BioMagResBank

Reliability of exclusively NOESY-based automated resonance assignment and structure determination of proteins

Protein structure determination by NMR can in principle be speeded up both by reducing the measurement time on the NMR spectrometer and by a more efficient analysis of the spectra. Here we study the reliability of protein structure determination based on a single type of spectra, namely nuclear Overhauser effect spectroscopy (NOESY), using a fully automated procedure for the sequence-specific resonance assignment with the recently introduced FLYA algorithm, followed by combined automated NOE distance restraint assignment and structure calculation with CYANA. This NOESY-FLYA method was applied to eight proteins with 63-160 residues for which resonance assignments and solution structures had previously been determined by the Northeast Structural Genomics Consortium (NESG), and unrefined and refined NOESY data sets have been made available for the Critical Assessment of Automated Structure Determination of Proteins by NMR project. Using only peak lists from three-dimensional (13)C- or (15)N-resolved NOESY spectra as input, the FLYA algorithm yielded for the eight proteins 91-98 % correct backbone and side-chain assignments if manually refined peak lists are used, and 64-96 % correct assignments based on raw peak lists. Subsequent structure calculations with CYANA then produced structures with root-mean-square deviation (RMSD) values to the manually determined reference structures of 0.8-2.0 Å if refined peak lists are used. With raw peak lists, calculations for 4 proteins converged resulting in RMSDs to the reference structure of 0.8-2.8 Å, whereas no convergence was obtained for the four other proteins (two of which did already not converge with the correct manual resonance assignments given as input). These results show that, given high-quality experimental NOESY peak lists, the chemical shift assignments can be uncovered, without any recourse to traditional through-bond type assignment experiments, to an extent that is sufficient for calculating accurate three-dimensional structures.

Protein folding on the ribosome studied using NMR spectroscopy

NMR spectroscopy is a powerful tool for the investigation of protein folding and misfolding, providing a characterization of molecular structure, dynamics and exchange processes, across a very wide range of timescales and with near atomic resolution. In recent years NMR methods have also been developed to study protein folding as it might occur within the cell, in a de novo manner, by observing the folding of nascent polypeptides in the process of emerging from the ribosome during synthesis. Despite the 2.3 MDa molecular weight of the bacterial 70S ribosome, many nascent polypeptides, and some ribosomal proteins, have sufficient local flexibility that sharp resonances may be observed in solution-state NMR spectra. In providing information on dynamic regions of the structure, NMR spectroscopy is therefore highly complementary to alternative methods such as X-ray crystallography and cryo-electron microscopy, which have successfully characterized the rigid core of the ribosome particle. However, the low working concentrations and limited sample stability associated with ribosome-nascent chain complexes means that such studies still present significant technical challenges to the NMR spectroscopist. This review will discuss the progress that has been made in this area, surveying all NMR studies that have been published to date, and with a particular focus on strategies for improving experimental sensitivity.

Molecular structure of RADA16-I designer self-assembling peptide nanofibers

The designer self-assembling peptide RADA16-I forms nanofiber matrices which have shown great promise for regenerative medicine and three-dimensional cell culture. The RADA16-I amino acid sequence has a β-strand-promoting alternating hydrophobic/charged motif, but arrangement of β-strands into the nanofiber structure has not been previously determined. Here we present a structural model of RADA16-I nanofibers, based on solid-state NMR measurements on samples with different schemes for (13)C isotopic labeling. NMR peak positions and line widths indicate an ordered structure composed of β-strands. The NMR data show that the nanofibers are composed of two stacked β-sheets stabilized by a hydrophobic core formed by alanine side chains, consistent with previous proposals. However, the previously proposed antiparallel β-sheet structure is ruled out by measured (13)C-(13)C dipolar couplings. Instead, neighboring β-strands within β-sheets are parallel, with a registry shift that allows cross-strand staggering of oppositely charged arginine and aspartate side chains. The resulting structural model is compared to nanofiber dimensions observed via images taken by transmission electron microscopy and atomic force microscopy. Multiple NMR peaks for each alanine side chain were observed and could be attributed to multiple configurations of side chain packing within a single scheme for intermolecular packing.

Proton fingerprints portray molecular structures: enhanced description of the 1H NMR spectra of small molecules

The characteristic signals observed in NMR spectra encode essential information on the structure of small molecules. However, extracting all of this information from complex signal patterns is not trivial. This report demonstrates how computer-aided spectral analysis enables the complete interpretation of 1D (1)H NMR data. The effectiveness of this approach is illustrated with a set of organic molecules, for which replicas of their (1)H NMR spectra were generated. The potential impact of this methodology on organic chemistry research is discussed.

Peptide lipidation stabilizes structure to enhance biological function

Medicines that decrease body weight and restore nutrient tolerance could improve human diabetes and obesity treatment outcomes. We developed lipid-acylated glucagon analogs that are co-agonists for the glucagon and glucagon-like peptide 1 receptors, and stimulate weight loss and plasma glucose lowering in pre-diabetic obese mice. Our studies identified lipid acylation (lipidation) can increase and balance in vitro potencies of select glucagon analogs for the two aforementioned receptors in a lipidation site-dependent manner. A general capacity for lipidation to enhance the secondary structure of glucagon analogs was recognized, and the energetics of this effect quantified. The molecular structure of a lipid-acylated glucagon analog in water was also characterized. These results support that lipidation can modify biological activity through thermodynamically-favorable intramolecular interactions which stabilize structure. This establishes use of lipidation to achieve specific pharmacology and implicates similar endogenous post-translational modifications as physiological tools capable of refining biological action in means previously underappreciated.

