BioMagResBank

Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY

Background

Chemical shift mapping is an important technique in NMR-based drug screening for identifying the atoms of a target protein that potentially bind to a drug molecule upon the molecule's introduction in increasing concentrations. The goal is to obtain a mapping of peaks with known residue assignment from the reference spectrum of the unbound protein to peaks with unknown assignment in the target spectrum of the bound protein. Although a series of perturbed spectra help to trace a path from reference peaks to target peaks, a one-to-one mapping generally is not possible, especially for large proteins, due to errors, such as noise peaks, missing peaks, missing but then reappearing, overlapped, and new peaks not associated with any peaks in the reference. Due to these difficulties, the mapping is typically done manually or semi-automatically, which is not efficient for high-throughput drug screening.

Results

We present PeakWalker, a novel peak walking algorithm for fast-exchange systems that models the errors explicitly and performs many-to-one mapping. On the proteins: hBcl_XL, UbcH5B, and histone H1, it achieves an average accuracy of over 95% with less than 1.5 residues predicted per target peak. Given these mappings as input, we present PeakAssigner, a novel combined structure-based backbone resonance and NOE assignment algorithm that uses just ¹⁵N-NOESY, while avoiding TOCSY experiments and ¹³C-labeling, to resolve the ambiguities for a one-to-one mapping. On the three proteins, it achieves an average accuracy of 94% or better.

Conclusions

Our mathematical programming approach for modeling chemical shift mapping as a graph problem, while modeling the errors directly, is potentially a time- and cost-effective first step for high-throughput drug screening based on limited NMR data and homologous 3D structures.

A helical conotoxin from Conus imperialis has a novel cysteine framework and defines a new superfamily

Cone snail venoms are a rich source of peptides, many of which are potent and selective modulators of ion channels and receptors. Here we report the isolation and characterization of two novel conotoxins from the venom of Conus imperialis. These two toxins contain a novel cysteine framework, C-C-C-CC-C, which has not been found in other conotoxins described to date. We name it framework XXIII and designate the two toxins im23a and im23b; cDNAs of these toxins exhibit a novel signal peptide sequence, which defines a new K-superfamily. The disulfide connectivity of im23a has been mapped by chemical mapping of partially reduced intermediates and by NMR structure calculations, both of which establish a I-II, III-IV, V-VI pattern of disulfide bridges. This pattern was also confirmed by synthesis of im23a with orthogonal protection of individual cysteine residues. The solution structure of im23a reveals that im23a adopts a novel helical hairpin fold. A cluster of acidic residues on the surface of the molecule is able to bind calcium. The biological activity of the native and recombinant peptides was tested by injection into mice intracranially and intravenously to assess the effects on the central and peripheral nervous systems, respectively. Intracranial injection of im23a or im23b into mice induced excitatory symptoms; however, the biological target of these new toxins has yet to be identified.

Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction

While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein (1)H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6-17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for (1)Hα, (1)HN, (13)Cα, (13)Cβ, (13)CO and backbone (15)N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at http://www.uef.fi/4dspot.

Metabolic discrimination of mango juice from various cultivars by band-selective NMR spectroscopy

NMR-based metabolic analysis of foods has been widely applied in food science. In this study, we performed discrimination of five different mango cultivars, Awin, Carabao, Keitt, Kent, and Nam Dok Mai, using metabolic analysis with band-selective excitation NMR spectra. A combination of unsupervised principal component analysis (PCA) with low-field region (1)H NMR spectra obtained by band-selective excitation provided a good discriminant model of the five mango cultivars. Using F(2)-selective 2D NMR spectra, we also identified various minor components in the mango juice. Signal assignment of the minor components facilitated the interpretation of the loading plot, and it was found that arginine, histidine, phenylalanine, glutamine, shikimic acid, and trigonelline were important for classification of the five mango cultivars.

Synergistic applications of MD and NMR for the study of biological systems

Modern biological sciences are becoming more and more multidisciplinary. At the same time, theoretical and computational approaches gain in reliability and their field of application widens. In this short paper, we discuss recent advances in the areas of solution nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations that were made possible by the combination of both methods, that is, through their synergistic use. We present the main NMR observables and parameters that can be computed from simulations, and how they are used in a variety of complementary applications, including dynamics studies, model-free analysis, force field validation, and structural studies.

KB-Rank: efficient protein structure and functional annotation identification via text query

The KB-Rank tool was developed to help determine the functions of proteins. A user provides text query and protein structures are retrieved together with their functional annotation categories. Structures and annotation categories are ranked according to their estimated relevance to the queried text. The algorithm for ranking first retrieves matches between the query text and the text fields associated with the structures. The structures are next ordered by their relative content of annotations that are found to be prevalent across all the structures retrieved. An interactive web interface was implemented to navigate and interpret the relevance of the structures and annotation categories retrieved by a given search. The aim of the KB-Rank tool is to provide a means to quickly identify protein structures of interest and the annotations most relevant to the queries posed by a user. Informational and navigational searches regarding disease topics are described to illustrate the tool’s utilities. The tool is available at the URL http://protein.tcmedc.org/KB-Rank.

Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies

A set of software tools for building and distributing models of macromolecular assemblies uses an integrative structure modeling approach, which casts the building of models as a computational optimization problem where information is encoded into a scoring function used to evaluate candidate models.

Metabolome analysis of gram-positive bacteria such as Staphylococcus aureus by GC-MS and LC-MS

The field of metabolomics has become increasingly important in the context of functional genomics. Together with other "omics" data, the investigation of the metabolome is an essential part of systems biology. Beside the analysis of human and animal biofluids, the investigation of the microbial physiology by methods of metabolomics has gained increased attention. For example, the analysis of metabolic processes during growth or virulence factor expression is crucially important to understand pathogenesis of bacteria. Common bioanalytical techniques for metabolome analysis include liquid and gas chromatographic methods coupled to mass spectrometry (LC-MS and GC-MS) and spectroscopic approaches such as NMR. In order to achieve metabolome data representing the physiological status of a microorganism, well-verified protocols for sampling and analysis are necessary. This chapter presents a detailed protocol for metabolome analysis of the Gram-positive bacterium Staphylococcus aureus. A detailed manual for cell sampling and metabolite extraction is given, followed by the description of the analytical procedures GC-MS and LC-MS. The advantages and limitations of each experimental setup are discussed. Here, a guideline specified for S. aureus metabolomics and information for important protocol steps are presented, to avoid common pitfalls in microbial metabolome analysis.

