/inactive transitions in receptors. Here, we provide the first comprehensive analyses of internal repeat symmetry in membrane proteins. We examined the known membrane protein structures and found that, remarkably, nearly half of them have internal repeat symmetry. Moreover, we found that the conserved cores of these internal repeats are positioned at the interface of symmetric units when they are mapped on structures. Because of the large sequence divergence that occurs between internal repeats, the inherent symmetry present in protein sequences often has only been detected after structure determination. We therefore developed a sensitive procedure to predict the internal repeat symmetry from sequence information and identified 4653 proteins that are likely to have internal repeat symmetry.     

Recognition of errors in three-dimensional structures of proteins

A major problem in the determination of the three-dimensional structure of proteins concerns the quality of the structural models obtained from the interpretation of experimental data. New developments in X-ray crystallography and nuclear magnetic resonance spectroscopy have acceleratedd the process of structure determination and the biological community is confronted with a steadily increasing number of experimentally determined protein folds. However, in the recent past several experimentally determined protein structures have been proven to contain major errors, indicating that in some cases the interpretation of experimental data is difficult and may yield incorrect models. Such problems can be avoided when computational methods are employed which complement experimental structure determinations. A prerequisite of such computational tools is that they are independent of the parameters obtained from a particular experiment. In addition such techniques are able to support and accelerate experimental structure determinations. Here we present techniques based on knowledge based mean fields which can be used to judge the quality of protein folds. The methods can be used to identify misfolded structures as well as faulty parts of structural models. The techniques are even applicable in cases where only the C_α trace of a protein conformation is available. The capabilities of the technique are demonstrated using correct and incorrect protein folds. © 1993 Wiley-Liss, Inc.     

Protein biophysical properties that correlate with crystallization success in Thermotoga maritima: maximum clustering strategy for structural genomics

Cost and time reduction are two of the driving forces in the development of new strategies for protein crystallization and subsequent structure determination. Here, we report the analysis of the Thermotoga maritima proteome, in which we compare the proteins that were successfully expressed, purified and crystallized versus the rest of the proteome. This set of almost 500 proteins represents one of the largest, internally consistent, protein expression and crystallization datasets available. The analysis shows that individual parameters, such as isoelectric point, sequence length, average hydropathy, low complexity regions (SEG), and combinations of these biophysical properties for crystallized proteins define a distinct subset of the T. maritima proteome. The distribution profiles of the various biophysical properties in the expression/crystallization set are then used to extract rules to improve target selection and improve the efficiency and output of structural genomics, as well as general structural biology efforts.     

Bacterial microcompartment shells of diverse functional types possess pentameric vertex proteins

Bacterial microcompartments (MCPs) are large proteinaceous structures comprised of a roughly icosahedral shell and a series of encapsulated enzymes. MCPs carrying out three different metabolic functions have been characterized in some detail, while gene expression and bioinformatics studies have implicated other types, including one believed to perform g lycyl r adical‐based metabolism of 1,2‐p ropanediol (Grp). Here we report the crystal structure of a protein (GrpN), which is presumed to be part of the shell of a Grp‐type MCP in Rhodospirillum rubrum F11. GrpN is homologous to a family of proteins (EutN/PduN/CcmL/CsoS4) whose members have been implicated in forming the vertices of MCP shells. Consistent with that notion, the crystal structure of GrpN revealed a pentameric assembly. That observation revived an outstanding question about the oligomeric state of this protein family: pentameric forms (for CcmL and CsoS4A) and a hexameric form (for EutN) had both been observed in previous crystal structures. To clarify these confounding observations, we revisited the case of EutN. We developed a molecular biology‐based method for accurately determining the number of subunits in homo‐oligomeric proteins, and found unequivocally that EutN is a pentamer in solution. Based on these convergent findings, we propose the name bacterial microcompartment vertex for this special family of MCP shell proteins.     