Recommendations of the wwPDB NMR Validation Task Force

As methods for analysis of biomolecular structure and dynamics using nuclear magnetic resonance spectroscopy (NMR) continue to advance, the resulting 3D structures, chemical shifts, and other NMR data are broadly impacting biology, chemistry, and medicine. Structure model assessment is a critical area of NMR methods development, and is an essential component of the process of making these structures accessible and useful to the wider scientific community. For these reasons, the Worldwide Protein Data Bank (wwPDB) has convened an NMR Validation Task Force (NMR-VTF) to work with wwPDB partners in developing metrics and policies for biomolecular NMR data harvesting, structure representation, and structure quality assessment. This paper summarizes the recommendations of the NMR-VTF, and lays the groundwork for future work in developing standards and metrics for biomolecular NMR structure quality assessment.

Resonance assignment for a particularly challenging protein based on systematic unlabeling of amino acids to complement incomplete NMR data sets

NMR-based structure determination of a protein requires the assignment of resonances as indispensable first step. Even though heteronuclear through-bond correlation methods are available for that purpose, challenging situations arise in cases where the protein in question only yields samples of limited concentration and/or stability. Here we present a strategy based upon specific individual unlabeling of all 20 standard amino acids to complement standard NMR experiments and to achieve unambiguous backbone assignments for the fast precipitating 23 kDa catalytic domain of human aprataxin of which only incomplete standard NMR data sets could be obtained. Together with the validation of this approach utilizing the protein GB1 as a model, a comprehensive insight into metabolic interconversion ("scrambling") of NH and CO groups in a standard Escherichia coli expression host is provided.

Protein conformational exchange measured by 1H R1ρ relaxation dispersion of methyl groups

Activated dynamics plays a central role in protein function, where transitions between distinct conformations often underlie the switching between active and inactive states. The characteristic time scales of these transitions typically fall in the microsecond to millisecond range, which is amenable to investigations by NMR relaxation dispersion experiments. Processes at the faster end of this range are more challenging to study, because higher RF field strengths are required to achieve refocusing of the exchanging magnetization. Here we describe a rotating-frame relaxation dispersion experiment for (1)H spins in methyl (13)CHD2 groups, which improves the characterization of fast exchange processes. The influence of (1)H-(1)H rotating-frame nuclear Overhauser effects (ROE) is shown to be negligible, based on a comparison of R 1ρ relaxation data acquired with tilt angles of 90° and 35°, in which the ROE is maximal and minimal, respectively, and on samples containing different (1)H densities surrounding the monitored methyl groups. The method was applied to ubiquitin and the apo form of calmodulin. We find that ubiquitin does not exhibit any (1)H relaxation dispersion of its methyl groups at 10 or 25 °C. By contrast, calmodulin shows significant conformational exchange of the methionine methyl groups in its C-terminal domain, as previously demonstrated by (1)H and (13)C CPMG experiments. The present R 1ρ experiment extends the relaxation dispersion profile towards higher refocusing frequencies, which improves the definition of the exchange correlation time, compared to previous results.

Protein structure validation and identification from unassigned residual dipolar coupling data using 2D-PDPA

More than 90% of protein structures submitted to the PDB each year are homologous to some previously characterized protein structure. The extensive resources that are required for structural characterization of proteins can be justified for the 10% of the novel structures, but not for the remaining 90%. This report presents the 2D-PDPA method, which utilizes unassigned residual dipolar coupling in order to address the economics of structure determination of routine proteins by reducing the data acquisition and processing time. 2D-PDPA has been demonstrated to successfully identify the correct structure of an array of proteins that range from 46 to 445 residues in size from a library of 619 decoy structures by using unassigned simulated RDC data. When using experimental data, 2D-PDPA successfully identified the correct NMR structures from the same library of decoy structures. In addition, the most homologous X-ray structure was also identified as the second best structural candidate. Finally, success of 2D-PDPA in identifying and evaluating the most appropriate structure from a set of computationally predicted structures in the case of a previously uncharacterized protein Pf2048.1 has been demonstrated. This protein exhibits less than 20% sequence identity to any protein with known structure and therefore presents a compelling and practical application of our proposed work.

Automated and assisted RNA resonance assignment using NMR chemical shift statistics

The three-dimensional structure determination of RNAs by NMR spectroscopy relies on chemical shift assignment, which still constitutes a bottleneck. In order to develop more efficient assignment strategies, we analysed relationships between sequence and ¹H and ¹³C chemical shifts. Statistics of resonances from regularly Watson–Crick base-paired RNA revealed highly characteristic chemical shift clusters. We developed two approaches using these statistics for chemical shift assignment of double-stranded RNA (dsRNA): a manual approach that yields starting points for resonance assignment and simplifies decision trees and an automated approach based on the recently introduced automated resonance assignment algorithm FLYA. Both strategies require only unlabeled RNAs and three 2D spectra for assigning the H2/C2, H5/C5, H6/C6, H8/C8 and H1′/C1′ chemical shifts. The manual approach proved to be efficient and robust when applied to the experimental data of RNAs with a size between 20 nt and 42 nt. The more advanced automated assignment approach was successfully applied to four stem-loop RNAs and a 42 nt siRNA, assigning 92–100% of the resonances from dsRNA regions correctly. This is the first automated approach for chemical shift assignment of non-exchangeable protons of RNA and their corresponding ¹³C resonances, which provides an important step toward automated structure determination of RNAs.