Can Biofluids Metabolic Profiling Help to Improve Healthcare during Pregnancy?

This paper describes a metabonomics study of 2nd trimester biofluids (amniotic fluid, maternal urine, and blood plasma), in an attempt to correlate biofluid metabolic changes with suspected/diagnosed fetal malformations (FM) and chromosomal disorders as well as with later occurring gestational diabetes mellitus (GDM), preterm delivery (PTD), and premature rupture of membranes (PROM). The global biochemical picture given by the threesome of biofluids should enable the definition of potential disease signatures and unveil potential metabolite markers for clinical use in predictive prenatal diagnostics. Results show that relatively strong metabolic disturbances accompany FM, reflected in all three biofluids and thus suggesting the involvement of both fetal and maternal metabolisms. Regarding GDM, amniotic fluid and maternal urine seem potential good media to detect early metabolic changes, and PTD subjects show small metabolite changes in the same biofluids, undergoing work being focused on plasma composition. Chromosomal disorders show an interestingly marked effect on maternal urine, whereas no statistically relevant early changes have been observed for PROM subjects. Interestingly, in the case of FM and chromosomal disorders, maternal biofluids show some sensitivity to disorder type, for example, for central nervous system malformations and trisomy 21, respectively. These results show the usefulness of biofluid metabonomics to probe overall metabolic disturbances in relation to prenatal disorders.

Advances in metabolite identification

One of the central challenges to metabolomics is metabolite identification. Regardless of whether one uses so-called 'targeted' or 'untargeted' metabolomics, eventually all paths lead to the requirement of identifying (and quantifying) certain key metabolites. Indeed, without metabolite identification, the results of any metabolomic analysis are biologically and chemically uninterpretable. Given the chemical diversity of most metabolomes and the character of most metabolomic data, metabolite identification is intrinsically difficult. Consequently a great deal of effort in metabolomics over the past decade has been focused on making metabolite identification better, faster and cheaper. This review describes some of the newly emerging techniques or technologies in metabolomics that are making metabolite identification easier and more robust. In particular, it focuses on advances in metabolite identification that have occurred over the past 2 to 3 years concerning the technologies, methodologies and software as applied to NMR, MS and separation science. The strengths and limitations of some of these approaches are discussed along with some of the important trends in metabolite identification.

Ligand-specific structural changes in the vitamin D receptor in solution

Vitamin D receptor (VDR) is a member of the nuclear hormone receptor superfamily. When bound to a variety of vitamin D analogues, VDR manifests a wide diversity of physiological actions. The molecular mechanism by which different vitamin D analogues cause specific responses is not understood. The published crystallographic structures of the ligand binding domain of VDR (VDR-LBD) complexed with ligands that have differential biological activities have exhibited identical protein conformations. Here we report that rat VDR-LBD (rVDR-LBD) in solution exhibits differential chemical shifts when bound to three ligands that cause diverse responses: the natural hormone, 1,25-dihydroxyvitamin D(3) [1,25(OH)₂D₃], a potent agonist analogue, 2-methylene-19-nor-(20S)-1,25-dihydroxyvitamin D₃ [2MD], and an antagonist, 2-methylene-(22E)-(24R)-25-carbobutoxy-26,27-cyclo-22-dehydro-1α,24-dihydroxy-19-norvitamin D₃ [OU-72]. Ligand-specific chemical shifts mapped not only to residues at or near the binding pocket but also to residues remote from the ligand binding site. The complexes of rVDR-LBD with native hormone and the potent agonist 2MD exhibited chemical shift differences in signals from helix-12, which is part of the AF2 transactivation domain that appears to play a role in the selective recruitment of coactivators. By contrast, formation of the complex of rVDR-LBD with the antagonist OU-72 led to disappearance of signals from residues in helices-11 and -12. We present evidence that disorder in this region of the receptor in the antagonist complex prevents the attachment of coactivators.

NRG-CING: integrated validation reports of remediated experimental biomolecular NMR data and coordinates in wwPDB

For many macromolecular NMR ensembles from the Protein Data Bank (PDB) the experiment-based restraint lists are available, while other experimental data, mainly chemical shift values, are often available from the BioMagResBank. The accuracy and precision of the coordinates in these macromolecular NMR ensembles can be improved by recalculation using the available experimental data and present-day software. Such efforts, however, generally fail on half of all NMR ensembles due to the syntactic and semantic heterogeneity of the underlying data and the wide variety of formats used for their deposition. We have combined the remediated restraint information from our NMR Restraints Grid (NRG) database with available chemical shifts from the BioMagResBank and the Common Interface for NMR structure Generation (CING) structure validation reports into the weekly updated NRG-CING database (http://nmr.cmbi.ru.nl/NRG-CING). Eleven programs have been included in the NRG-CING production pipeline to arrive at validation reports that list for each entry the potential inconsistencies between the coordinates and the available experimental NMR data. The longitudinal validation of these data in a publicly available relational database yields a set of indicators that can be used to judge the quality of every macromolecular structure solved with NMR. The remediated NMR experimental data sets and validation reports are freely available online.