A structure and evolution-guided Monte Carlo sequence selection strategy for multiple alignment-based analysis of proteins

MOTIVATION: Various multiple sequence alignment-based methods have been proposed to detect functional surfaces in proteins, such as active sites or protein interfaces. The effect that the choice of sequences has on the conclusions of such analysis has seldom been discussed. In particular, no method has been discussed in terms of its ability to optimize the sequence selection for the reliable detection of functional surfaces. RESULTS: Here we propose, for the case of proteins with known structure, a heuristic Metropolis Monte Carlo strategy to select sequences from a large set of homologues, in order to improve detection of functional surfaces. The quantity guiding the optimization is the clustering of residues which are under increased evolutionary pressure, according to the sample of sequences under consideration. We show that we can either improve the overlap of our prediction with known functional surfaces in comparison with the sequence similarity criteria of selection or match the quality of prediction obtained through more elaborate non-structure based-methods of sequence selection. For the purpose of demonstration we use a set of 50 homodimerizing enzymes which were co-crystallized with their substrates and cofactors.     

Prediction of protein secondary structure from circular dichroism using theoretically derived spectra

Circular dichroism (CD) is a spectroscopic technique commonly used to investigate the structure of proteins. Major secondary structure types, alpha‐helices and beta‐strands, produce distinctive CD spectra. Thus, by comparing the CD spectrum of a protein of interest to a reference set consisting of CD spectra of proteins of known structure, predictive methods can estimate the secondary structure of the protein. Currently available methods, including K2D2, use such experimental CD reference sets, which are very small in size when compared to the number of tertiary structures available in the Protein Data Bank (PDB). Conversely, given a PDB structure, it is possible to predict a theoretical CD spectrum from it. The methodological framework for this calculation was established long ago but only recently a convenient implementation called DichroCalc has been developed. In this study, we set to determine whether theoretically derived spectra could be used as reference set for accurate CD based predictions of secondary structure. We used DichroCalc to calculate the theoretical CD spectra of a nonredundant set of structures representing most proteins in the PDB, and applied a straightforward approach for predicting protein secondary structure content using these theoretical CD spectra as reference set. We show that this method improves the predictions, particularly for the wavelength interval between 200 and 240 nm and for beta‐strand content. We have implemented this method, called K2D3, in a publicly accessible web server at http://www. ogic.ca/projects/k2d3 . Proteins 2012. © 2011 Wiley Periodicals, Inc.     

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.     

Relaxation data in NMR structure determination: model calculations for the lysozyme-Gd3+ complex

The effect of including paramagnetic relaxation data as additional restraints in the determination of protein tertiary structures from NMR data has been explored by a systematic series of model calculations. The system used for testing the method was the 2.0 A resolution tetragonal crystal structure of hen egg white lysozyme (129 amino acid residues) and structures were generated using a version of the hybrid "distance geometry-dynamic simulated annealing" procedure. A limited set of 769 NOEs was used as restraints in all the calculations; the strengths of these were categorized into three classes on the basis of distances observed in the crystal structure. The values of 50 phi angles were also restrained on the basis of amide-alpha coupling constants calculated from the X-ray structure. Five sets of 12 structures were determined using differing sets of paramagnetic relaxation data as restraints additional to those involving the NOE and coupling constant data. The paramagnetic relaxation data were modeled on the basis of the distances of defined protons from the crystallographic binding site of Gd3+ in lysozyme. Analysis of the results showed that the relaxation data significantly improved the correspondence between the set of generated structures and the crystal structure, and that the more well defined the relaxation data, the more significant the improvement in the quality of the structures. The results suggest that the inclusion of paramagnetic relaxation restraints could be of significant value for the experimental determination of protein structures from NMR data.     