NMR solution structure of a Chymotrypsin inhibitor from the Taiwan cobra Naja naja atra

The Taiwan cobra (Naja naja atra) chymotrypsin inhibitor (NACI) consists of 57 amino acids and is related to other Kunitz-type inhibitors such as bovine pancreatic trypsin inhibitor (BPTI) and Bungarus fasciatus fraction IX (BF9), another chymotrypsin inhibitor. Here we present the solution structure of NACI. We determined the NMR structure of NACI with a root-mean-square deviation of 0.37 Å for the backbone atoms and 0.73 Å for the heavy atoms on the basis of 1,075 upper distance limits derived from NOE peaks measured in its NOESY spectra. To investigate the structural characteristics of NACI, we compared the three-dimensional structure of NACI with BPTI and BF9. The structure of the NACI protein comprises one 310-helix, one α-helix and one double-stranded antiparallel β-sheet, which is comparable with the secondary structures in BPTI and BF9. The RMSD value between the mean structures is 1.09 Å between NACI and BPTI and 1.27 Å between NACI and BF9. In addition to similar secondary and tertiary structure, NACI might possess similar types of protein conformational fluctuations as reported in BPTI, such as Cys14-Cys38 disulfide bond isomerization, based on line broadening of resonances from residues which are mainly confined to a region around the Cys14-Cys38 disulfide bond.

Effects of NMR spectral resolution on protein structure calculation

Adequate digital resolution and signal sensitivity are two critical factors for protein structure determinations by solution NMR spectroscopy. The prime objective for obtaining high digital resolution is to resolve peak overlap, especially in NOESY spectra with thousands of signals where the signal analysis needs to be performed on a large scale. Achieving maximum digital resolution is usually limited by the practically available measurement time. We developed a method utilizing non-uniform sampling for balancing digital resolution and signal sensitivity, and performed a large-scale analysis of the effect of the digital resolution on the accuracy of the resulting protein structures. Structure calculations were performed as a function of digital resolution for about 400 proteins with molecular sizes ranging between 5 and 33 kDa. The structural accuracy was assessed by atomic coordinate RMSD values from the reference structures of the proteins. In addition, we monitored also the number of assigned NOESY cross peaks, the average signal sensitivity, and the chemical shift spectral overlap. We show that high resolution is equally important for proteins of every molecular size. The chemical shift spectral overlap depends strongly on the corresponding spectral digital resolution. Thus, knowing the extent of overlap can be a predictor of the resulting structural accuracy. Our results show that for every molecular size a minimal digital resolution, corresponding to the natural linewidth, needs to be achieved for obtaining the highest accuracy possible for the given protein size using state-of-the-art automated NOESY assignment and structure calculation methods.

Solid-state NMR evidence for β-hairpin structure within MAX8 designer peptide nanofibers

MAX8, a designer peptide known to undergo self-assembly following changes in temperature, pH, and ionic strength, has demonstrated usefulness for tissue engineering and drug delivery. It is hypothesized that the self-assembled MAX8 nanofiber structure consists of closed β-hairpins aligned into antiparallel β-sheets. Here, we report evidence from solid-state NMR spectroscopy that supports the presence of the hypothesized β-hairpin conformation within the nanofiber structure. Specifically, our (13)C-(13)C two-dimensional exchange data indicate spatial proximity between V3 and K17, and (13)C-(13)C dipolar coupling measurements reveal proximity between the V3 and V18 backbone carbonyls. Moreover, isotopic dilution of labeled MAX8 nanofibers did not result in a loss of the (13)C-(13)C dipolar couplings, showing that these couplings are primarily intramolecular. NMR spectra also indicate the existence of a minor conformation, which is discussed in terms of previously hypothesized nanofiber physical cross-linking and possible nanofiber polymorphism.

Automated solid-state NMR resonance assignment of protein microcrystals and amyloids

Solid-state NMR is an emerging structure determination technique for crystalline and non-crystalline protein assemblies, e.g., amyloids. Resonance assignment constitutes the first and often very time-consuming step to a structure. We present ssFLYA, a generally applicable algorithm for automatic assignment of protein solid-state NMR spectra. Application to microcrystals of ubiquitin and the Ure2 prion C-terminal domain, as well as amyloids of HET-s(218-289) and α-synuclein yielded 88-97 % correctness for the backbone and side-chain assignments that are classified as self-consistent by the algorithm, and 77-90 % correctness if also assignments classified as tentative by the algorithm are included.