Uncovering symmetry-breaking vector and reliability order for assigning secondary structures of proteins from atomic NMR chemical shifts in amino acids

Unravelling the complex correlation between chemical shifts of (13) C (α), (13) C (β), (13) C', (1) H (α), (15) N, (1) H ( N ) atoms in amino acids of proteins from NMR experiment and local structural environments of amino acids facilitates the assignment of secondary structures of proteins. This is an important impetus for both determining the three-dimensional structure and understanding the biological function of proteins. The previous empirical correlation scores which relate chemical shifts of (13) C (α), (13) C (β), (13) C', (1) H (α), (15) N, (1) H ( N ) atoms to secondary structures resulted in progresses toward assigning secondary structures of proteins. However, the physical-mathematical framework for these was elusive partly due to both the limited and orthogonal exploration of higher-dimensional chemical shifts of hetero-nucleus and the lack of physical-mathematical understanding underlying those correlation scores. Here we present a simple multi-dimensional hetero-nuclear chemical shift score function (MDHN-CSSF) which captures systematically the salient feature of such complex correlations without any references to a random coil state of proteins. We uncover the symmetry-breaking vector and its reliability order not only for distinguishing different secondary structures of proteins but also for capturing the delicate sensitivity interplayed among chemical shifts of (13) C (α), (13) C (β), (13) C', (1) H (α), (15) N, (1) H ( N ) atoms simultaneously, which then provides a straightforward framework toward assigning secondary structures of proteins. MDHN-CSSF could correctly assign secondary structures of training (validating) proteins with the favourable (comparable) Q3 scores in comparison with those from the previous correlation scores. MDHN-CSSF provides a simple and robust strategy for the systematic assignment of secondary structures of proteins and would facilitate the de novo determination of three-dimensional structures of proteins.

Chemical shifts for the unusual DNA structure in Pf1 bacteriophage from dynamic-nuclear-polarization-enhanced solid-state NMR spectroscopy

Solid-state NMR spectra, including dynamic nuclear polarization enhanced 400 MHz spectra acquired at 100 K, as well as non-DNP spectra at a variety of field strengths and at temperatures in the range 213-243 K, have allowed the assignment of the (13)C and (15)N resonances of the unusual DNA structure in the Pf1 virion. The (13)C chemical shifts of C3' and C5', considered to be key reporters of deoxyribose conformation, fall near or beyond the edges of their respective ranges in available databases. The (13)C and (15)N chemical shifts of the DNA bases have above-average values for AC4, AC5, CC5, TC2, and TC5, and below average values for AC8, GC8, and GN2, pointing to an absence of Watson-Crick hydrogen bonding, yet the presence of some type of aromatic ring interaction. Crosspeaks between Tyr40 of the coat protein and several DNA atoms suggest that Tyr40 is involved in this ring interaction. In addition, these crosspeak resonances and several deoxyribose resonances are multiply split, presumably through the effects of ordered but differing interactions between capsid protein subunits and each type of nucleotide in each of the two DNA strands. Overall, these observations characterize and support the DNA model proposed by Liu and Day and refined by Tsuboi et al., which calls for the most highly stretched and twisted naturally occurring DNA yet encountered.

Urine metabolic signature of pancreatic ductal adenocarcinoma by (1)h nuclear magnetic resonance: identification, mapping, and evolution

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis and is highly chemoresistant. Early detection is the only means to impact long-term survival, but screening methods are lacking. Given the complex and heterogeneous nature of pancreatic cancer, unbiased analytical methods such as metabolomics by nuclear magnetic resonance (NMR) spectroscopy show promise to identify disease-specific molecular fingerprints. NMR profiles constitute a fingerprint of the biofluid, reporting quantitatively on all detectable small biomolecules. NMR spectroscopy was applied to investigate the urine metabolome of PDAC patients (n = 33) and to detect altered metabolic profiles in comparison with healthy matched controls (n = 54). The spectral data were analyzed using multivariate statistical techniques. Statistically significant differences were found between urine metabolomic profiles of PDAC and control individuals (p < 10(-5)). Group discrimination was possible due to average concentration differences of several metabolite signals, pointing to a multimolecular signature of the disease. The robustness of the determined statistical model is confirmed by its predictive performance (sensitivity = 75.8%, specificity = 90.7%). Additionally, the method allowed for a neat separation between spectral profiles of individuals with intermediate and advanced pathologic staging, as well as for the discrimination of samples based on tumor localization. NMR spectroscopy analysis of urinary metabolic profiles proved successful in identifying a complex molecular signature of PDAC. Furthermore, results of a descriptive-level analysis show the possibility to follow disease evolution and to carry out tumor site mapping. Given the high reproducibility and the noninvasive nature of the analytical procedure, the described method bears potential to impact large-scale screening programs.

Transient, sparsely populated compact states of apo and calcium-loaded calmodulin probed by paramagnetic relaxation enhancement: interplay of conformational selection and induced fit

Calmodulin (CaM) is the universal calcium sensor in eukaryotes, regulating the function of numerous proteins. Crystallography and NMR show that free CaM-4Ca(2+) exists in an extended conformation with significant interdomain separation, but clamps down upon target peptides to form a highly compact structure. NMR has revealed substantial interdomain motions in CaM-4Ca(2+), enabled by a flexible linker. In one instance, CaM-4Ca(2+) has been crystallized in a compact configuration; however, no direct evidence for transient interdomain contacts has been observed in solution, and little is known about how large-scale interdomain motions contribute to biological function. Here, we use paramagnetic relaxation enhancement (PRE) to characterize transient compact states of free CaM that are too sparsely populated to observe by traditional NMR methods. We show that unbound CaM samples a range of compact structures, populated at 5-10%, and that Ca(2+) dramatically alters the distribution of these configurations in favor of states resembling the peptide-bound structure. In the absence of Ca(2+), the target peptide binds only to the C-terminal domain, and the distribution of compact states is similar with and without peptide. These data suggest an alternative pathway of CaM action in which CaM remains associated with its kinase targets even in the resting state. Only CaM-4Ca(2+), however, shows an innate propensity to form the physiologically active compact structures, suggesting that Ca(2+) activates CaM not only through local structural changes within each domain but also through more global remodeling of interdomain interactions. Thus, these findings illustrate the subtle interplay between conformational selection and induced fit.