DRoP: Automated detection of conserved solvent-binding sites on proteins

Water and ligand binding play critical roles in the structure and function of proteins, yet their binding sites and significance are difficult to predict a priori. Multiple solvent crystal structures (MSCS) is a method where several X-ray crystal structures are solved, each in a unique solvent environment, with organic molecules that serve as probes of the protein surface for sites evolved to bind ligands, while the first hydration shell is essentially maintained. When superimposed, these structures contain a vast amount of information regarding hot spots of protein-protein or protein-ligand interactions, as well as conserved water-binding sites retained with the change in solvent properties. Optimized mining of this information requires reliable structural data and a consistent, objective analysis tool. Detection of related solvent positions (DRoP) was developed to automatically organize and rank the water or small organic molecule binding sites within a given set of structures. It is a flexible tool that can also be used in conserved water analysis given multiple structures of any protein independent of the MSCS method. The DRoP output is an HTML format list of the solvent sites ordered by conservation rank in its population within the set of structures, along with renumbered and recolored PDB files for visualization and facile analysis. Here, we present a previously unpublished set of MSCS structures of bovine pancreatic ribonuclease A (RNase A) and use it together with published structures to illustrate the capabilities of DRoP.     

Automatic procedure for using models of proteins in molecular replacement

In a crystallography experiment, a crystal is irradiated with X-rays whose diffracted waves are collected and measured. The reconstruction of the structure of the molecule in the crystal requires knowledge of the phase of the diffracted waves, information that is lost in the passage from the three-dimensional structure of the molecule to its diffraction pattern. It can be recovered using experimental methods such as heavy-atom isomorphous replacement and anomalous scattering or by molecular replacement, which relies on the availability of an atomic model of the target structure. This can be the structure of the target protein itself, if a previous structure determination is available, or a computational model or, in some cases, the structure of a homologous protein. It is not straightforward to predict beforehand whether or not a computational model will work in a molecular replacement experiment, although some rules of thumb exist. The consensus is that even minor differences in the quality of the model, which are rather difficult to estimate a priori, can have a significant effect on the outcome of the procedure. We describe here a method for quickly assessing whether a protein structure can be solved by molecular replacement. The procedure consists in submitting the sequence of the target protein to a selected list of freely available structure prediction servers, cluster the resulting models, select the representative structures of each cluster and use them as search models in an automatic phasing procedure. We tested the procedure using the structure factors of newly released proteins of known structure downloaded from the Protein Data Bank as soon as they were made available. Using our automatic procedure we were able to obtain an interpretable electron density map in more than half the cases.     

Analyses of circular dichroism spectra of membrane proteins

Circular dichroism (CD) spectroscopy is a valuable technique for the determination of protein secondary structures. Many linear and nonlinear algorithms have been developed for the empirical analysis of CD data, using reference databases derived from proteins of known structures. To date, the reference databases used by the various algorithms have all been derived from the spectra of soluble proteins. When applied to the analysis of soluble protein spectra, these methods generally produce calculated secondary structures that correspond well with crystallographic structures. In this study, however, it was shown that when applied to membrane protein spectra, the resulting calculations produce considerably poorer results. One source of this discrepancy may be the altered spectral peak positions (wavelength shifts) of membrane proteins due to the different dielectric of the membrane environment relative to that of water. These results have important consequences for studies that seek to use the existing soluble protein reference databases for the analyses of membrane proteins.     

Highly efficient selenomethionine labeling of recombinant proteins produced in mammalian cells

The advent of the multiwavelength anomalous diffraction phasing method has significantly accelerated crystal structure determination and has become the norm in protein crystallography. This method allows researchers to take advantage of the anomalous signal from diverse atoms, but the dominant method for derivative preparation is selenomethionine substitution. Several generally applicable, high-efficiency labeling protocols have been developed for use in the bacterial, yeast, and baculovirus/insect cell expression systems but not for mammalian tissue culture. As a large number of proteins of biomedical importance can only be produced in yields sufficient for X-ray diffraction experiments in mammalian expression systems, it becomes all the more important to develop such protocols. We therefore evaluated several variables that play roles in determining incorporation levels and report here a simple protocol for selenomethionine modification of proteins in mammalian cells routinely yielding >90% labeling efficiency.