Site-specific NMR mapping and time-resolved monitoring of serine and threonine phosphorylation in reconstituted kinase reactions and mammalian cell extracts

We outline NMR protocols for site-specific mapping and time-resolved monitoring of protein phosphorylation reactions using purified kinases and mammalian cell extracts. These approaches are particularly amenable to intrinsically disordered proteins and unfolded, regulatory protein domains. We present examples for the ¹⁵N isotope-labeled N-terminal transactivation domain of human p53, which is either sequentially reacted with recombinant enzymes or directly added to mammalian cell extracts and phosphorylated by endogenous kinases. Phosphorylation reactions with purified enzymes are set up in minutes, whereas NMR samples in cell extracts are prepared within 1 h. Time-resolved NMR measurements are performed over minutes to hours depending on the activities of the probed kinases. Phosphorylation is quantitatively monitored with consecutive 2D ¹H-¹⁵N band-selective optimized-flip-angle short-transient (SOFAST)-heteronuclear multiple-quantum (HMQC) NMR experiments, which provide atomic-resolution insights into the phosphorylation levels of individual substrate residues and time-dependent changes thereof, thereby offering unique advantages over western blotting and mass spectrometry.

Buried and accessible surface area control intrinsic protein flexibility

Proteins experience a wide variety of conformational dynamics that can be crucial for facilitating their diverse functions. How is the intrinsic flexibility required for these motions encoded in their three-dimensional structures? Here, the overall flexibility of a protein is demonstrated to be tightly coupled to the total amount of surface area buried within its fold. A simple proxy for this, the relative solvent-accessible surface area (Arel), therefore shows excellent agreement with independent measures of global protein flexibility derived from various experimental and computational methods. Application of Arel on a large scale demonstrates its utility by revealing unique sequence and structural properties associated with intrinsic flexibility. In particular, flexibility as measured by Arel shows little correspondence with intrinsic disorder, but instead tends to be associated with multiple domains and increased α-helical structure. Furthermore, the apparent flexibility of monomeric proteins is found to be useful for identifying quaternary-structure errors in published crystal structures. There is also a strong tendency for the crystal structures of more flexible proteins to be solved to lower resolutions. Finally, local solvent accessibility is shown to be a primary determinant of local residue flexibility. Overall, this work provides both fundamental mechanistic insight into the origin of protein flexibility and a simple, practical method for predicting flexibility from protein structures.

Correlation between local structural dynamics of proteins inferred from NMR ensembles and evolutionary dynamics of homologues of known structure

Conformational changes in proteins are extremely important for their biochemical functions. Correlation between inherent conformational variations in a protein and conformational differences in its homologues of known structure is still unclear. In this study, we have used a structural alphabet called Protein Blocks (PBs). PBs are used to perform abstraction of protein 3-D structures into a 1-D strings of 16 alphabets (a-p) based on dihedral angles of overlapping pentapeptides. We have analyzed the variations in local conformations in terms of PBs represented in the ensembles of 801 protein structures determined using NMR spectroscopy. In the analysis of concatenated data over all the residues in all the NMR ensembles, we observe that the overall nature of inherent local structural variations in NMR ensembles is similar to the nature of local structural differences in homologous proteins with a high correlation coefficient of .94. High correlation at the alignment positions corresponding to helical and β-sheet regions is only expected. However, the correlation coefficient by considering only the loop regions is also quite high (.91). Surprisingly, segregated position-wise analysis shows that this high correlation does not hold true to loop regions at the structurally equivalent positions in NMR ensembles and their homologues of known structure. This suggests that the general nature of local structural changes is unique; however most of the local structural variations in loop regions of NMR ensembles do not correlate to their local structural differences at structurally equivalent positions in homologues.

Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information

We introduce a Python-based program that utilizes the large database of (13)C and (15)N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D (13)C-(13)C, (15)N-(13)C, or 3D (15)N-(13)C-(13)C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D (13)C-(13)C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the Cα and Cβ chemical shifts, the highest-ranked PLUQ assignments were 40-60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO-Cα-Cβ or N-Cα-Cβ), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.

Prediction of peak overlap in NMR spectra

Peak overlap is one of the major factors complicating the analysis of biomolecular NMR spectra. We present a general method for predicting the extent of peak overlap in multidimensional NMR spectra and its validation using both, experimental data sets and Monte Carlo simulation. The method is based on knowledge of the magnetization transfer pathways of the NMR experiments and chemical shift statistics from the Biological Magnetic Resonance Data Bank. Assuming a normal distribution with characteristic mean value and standard deviation for the chemical shift of each observable atom, an analytic expression was derived for the expected overlap probability of the cross peaks. The analytical approach was verified to agree with the average peak overlap in a large number of individual peak lists simulated using the same chemical shift statistics. The method was applied to eight proteins, including an intrinsically disordered one, for which the prediction results could be compared with the actual overlap based on the experimentally measured chemical shifts. The extent of overlap predicted using only statistical chemical shift information was in good agreement with the overlap that was observed when the measured shifts were used in the virtual spectrum, except for the intrinsically disordered protein. Since the spectral complexity of a protein NMR spectrum is a crucial factor for protein structure determination, analytical overlap prediction can be used to identify potentially difficult proteins before conducting NMR experiments. Overlap predictions can be tailored to particular classes of proteins by preparing statistics from corresponding protein databases. The method is also suitable for optimizing recording parameters and labeling schemes for NMR experiments and improving the reliability of automated spectra analysis and protein structure determination.