PROTEIN SECONDARY STRUCTURE PREDICTION USING NMR CHEMICAL SHIFT DATA

Accurate determination of protein secondary structure from the chemical shift information is a key step for NMR tertiary structure determination. Relatively few work has been done on this subject. There needs to be a systematic investigation of algorithms that are (a) robust for large datasets; (b) easily extendable to (the dynamic) new databases; and (c) approaching to the limit of accuracy. We introduce new approaches using k-nearest neighbor algorithm to do the basic prediction and use the BCJR algorithm to smooth the predictions and combine different predictions from chemical shifts and based on sequence information only. Our new system, SUCCES, improves the accuracy of all existing methods on a large dataset of 805 proteins (at 86% Q3 accuracy and at 92.6% accuracy when the boundary residues are ignored), and it is easily extendable to any new dataset without requiring any new training. The software is publicly available at .

PDBe: Protein Data Bank in Europe

The Protein Data Bank in Europe (PDBe; pdbe.org) is a partner in the Worldwide PDB organization (wwPDB; wwpdb.org) and as such actively involved in managing the single global archive of biomacromolecular structure data, the PDB. In addition, PDBe develops tools, services and resources to make structure-related data more accessible to the biomedical community. Here we describe recently developed, extended or improved services, including an animated structure-presentation widget (PDBportfolio), a widget to graphically display the coverage of any UniProt sequence in the PDB (UniPDB), chemistry- and taxonomy-based PDB-archive browsers (PDBeXplore), and a tool for interactive visualization of NMR structures, corresponding experimental data as well as validation and analysis results (Vivaldi).

STAP Refinement of the NMR database: a database of 2405 refined solution NMR structures

According to several studies, some nuclear magnetic resonance (NMR) structures are of lower quality, less reliable and less suitable for structural analysis than high-resolution X-ray crystallographic structures. We present a public database of 2405 refined NMR solution structures [statistical torsion angle potentials (STAP) refinement of the NMR database, http://psb.kobic.re.kr/STAP/refinement] from the Protein Data Bank (PDB). A simulated annealing protocol was employed to obtain refined structures with target potentials, including the newly developed STAP. The refined database was extensively analysed using various quality indicators from several assessment programs to determine the nuclear Overhauser effect (NOE) completeness, Ramachandran appearance, χ₁-χ₂ rotamer normality, various parameters for protein stability and other indicators. Most quality indicators are improved in our protocol mainly due to the inclusion of the newly developed knowledge-based potentials. This database can be used by the NMR structure community for further development of research and validation tools, structure-related studies and modelling in many fields of research.

YMDB: the Yeast Metabolome Database

The Yeast Metabolome Database (YMDB, http://www.ymdb.ca) is a richly annotated ‘metabolomic’ database containing detailed information about the metabolome of Saccharomyces cerevisiae. Modeled closely after the Human Metabolome Database, the YMDB contains >2000 metabolites with links to 995 different genes/proteins, including enzymes and transporters. The information in YMDB has been gathered from hundreds of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the YMDB also contains an extensive collection of experimental intracellular and extracellular metabolite concentration data compiled from detailed Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR) metabolomic analyses performed in our lab. This is further supplemented with thousands of NMR and MS spectra collected on pure, reference yeast metabolites. Each metabolite entry in the YMDB contains an average of 80 separate data fields including comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, intracellular/extracellular concentrations, growth conditions and substrates, pathway information, enzyme data, gene/protein sequence data, as well as numerous hyperlinks to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of S. cervesiae's importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers, but also to yeast biologists, systems biologists, the industrial fermentation industry, as well as the beer, wine and spirit industry.

ConoServer: updated content, knowledge, and discovery tools in the conopeptide database

ConoServer (http://www.conoserver.org) is a database specializing in the sequences and structures of conopeptides, which are toxins expressed by marine cone snails. Cone snails are carnivorous gastropods, which hunt their prey using a cocktail of toxins that potently subvert nervous system function. The ability of these toxins to specifically target receptors, channels and transporters of the nervous system has attracted considerable interest for their use in physiological research and as drug leads. Since the founding publication on ConoServer in 2008, the number of entries in the database has nearly doubled, the interface has been redesigned and new annotations have been added, including a more detailed description of cone snail species, biological activity measurements and information regarding the identification of each sequence. Automatically updated statistics on classification schemes, three-dimensional structures, conopeptide-bearing species and endoplasmic reticulum signal sequence conservation trends, provide a convenient overview of current knowledge on conopeptides. Transcriptomics and proteomics have began generating massive numbers of new conopeptide sequences, and two dedicated tools have been recently implemented in ConoServer to standardize the analysis of conopeptide precursor sequences and to help in the identification by mass spectrometry of toxins whose sequences were predicted at the nucleic acid level.

Extensive de novo solid-state NMR assignments of the 33 kDa C-terminal domain of the Ure2 prion

We present the de novo resonance assignments for the crystalline 33 kDa C-terminal domain of the Ure2 prion using an optimized set of five 3D solid-state NMR spectra. We obtained, using a single uniformly (13)C, (15)N labeled protein sample, sequential chemical-shift information for 74% of the N, Cα, Cβ triples, and for 80% of further side-chain resonances for these spin systems. We describe the procedures and protocols devised, and discuss possibilities and limitations of the assignment of this largest protein assigned today by solid-state NMR, and for which no solution-state NMR shifts were available. A comparison of the NMR chemical shifts with crystallographic data reveals that regions with high crystallographic B-factors are particularly difficult to assign. While the secondary structure elements derived from the chemical shift data correspond mainly to those present in the X-ray crystal structure, we detect an additional helical element and structural variability in the protein crystal, most probably originating from the different molecules in the asymmetric unit, with the observation of doubled resonances in several parts, including entire stretches, of the protein. Our results provide the point of departure towards an atomic-resolution structural analysis of the C-terminal Ure2p domain in the context of the full-length prion fibrils.