High-resolution quantitative metabolome analysis of urine by automated flow injection NMR

Metabolism is essential to understand human health. To characterize human metabolism, a high-resolution read-out of the metabolic status under various physiological conditions, either in health or disease, is needed. Metabolomics offers an unprecedented approach for generating system-specific biochemical definitions of a human phenotype through the capture of a variety of metabolites in a single measurement. The emergence of large cohorts in clinical studies increases the demand of technologies able to analyze a large number of measurements, in an automated fashion, in the most robust way. NMR is an established metabolomics tool for obtaining metabolic phenotypes. Here, we describe the analysis of NMR-based urinary profiles for metabolic studies, challenged to a large human study (3007 samples). This method includes the acquisition of nuclear Overhauser effect spectroscopy one-dimensional and J-resolved two-dimensional (J-Res-2D) ¹H NMR spectra obtained on a 600 MHz spectrometer, equipped with a 120 μL flow probe, coupled to a flow-injection analysis system, in full automation under the control of a sampler manager. Samples were acquired at a throughput of ∼20 (or 40 when J-Res-2D is included) min/sample. The associated technical analysis error over the full series of analysis is 12%, which demonstrates the robustness of the method. With the aim to describe an overall metabolomics workflow, the quantification of 36 metabolites, mainly related to central carbon metabolism and gut microbial host cometabolism, was obtained, as well as multivariate data analysis of the full spectral profiles. The metabolic read-outs generated using our analytical workflow can therefore be considered for further pathway modeling and/or biological interpretation.

Statistical spectroscopic tools for biomarker discovery and systems medicine

Metabolic profiling based on comparative, statistical analysis of NMR spectroscopic and mass spectrometric data from complex biological samples has contributed to increased understanding of the role of small molecules in affecting and indicating biological processes. To enable this research, the development of statistical spectroscopy has been marked by early beginnings in applying pattern recognition to nuclear magnetic resonance data and the introduction of statistical total correlation spectroscopy (STOCSY) as a tool for biomarker identification in the past decade. Extensions of statistical spectroscopy now compose a family of related tools used for compound identification, data preprocessing, and metabolic pathway analysis. In this Perspective, we review the theory and current state of research in statistical spectroscopy and discuss the growing applications of these tools to medicine and systems biology. We also provide perspectives on how recent institutional initiatives are providing new platforms for the development and application of statistical spectroscopy tools and driving the development of integrated "systems medicine" approaches in which clinical decision making is supported by statistical and computational analysis of metabolic, phenotypic, and physiological data.

Modeling proteins using a super-secondary structure library and NMR chemical shift information

A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.

Combining NMR and small angle X-ray and neutron scattering in the structural analysis of a ternary protein-RNA complex

Many processes in the regulation of gene expression and signaling involve the formation of protein complexes involving multi-domain proteins. Individual domains that mediate protein-protein and protein-nucleic acid interactions are typically connected by flexible linkers, which contribute to conformational dynamics and enable the formation of complexes with distinct binding partners. Solution techniques are therefore required for structural analysis and to characterize potential conformational dynamics. Nuclear magnetic resonance spectroscopy (NMR) provides such information but often only sparse data are obtained with increasing molecular weight of the complexes. It is therefore beneficial to combine NMR data with additional structural restraints from complementary solution techniques. Small angle X-ray/neutron scattering (SAXS/SANS) data can be efficiently combined with NMR-derived information, either for validation or by providing additional restraints for structural analysis. Here, we show that the combination of SAXS and SANS data can help to refine structural models obtained from data-driven docking using HADDOCK based on sparse NMR data. The approach is demonstrated with the ternary protein-protein-RNA complex involving two RNA recognition motif (RRM) domains of Sex-lethal, the N-terminal cold shock domain of Upstream-to-N-Ras, and msl-2 mRNA. Based on chemical shift perturbations we have mapped protein-protein and protein-RNA interfaces and complemented this NMR-derived information with SAXS data, as well as SANS measurements on subunit-selectively deuterated samples of the ternary complex. Our results show that, while the use of SAXS data is beneficial, the additional combination with contrast variation in SANS data resolves remaining ambiguities and improves the docking based on chemical shift perturbations of the ternary protein-RNA complex.

The role of structural bioinformatics resources in the era of integrative structural biology

The history and the current state of the PDB and EMDB archives is briefly described, as well as some of the challenges that they face. It seems natural that the role of structural biology archives will change from being a pure repository of historic data into becoming an indispensable resource for the wider biomedical community. As part of this transformation, it will be necessary to validate the biomacromolecular structure data and ensure the highest possible quality for the archive holdings, to combine structural data from different spatial scales into a unified resource and to integrate structural data with functional, genetic and taxonomic data as well as other information available in bioinformatics resources. Some recent developments and plans to address these challenges at PDBe are presented.

Insights into the photoprotective switch of the major light-harvesting complex II (LHCII): a preserved core of arginine-glutamate interlocked helices complemented by adjustable loops

Light-harvesting antennae of the LHC family form transmembrane three-helix bundles of which two helices are interlocked by conserved arginine-glutamate (Arg-Glu) ion pairs that form ligation sites for chlorophylls. The antenna proteins of photosystem II have an intriguing dual function. In excess light, they can switch their conformation from a light-harvesting into a photoprotective state, in which the excess and harmful excitation energies are safely dissipated as heat. Here we applied magic angle spinning NMR and selective Arg isotope enrichment as a noninvasive method to analyze the Arg structures of the major light-harvesting complex II (LHCII). The conformations of the Arg residues that interlock helix A and B appear to be preserved in the light-harvesting and photoprotective state. Several Arg residues have very downfield-shifted proton NMR responses, indicating that they stabilize the complex by strong hydrogen bonds. For the Arg Cα chemical shifts, differences are observed between LHCII in the active, light-harvesting and in the photoprotective, quenched state. These differences are attributed to a conformational change of the Arg residue in the stromal loop region. We conclude that the interlocked helices of LHCII form a rigid core. Consequently, the LHCII conformational switch does not involve changes in A/B helix tilting but likely involves rearrangements of the loops and helical segments close to the stromal and lumenal ends.

Structural characterization by NMR of a double phosphorylated chimeric peptide vaccine for treatment of Alzheimer's disease

Rational design of peptide vaccines becomes important for the treatment of some diseases such as Alzheimer's disease (AD) and related disorders. In this study, as part of a larger effort to explore correlations of structure and activity, we attempt to characterize the doubly phosphorylated chimeric peptide vaccine targeting a hyperphosphorylated epitope of the Tau protein. The 28-mer linear chimeric peptide consists of the double phosphorylated B cell epitope Tau₂₂₉₋₂₃₇[pThr231/pSer235] and the immunomodulatory T cell epitope Ag85B₂₄₁₋₂₅₅ originating from the well-known antigen Ag85B of the Mycobacterium tuberculosis, linked by a four amino acid sequence -GPSL-. NMR chemical shift analysis of our construct demonstrated that the synthesized peptide is essentially unfolded with a tendency to form a β-turn due to the linker. In conclusion, the -GPSL- unit presumably connects the two parts of the vaccine without transferring any structural information from one part to the other. Therefore, the double phosphorylated epitope of the Tau peptide is flexible and accessible.

Mammalian neuronal sodium channel blocker μ-conotoxin BuIIIB has a structured N-terminus that influences potency

Among the μ-conotoxins that block vertebrate voltage-gated sodium channels (VGSCs), some have been shown to be potent analgesics following systemic administration in mice. We have determined the solution structure of a new representative of this family, μ-BuIIIB, and established its disulfide connectivities by direct mass spectrometric collision induced dissociation fragmentation of the peptide with disulfides intact. The major oxidative folding product adopts a 1-4/2-5/3-6 pattern with the following disulfide bridges: Cys5-Cys17, Cys6-Cys23, and Cys13-Cys24. The solution structure reveals that the unique N-terminal extension in μ-BuIIIB, which is also present in μ-BuIIIA and μ-BuIIIC but absent in other μ-conotoxins, forms part of a short α-helix encompassing Glu3 to Asn8. This helix is packed against the rest of the toxin and stabilized by the Cys5-Cys17 and Cys6-Cys23 disulfide bonds. As such, the side chain of Val1 is located close to the aromatic rings of Trp16 and His20, which are located on the canonical helix that displays several residues found to be essential for VGSC blockade in related μ-conotoxins. Mutations of residues 2 and 3 in the N-terminal extension enhanced the potency of μ-BuIIIB for NaV1.3. One analogue, [d-Ala2]BuIIIB, showed a 40-fold increase, making it the most potent peptide blocker of this channel characterized to date and thus a useful new tool with which to characterize this channel. On the basis of previous results for related μ-conotoxins, the dramatic effects of mutations at the N-terminus were unanticipated and suggest that further gains in potency might be achieved by additional modifications of this region.

Impacts of 17α-ethynylestradiol exposure on metabolite profiles of zebrafish (Danio rerio) liver cells

Endocrine disrupting chemicals (EDCs) that are frequently detected in bodies of water downstream from sewage treatment facilities can have adverse impacts on fish and other aquatic organisms. To properly assess risk(s) from EDCs, tools are needed that can establish linkages from chemical exposures to adverse outcomes. Traditional methods of testing chemical exposure and toxicity using experimental animals are excessively resource- and time-consuming. In line with EPA's goal of reducing animal use in testing, these traditional screening methods may not be sustainable in the long term, given the ever increasing number of chemicals that must be tested for safety. One of the most promising ways to reduce costs and increase throughput is to use cell cultures instead of experimental animals. In accordance with National Research Council's vision on 21st century toxicity testing, we have developed a cell culture-based metabolomics approach for this application. Using a zebrafish (Danio rerio) liver cell line (ZFL), we have applied NMR-based metabolomics to investigate responses of ZFL cells exposed to 17α-ethynylestradiol (EE2). This analysis showed that metabolite changes induced by EE2 exposure agree well with known impacts of estrogens on live fish. The results of this study demonstrate the potential of cell-based metabolomics to assess chemical exposure and toxicity for regulatory application.