Interactions of protein side chains with RNA defined with REDOR solid state NMR

Formation of the complex between human immunodeficiency virus type-1 Tat protein and the transactivation response region (TAR) RNA is vital for transcriptional elongation, yet the structure of the Tat-TAR complex remains to be established. The NMR structures of free TAR, and TAR bound to Tat-derived peptides have been obtained by solution NMR, but only a small number of intermolecular NOEs could be identified unambiguously, preventing the determination of a complete structure. Here we show that a combination of multiple solid state NMR REDOR experiments can be used to obtain multiple distance constraints from (15)N to (13)C spins within the backbone and side chain guanidinium groups of arginine in a Tat-derived peptide, using (19)F spins incorporated into the base of U23 in TAR and (31)P spins in the P22 and P23 phosphate groups. Distances between the side chain of Arg52 and the base and phosphodiester backbone near U23 measured by REDOR NMR are comparable to distances observed in solution NMR-derived structural models, indicating that interactions of TAR RNA with key amino acid side chains in Tat are the same in the amorphous solid state as in solution. This method is generally applicable to other protein-RNA complexes where crystallization or solution NMR has failed to provide high resolution structural information.

Automated sequence- and stereo-specific assignment of methyl-labeled proteins by paramagnetic relaxation and methyl-methyl nuclear Overhauser enhancement spectroscopy

Methyl-transverse relaxation optimized spectroscopy is rapidly becoming the preferred NMR technique for probing structure and dynamics of very large proteins up to ~1 MDa in molecular size. Data interpretation, however, necessitates assignment of methyl groups which still presents a very challenging and time-consuming process. Here we demonstrate that, in combination with a known 3D structure, paramagnetic relaxation enhancement (PRE), induced by nitroxide spin-labels incorporated at only a few surface-exposed engineered cysteines, provides fast, straightforward and robust access to methyl group resonance assignments, including stereoassignments for the methyl groups of leucine and valine. Neither prior assignments, including backbone assignments, for the protein, nor experiments that transfer magnetization between methyl groups and the protein backbone, are required. PRE-derived assignments are refined by 4D methyl-methyl nuclear Overhauser enhancement data, eliminating ambiguities and errors that may arise due to the high sensitivity of PREs to the potential presence of sparsely-populated transient states.

Structure, dynamics, and ionization equilibria of the tyrosine residues in Bacillus circulans xylanase

We have developed NMR spectroscopic methods to investigate the tyrosines within Bacillus circulans xylanase (BcX). Four slowly exchanging buried tyrosine hydroxyl protons with chemical shifts between 7.5 and 12.5 ppm were found using a long-range (13)C-HSQC experiment that exploits the (3)J(CH) coupling between the ring (1)H(η) and (13)C(ε) nuclei. The NMR signals from these protons were assigned via (13)C-tyrosine selective labelling and a suite of scalar and (13)C,(15)N-filtered/edited NOE correlation spectra. Of the fifteen tyrosines in BcX, only the buried Tyr79 and Tyr105 showed four distinct, rather than two averaged, signals from ring (13)C-(1)H pairs, indicative of slow flipping on the chemical shift timescale. Ring flipping rate constants of ~10 and ~0.2 s(-1) were measured for the two residues, respectively, using a (13)C longitudinal exchange experiment. The hydrogen bonding properties of the Tyr79 and Tyr105 hydroxyls were also defined by complementary NOE and J-coupling measurements. The (1)H(η) hydrogen-deuterium exchange rate constants of the buried tyrosines were determined from (13)C/(15)N-filtered spectra recorded as a function of pH. These exchange rate constants correspond to estimated protection factors of ~10(4)-10(8) relative to a random coil tyrosine. The phenolic sidechain pK (a) values were also measured by monitoring their pH-dependent (13)C(ζ) chemical shifts via (1)H(ε/δ)((13)C(ε))(13)C(ζ) correlation spectra. Exposed tyrosines had unperturbed pK (a) values of ~10.2, whereas buried residues remained predominantly neutral at or even above pH 11. Combined with selective isotope labelling, these NMR experiments should prove useful for investigating the structural and electrostatic properties of tyrosines in many interesting proteins.

Ratio analysis nuclear magnetic resonance spectroscopy for selective metabolite identification in complex samples

Metabolite identification in the complex NMR spectra of biological samples is a challenging task due to significant spectral overlap and limited signal-to-noise. In this study we present a new approach, RANSY (ratio analysis NMR spectroscopy), which identifies all the peaks of a specific metabolite on the basis of the ratios of peak heights or integrals. We show that the spectrum for an individual metabolite can be generated by exploiting the fact that the peak ratios for any metabolite in the NMR spectrum are fixed and proportional to the relative numbers of magnetically distinct protons. When the peak ratios are divided by their coefficients of variation derived from a set of NMR spectra, the generation of an individual metabolite spectrum is enabled. We first tested the performance of this approach using one-dimensional (1D) and two-dimensional (2D) NMR data of mixtures of synthetic analogues of common body fluid metabolites. Subsequently, the method was applied to (1)H NMR spectra of blood serum samples to demonstrate the selective identification of a number of metabolites. The RANSY approach, which does not need any additional NMR experiments for spectral simplification, is easy to perform and has the potential to aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.

Protein Data Bank Japan (PDBj): maintaining a structural data archive and resource description framework format

The Protein Data Bank Japan (PDBj, http://pdbj.org) is a member of the worldwide Protein Data Bank (wwPDB) and accepts and processes the deposited data of experimentally determined macromolecular structures. While maintaining the archive in collaboration with other wwPDB partners, PDBj also provides a wide range of services and tools for analyzing structures and functions of proteins, which are summarized in this article. To enhance the interoperability of the PDB data, we have recently developed PDB/RDF, PDB data in the Resource Description Framework (RDF) format, along with its ontology in the Web Ontology Language (OWL) based on the PDB mmCIF Exchange Dictionary. Being in the standard format for the Semantic Web, the PDB/RDF data provide a means to integrate the PDB with other biological information resources.

Estimation of interdomain flexibility of N-terminus of factor H using residual dipolar couplings

Characterization of segmental flexibility is needed to understand the biological mechanisms of the very large category of functionally diverse proteins, exemplified by the regulators of complement activation, that consist of numerous compact modules or domains linked by short, potentially flexible, sequences of amino acid residues. The use of NMR-derived residual dipolar couplings (RDCs), in magnetically aligned media, to evaluate interdomain motion is established but only for two-domain proteins. We focused on the three N-terminal domains (called CCPs or SCRs) of the important complement regulator, human factor H (i.e., FH1-3). These domains cooperate to facilitate cleavage of the key complement activation-specific protein fragment, C3b, forming iC3b that no longer participates in the complement cascade. We refined a three-dimensional solution structure of recombinant FH1-3 based on nuclear Overhauser effects and RDCs. We then employed a rudimentary series of RDC data sets, collected in media containing magnetically aligned bicelles (disklike particles formed from phospholipids) under three different conditions, to estimate interdomain motions. This circumvents a requirement of previous approaches for technically difficult collection of five independent RDC data sets. More than 80% of conformers of this predominantly extended three-domain molecule exhibit flexions of <40°. Such segmental flexibility (together with the local dynamics of the hypervariable loop within domain 3) could facilitate recognition of C3b via initial anchoring and eventual reorganization of modules to the conformation captured in the previously solved crystal structure of a C3b:FH1-4 complex.

Sparse non-negative generalized PCA with applications to metabolomics

MOTIVATION

Nuclear magnetic resonance (NMR) spectroscopy has been used to study mixtures of metabolites in biological samples. This technology produces a spectrum for each sample depicting the chemical shifts at which an unknown number of latent metabolites resonate. The interpretation of this data with common multivariate exploratory methods such as principal components analysis (PCA) is limited due to high-dimensionality, non-negativity of the underlying spectra and dependencies at adjacent chemical shifts.

RESULTS

We develop a novel modification of PCA that is appropriate for analysis of NMR data, entitled Sparse Non-Negative Generalized PCA. This method yields interpretable principal components and loading vectors that select important features and directly account for both the non-negativity of the underlying spectra and dependencies at adjacent chemical shifts. Through the reanalysis of experimental NMR data on five purified neural cell types, we demonstrate the utility of our methods for dimension reduction, pattern recognition, sample exploration and feature selection. Our methods lead to the identification of novel metabolites that reflect the differences between these cell types.

AVAILABILITY

www.stat.rice.edu/~gallen/software.html.

CONTACT

gallen@rice.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analysis of binding site hot spots on the surface of Ras GTPase

We have recently discovered an allosteric switch in Ras, bringing an additional level of complexity to this GTPase whose mutants are involved in nearly 30% of cancers. Upon activation of the allosteric switch, there is a shift in helix 3/loop 7 associated with a disorder to order transition in the active site. Here, we use a combination of multiple solvent crystal structures and computational solvent mapping (FTMap) to determine binding site hot spots in the "off" and "on" allosteric states of the GTP-bound form of H-Ras. Thirteen sites are revealed, expanding possible target sites for ligand binding well beyond the active site. Comparison of FTMaps for the H and K isoforms reveals essentially identical hot spots. Furthermore, using NMR measurements of spin relaxation, we determined that K-Ras exhibits global conformational dynamics very similar to those we previously reported for H-Ras. We thus hypothesize that the global conformational rearrangement serves as a mechanism for allosteric coupling between the effector interface and remote hot spots in all Ras isoforms. At least with respect to the binding sites involving the G domain, H-Ras is an excellent model for K-Ras and probably N-Ras as well. Ras has so far been elusive as a target for drug design. The present work identifies various unexplored hot spots throughout the entire surface of Ras, extending the focus from the disordered active site to well-ordered locations that should be easier to target.

Two-dimensional 1H-13C nuclear magnetic resonance (NMR)-based comprehensive analysis of roasted coffee bean extract

Coffee was characterized by proton and carbon nuclear magnetic resonance (NMR) spectroscopy. To identify the coffee components, a detailed and approximately 90% signal assignment was carried out using various two-dimensional NMR spectra and a spiking method, in which authentic compounds were added to the roasted coffee bean extract (RCBE) sample. A total of 24 coffee components, including 5 polysaccharide units, 3 stereoisomers of chlorogenic acids, and 2 stereoisomers of quinic acids, were identified with the NMR spectra of RCBE. On the basis of the signal assignment, state analyses were further launched for the metal ion-citrate complexes and caffeine-chlorogenate complexes. On the basis of the signal integration, the coffee components were successfully quantified. This NMR methodology yielded detailed information on RCBE using only a single observation and provides a systemic approach for the analysis of other complex mixtures.

Deconvolution of chemical mixtures with high complexity by NMR consensus trace clustering

Identification and quantification of analytes in complex solution-state mixtures are critical procedures in many areas of chemistry, biology, and molecular medicine. Nuclear magnetic resonance (NMR) is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of the samples. We present three new multidimensional-NMR based approaches that are geared toward the analysis of mixtures with high complexity at natural (13)C abundance, including approaches that are encountered in metabolomics. Common to all three approaches is the concept of the extraction of one-dimensional (1D) consensus spectral traces or 2D consensus planes followed by clustering, which significantly improves the capability to identify mixture components that are affected by strong spectral overlap. The methods are demonstrated for covariance (1)H-(1)H TOCSY and (13)C-(1)H HSQC-TOCSY spectra and triple-rank correlation spectra constructed from pairs of (13)C-(1)H HSQC and (13)C-(1)H HSQC-TOCSY spectra. All methods are first demonstrated for an eight-compound metabolite model mixture before being applied to an extract from E. coli cell lysate.

Homogeneous and heterogeneous tertiary structure ensembles of amyloid-β peptides

The interplay of modern molecular simulation and high-quality nuclear magnetic resonance (NMR) experiments has reached a fruitful stage for quantitative characterization of structural ensembles of disordered peptides. Amyloid-β 1-42 (Aβ42), the primary peptide associated with Alzheimer's disease, and fragments such as Aβ21-30 are both classified as intrinsically disordered peptides (IDPs). We use a variety of NMR observables to validate de novo molecular dynamics simulations in explicit water to characterize the tertiary structure ensemble of Aβ42 and Aβ21-30 from the perspective of their classification as IDPs. Unlike the Aβ21-30 fragment that conforms to expectations of an IDP that is primarily extended, we find that Aβ42 samples conformations reflecting all possible secondary structure categories and spans the range of IDP classifications from collapsed structured states to highly extended conformations, making it an IDP with a far more heterogeneous tertiary ensemble.