Dynamic nuclear polarization study of inhibitor binding to the M2(18-60) proton transporter from influenza A

We demonstrate the use of dynamic nuclear polarization (DNP) to elucidate ligand binding to a membrane protein using dipolar recoupling magic angle spinning (MAS) NMR. In particular, we detect drug binding in the proton transporter M2(18-60) from influenza A using recoupling experiments at room temperature and with cryogenic DNP. The results indicate that the pore binding site of rimantadine is correlated with previously reported widespread chemical shift changes, suggesting functional binding in the pore. Futhermore, the (15)N-labeled ammonium of rimantadine was observed near A30 (13)Cβ and G34 (13)Cα, suggesting a possible hydrogen bond to A30 carbonyl. Cryogenic DNP was required to observe the weaker external binding site(s) in a ZF-TEDOR spectrum. This approach is generally applicable, particularly for weakly bound ligands, in which case the application of MAS NMR dipolar recoupling requires the low temperatures to quench dynamic exchange processes. For the fully protonated samples investigated, we observed DNP signal enhancements of ~10 at 400 MHz using only 4-6 mM of the polarizing agent TOTAPOL. At 600 MHz and with DNP, we measured a distance between the drug and the protein to a precision of 0.2 Å.

Metabolite profiling to characterize disease-related bacteria: gluconate excretion by Pseudomonas aeruginosa mutants and clinical isolates from cystic fibrosis patients

Metabolic footprinting of supernatants has been proposed as a tool for assigning gene function. We used NMR spectroscopy to measure the exometabolome of 86 single-gene transposon insertion mutant strains (mutants from central carbon metabolism and regulatory mutants) of the opportunistic pathogen Pseudomonas aeruginosa, grown on a medium designed to represent the nutritional content of cystic fibrosis sputum. Functionally related genes had similar metabolic profiles. E.g. for two-component system mutants, the cognate response regulator and sensor kinase genes clustered tightly together. Some strains had metabolic phenotypes (metabotypes) that could be related to the known gene function. E.g. pyruvate dehydrogenase mutants accumulated large amounts of pyruvate in the medium. In other cases, the metabolic phenotypes were not easily interpretable. The rpoN mutant, which lacks the alternative σ factor RpoN (σ⁵⁴), accumulated high levels of gluconate in the medium. In addition, endometabolome profiling of intracellular metabolites identified a number of systemic metabolic changes. We linked this to indirect regulation of the catabolite repression protein Crc via the non-coding RNA crcZ and found that a crcZ (but not crc) mutant also shared the high-gluconate phenotype. We profiled an additional set of relevant metabolic enzymes and transporters, including Crc targets, and showed that the Crc-regulated edd mutant (gluconate-6-phosphate dehydratase) had similar gluconate levels as the rpoN mutant. Finally, a set of clinical isolates showed patient- and random amplification of polymorphic DNA (RAPD) type-specific differences in gluconate production, which were associated significantly with resistance across four antibiotics (tobramycin, ciprofloxacin, aztreonam, and imipenem), indicating that this has potential clinical relevance.

NMR-Based Milk Metabolomics

Milk is a key component in infant nutrition worldwide and, in the Western parts of the world, also in adult nutrition. Milk of bovine origin is both consumed fresh and processed into a variety of dairy products including cheese, fermented milk products, and infant formula. The nutritional quality and processing capabilities of bovine milk is closely associated to milk composition. Metabolomics is ideal in the study of the low-molecular-weight compounds in milk, and this review focuses on the recent nuclear magnetic resonance (NMR)-based metabolomics trends in milk research, including applications linking the milk metabolite profiling with nutritional aspects, and applications which aim to link the milk metabolite profile to various technological qualities of milk. The metabolite profiling studies encompass the identification of novel metabolites, which potentially can be used as biomarkers or as bioactive compounds. Furthermore, metabolomics applications elucidating how the differential regulated genes affects milk composition are also reported. This review will highlight the recent advances in NMR-based metabolomics on milk, as well as give a brief summary of when NMR spectroscopy can be useful for gaining a better understanding of how milk composition is linked to nutritional or quality traits.

Evaluation of Pacific white shrimp (Litopenaeus vannamei) health during a superintensive aquaculture growout using NMR-based metabolomics

Success of the shrimp aquaculture industry requires technological advances that increase production and environmental sustainability. Indoor, superintensive, aquaculture systems are being developed that permit year-round production of farmed shrimp at high densities. These systems are intended to overcome problems of disease susceptibility and of water quality issues from waste products, by operating as essentially closed systems that promote beneficial microbial communities (biofloc). The resulting biofloc can assimilate and detoxify wastes, may provide nutrition for the farmed organisms resulting in improved growth, and may aid in reducing disease initiated from external sources. Nuclear magnetic resonance (NMR)-based metabolomic techniques were used to assess shrimp health during a full growout cycle from the nursery phase through harvest in a minimal-exchange, superintensive, biofloc system. Aberrant shrimp metabolomes were detected from a spike in total ammonia nitrogen in the nursery, from a reduced feeding period that was a consequence of surface scum build-up in the raceway, and from the stocking transition from the nursery to the growout raceway. The biochemical changes in the shrimp that were induced by the stressors were essential for survival and included nitrogen detoxification and energy conservation mechanisms. Inosine and trehalose may be general biomarkers of stress in Litopenaeus vannamei. This study demonstrates one aspect of the practicality of using NMR-based metabolomics to enhance the aquaculture industry by providing physiological insight into common environmental stresses that may limit growth or better explain reduced survival and production.