Using the water signal to detect invisible exchanging protons in the catalytic triad of a serine protease

Chemical Exchange Saturation Transfer (CEST) is an MRI approach that can indirectly detect exchange broadened protons that are invisible in traditional NMR spectra. We modified the CEST pulse sequence for use on high-resolution spectrometers and developed a quantitative approach for measuring exchange rates based upon CEST spectra. This new methodology was applied to the rapidly exchanging Hδ1 and Hε2 protons of His57 in the catalytic triad of bovine chymotrypsinogen-A (bCT-A). CEST enabled observation of Hε2 at neutral pH values, and also allowed measurement of solvent exchange rates for His57-Hδ1 and His57-Hε2 across a wide pH range (3-10). Hδ1 exchange was only dependent upon the charge state of the His57 (k (ex,Im+) = 470 s(-1), k (ex,Im) = 50 s(-1)), while Hε2 exchange was found to be catalyzed by hydroxide ion and phosphate base (k(OH)⁻ = 1.7 × 10(10) M(-1) s(-1), K(HPO)²⁻₄ = 1.7 × 10(6) M(-1) s(-1)), reflecting its greater exposure to solute catalysts. Concomitant with the disappearance of the Hε2 signal as the pH was increased above its pK (a), was the appearance of a novel signal (δ = 12 ppm), which we assigned to Hγ of the nearby Ser195 nucleophile, that is hydrogen bonded to Nε2 of neutral His57. The chemical shift of Hγ is about 7 ppm downfield from a typical hydroxyl proton, suggesting a highly polarized O-Hγ bond. The significant alkoxide character of Oγ indicates that Ser195 is preactivated for nucleophilic attack before substrate binding. CEST should be generally useful for mechanistic investigations of many enzymes with labile protons involved in active site chemistry.

Backbone resonance assignment and order tensor estimation using residual dipolar couplings

An NMR investigation of proteins with known X-ray structures is of interest in a number of endeavors. Performing these studies through nuclear magnetic resonance (NMR) requires the costly step of resonance assignment. The prevalent assignment strategy does not make use of existing structural information and requires uniform isotope labeling. Here we present a rapid and cost-effective method of assigning NMR data to an existing structure-either an X-ray or computationally modeled structure. The presented method, Exhaustively Permuted Assignment of RDCs (EPAR), utilizes unassigned residual dipolar coupling (RDC) data that can easily be obtained by NMR spectroscopy. The algorithm uses only the backbone N-H RDCs from multiple alignment media along with the amino acid type of the RDCs. It is inspired by previous work from Zweckstetter and provides several extensions. We present results on 13 synthetic and experimental datasets from 8 different structures, including two homodimers. Using just two alignment media, EPAR achieves an average assignment accuracy greater than 80%. With three media, the average accuracy is higher than 94%. The algorithm also outputs a prediction of the assignment accuracy, which has a correlation of 0.77 to the true accuracy. This prediction score can be used to establish the needed confidence in assignment accuracy.

Protein side-chain resonance assignment and NOE assignment using RDC-defined backbones without TOCSY data

One bottleneck in NMR structure determination lies in the laborious and time-consuming process of side-chain resonance and NOE assignments. Compared to the well-studied backbone resonance assignment problem, automated side-chain resonance and NOE assignments are relatively less explored. Most NOE assignment algorithms require nearly complete side-chain resonance assignments from a series of through-bond experiments such as HCCH-TOCSY or HCCCONH. Unfortunately, these TOCSY experiments perform poorly on large proteins. To overcome this deficiency, we present a novel algorithm, called NASCA: (NOE Assignment and Side-Chain Assignment), to automate both side-chain resonance and NOE assignments and to perform high-resolution protein structure determination in the absence of any explicit through-bond experiment to facilitate side-chain resonance assignment, such as HCCH-TOCSY. After casting the assignment problem into a Markov Random Field (MRF), NASCA: extends and applies combinatorial protein design algorithms to compute optimal assignments that best interpret the NMR data. The MRF captures the contact map information of the protein derived from NOESY spectra, exploits the backbone structural information determined by RDCs, and considers all possible side-chain rotamers. The complexity of the combinatorial search is reduced by using a dead-end elimination (DEE) algorithm, which prunes side-chain resonance assignments that are provably not part of the optimal solution. Then an A* search algorithm is employed to find a set of optimal side-chain resonance assignments that best fit the NMR data. These side-chain resonance assignments are then used to resolve the NOE assignment ambiguity and compute high-resolution protein structures. Tests on five proteins show that NASCA: assigns resonances for more than 90% of side-chain protons, and achieves about 80% correct assignments. The final structures computed using the NOE distance restraints assigned by NASCA: have backbone RMSD 0.8-1.5 Å from the reference structures determined by traditional NMR approaches.

Quantifying millisecond time-scale exchange in proteins by CPMG relaxation dispersion NMR spectroscopy of side-chain carbonyl groups

A new pulse sequence is presented for the measurement of relaxation dispersion profiles quantifying millisecond time-scale exchange dynamics of side-chain carbonyl groups in uniformly (13)C labeled proteins. The methodology has been tested using the 87-residue colicin E7 immunity protein, Im7, which is known to fold via a partially structured low populated intermediate that interconverts with the folded, ground state on the millisecond time-scale. Comparison of exchange parameters extracted for this folding 'reaction' using the present methodology with those obtained from more 'traditional' (15)N and backbone carbonyl probes establishes the utility of the approach. The extracted excited state side-chain carbonyl chemical shifts indicate that the Asx/Glx side-chains are predominantly unstructured in the Im7 folding intermediate. However, several crucial salt-bridges that exist in the native structure appear to be already formed in the excited state, either in part or in full. This information, in concert with that obtained from existing backbone and side-chain methyl relaxation dispersion experiments, will ultimately facilitate a detailed description of the structure of the Im7 folding intermediate.

Structure-based prediction of methyl chemical shifts in proteins

Protein methyl groups have recently been the subject of much attention in NMR spectroscopy because of the opportunities that they provide to obtain information about the structure and dynamics of proteins and protein complexes. With the advent of selective labeling schemes, methyl groups are particularly interesting in the context of chemical shift based protein structure determination, an approach that to date has exploited primarily the mapping between protein structures and backbone chemical shifts. In order to extend the scope of chemical shifts for structure determination, we present here the CH3Shift method of performing structure-based predictions of methyl chemical shifts. The terms considered in the predictions take account of ring current, magnetic anisotropy, electric field, rotameric type, and dihedral angle effects, which are considered in conjunction with polynomial functions of interatomic distances. We show that the CH3Shift method achieves an accuracy in the predictions that ranges from 0.133 to 0.198 ppm for (1)H chemical shifts for Ala, Thr, Val, Leu and Ile methyl groups. We illustrate the use of the method by assessing the accuracy of side-chain structures in structural ensembles representing the dynamics of proteins.

A Grid-enabled web portal for NMR structure refinement with AMBER

MOTIVATION

The typical workflow for NMR structure determination involves collecting thousands of conformational restraints, calculating a bundle of 20-40 conformers in agreement with them and refining the energetics of these conformers. The structure calculation step employs simulated annealing based on molecular dynamics (MD) simulations with very simplified force fields. The value of refining the calculated conformers using restrained MD (rMD) simulations with state-of-art force fields is documented. This refinement however presents various subtleties, from the proper formatting of conformational restraints to the definition of suitable protocols.

RESULTS

We describe a web interface to set up and run calculations with the AMBER package, which we called AMPS-NMR (AMBER-based Portal Server for NMR structures). The interface allows the refinement of NMR structures through rMD. Some predefined protocols are provided for this purpose, which can be personalized; it is also possible to create an entirely new protocol. AMPS-NMR can handle various restraint types. Standard rMD refinement in explicit water of the structures of three different proteins are shown as examples. AMPS-NMR additionally includes a workspace for the user to store different calculations. As an ancillary service, a web interface to AnteChamber is available, enabling the calculation of force field parameters for organic molecules such as ligands in protein-ligand adducts.

AVAILABILITY AND IMPLEMENTATION

AMPS-NMR is embedded within the NMR services of the WeNMR project and is available at http://py-enmr.cerm.unifi.it/access/index/amps-nmr; its use requires registration with a digital certificate.

CONTACT

ivanobertini@cerm.unifi.it

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural characterization of polyglutamine fibrils by solid-state NMR spectroscopy

Protein aggregation via polyglutamine stretches occurs in a number of severe neurodegenerative diseases such as Huntington's disease. We have investigated fibrillar aggregates of polyglutamine peptides below, at, and above the toxicity limit of around 37 glutamine residues using solid-state NMR and electron microscopy. Experimental data are consistent with a dry fibril core of at least 70-80 Å in width for all constructs. Solid-state NMR dipolar correlation experiments reveal a largely β-strand character of all samples and point to tight interdigitation of hydrogen-bonded glutamine side chains from different sheets. Two approximately equally frequent populations of glutamine residues with distinct sets of chemical shifts are found, consistent with local backbone dihedral angles compensating for β-strand twist or with two distinct sets of side-chain conformations. Peptides comprising 15 glutamine residues are present as single extended β-strands. Data obtained for longer constructs are most compatible with a superpleated arrangement with individual molecules contributing β-strands to more than one sheet and an antiparallel assembly of strands within β-sheets.

NMR analysis of a stress response metabolic signaling network

We previously hypothesized that Staphylococcus epidermidis senses a diverse set of environmental and nutritional factors associated with biofilm formation through a modulation in the activity of the tricarboxylic acid (TCA) cycle. Herein, we report our further investigation of the impact of additional environmental stress factors on TCA cycle activity and provide a detailed description of our NMR methodology. S. epidermidis wild-type strain 1457 was treated with stressors that are associated with biofilm formation, a sublethal dose of tetracycline, 5% NaCl, 2% glucose, and autoinducer-2 (AI-2). As controls and to integrate our current data with our previous study, 4% ethanol stress and iron-limitation were also used. Consistent with our prior observations, the effect of many environmental stress factors on the S. epidermidis metabolome was essentially identical to the effect of TCA cycle inactivation in the aconitase mutant strain 1457-acnA::tetM. A detailed quantitative analysis of metabolite concentration changes using 2D (1)H-(13)C HSQC and (1)H-(1)H TOCSY spectra identified a network of 37 metabolites uniformly affected by the stressors and TCA cycle inactivation. We postulate that the TCA cycle acts as the central pathway in a metabolic signaling network.

Architecture of the high mobility group nucleosomal protein 2-nucleosome complex as revealed by methyl-based NMR

Chromatin structure and function are regulated by numerous proteins through specific binding to nucleosomes. The structural basis of many of these interactions is unknown, as in the case of the high mobility group nucleosomal (HMGN) protein family that regulates various chromatin functions, including transcription. Here, we report the architecture of the HMGN2-nucleosome complex determined by a combination of methyl-transverse relaxation optimized nuclear magnetic resonance spectroscopy (methyl-TROSY) and mutational analysis. We found that HMGN2 binds to both the acidic patch in the H2A-H2B dimer and to nucleosomal DNA near the entry/exit point, "stapling" the histone core and the DNA. These results provide insight into how HMGNs regulate chromatin structure through interfering with the binding of linker histone H1 to the nucleosome as well as a structural basis of how phosphorylation induces dissociation of HMGNs from chromatin during mitosis. Importantly, our approach is generally applicable to the study of nucleosome-binding interactions in chromatin.