NightShift: NMR shift inference by general hybrid model training--a framework for NMR chemical shift prediction

Background

NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.

A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore.

Results

In this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction.

In addition to this main result – the NightShift framework itself – we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline.

Conclusion

By demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators.

The framework can be downloaded from https://bitbucket.org/akdehof/nightshift. It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via https://ballaxy.bioinf.uni-sb.de/.

Peakr: simulating solid-state NMR spectra of proteins

MOTIVATION

When analyzing solid-state nuclear magnetic resonance (NMR) spectra of proteins, assignment of resonances to nuclei and derivation of restraints for 3D structure calculations are challenging and time-consuming processes. Simulated spectra that have been calculated based on, for example, chemical shift predictions and structural models can be of considerable help. Existing solutions are typically limited in the type of experiment they can consider and difficult to adapt to different settings.

RESULTS

Here, we present Peakr, a software to simulate solid-state NMR spectra of proteins. It can generate simulated spectra based on numerous common types of internuclear correlations relevant for assignment and structure elucidation, can compare simulated and experimental spectra and produces lists and visualizations useful for analyzing measured spectra. Compared with other solutions, it is fast, versatile and user friendly.

AVAILABILITY AND IMPLEMENTATION

Peakr is maintained under the GPL license and can be accessed at http://www.peakr.org. The source code can be obtained on request from the authors.

The future of the Protein Data Bank

The Worldwide Protein Data Bank (wwPDB) is the international collaboration that manages the deposition, processing and distribution of the PDB archive. The wwPDB's mission is to maintain a single archive of macromolecular structural data that are freely and publicly available to the global community. Its members [RCSB PDB (USA), PDBe (Europe), PDBj (Japan), and BMRB (USA)] host data-deposition sites and mirror the PDB ftp archive. To support future developments in structural biology, the wwPDB partners are addressing organizational, scientific, and technical challenges.

Structural features of the apelin receptor N-terminal tail and first transmembrane segment implicated in ligand binding and receptor trafficking

G-protein coupled receptors (GPCRs) comprise a large family of membrane proteins with rich functional diversity. Signaling through the apelin receptor (AR or APJ) influences the cardiovascular system, central nervous system and glucose regulation. Pathophysiological involvement of apelin has been shown in atherosclerosis, cancer, human immunodeficiency virus-1 (HIV-1) infection and obesity. Here, we present the high-resolution nuclear magnetic resonance (NMR) spectroscopy-based structure of the N-terminus and first transmembrane (TM) segment of AR (residues 1-55, AR55) in dodecylphosphocholine micelles. AR55 consists of two disrupted helices, spanning residues D14-K25 and A29-R55(1.59). Molecular dynamics (MD) simulations of AR built from a hybrid of experimental NMR and homology model-based restraints allowed validation of the AR55 structure in the context of the full-length receptor in a hydrated bilayer. AR55 structural features were functionally probed using mutagenesis in full-length AR through monitoring of apelin-induced extracellular signal-regulated kinase (ERK) phosphorylation in transiently transfected human embryonic kidney (HEK) 293A cells. Residues E20 and D23 form an extracellular anionic face and interact with lipid headgroups during MD simulations in the absence of ligand, producing an ideal binding site for a cationic apelin ligand proximal to the membrane-water interface, lending credence to membrane-catalyzed apelin-AR binding. In the TM region of AR55, N46(1.50) is central to a disruption in helical character. G42(1.46), G45(1.49) and N46(1.50), which are all involved in the TM helical disruption, are essential for proper trafficking of AR. In summary, we introduce a new correlative NMR spectroscopy and computational biochemistry methodology and demonstrate its utility in providing some of the first high-resolution structural information for a peptide-activated GPCR TM domain.

Chemical shifts in biomolecules

NMR chemical shifts are sensitive probes of structure and dynamics in proteins. Empirical models, based on a large database of measured shifts, take an input structure and provide increasingly accurate estimates of the corresponding shifts. Quantum chemical calculations can provide the same information, with greater generality but (currently) with less accuracy. These methods are now providing new ways to approach NMR structure determination, and new insights into the conformational dynamics of proteins.

NMR spectroscopy of macrophages loaded with native, oxidized or enzymatically degraded lipoproteins

Oxidized and enzymatically modified low-density lipoproteins (oxLDL and eLDL) play a key role in early stages of atherogenesis. Their uptake by recruited macrophages leads to endolysosomal phospholipidosis or foam cell formation, respectively, each of which is preceded by highly differential lipid restructuring processes. We applied ¹H-NMR spectroscopy (NMRS) to elucidate these structural rearrangements both in consequence of lipoprotein modifications and following phagocytosis. Being specifically sensitive to the mobile lipid subset, NMRS of oxLDL and eLDL revealed a partial and total immobilization of lipids, respectively. NMRS of intact macrophages showed a sixfold increase in mobile lipids in case of loading with eLDL but no significant changes for oxLDL or native LDL. This finding reflected the disparate lipid storage in lipid droplets and in multilamellar endolysosomal clusters when loaded with either eLDL or oxLDL, respectively. Moreover, a significant shift of the degree of saturation towards mainly polyunsaturated fatty acid chains was found for the mobile lipid pool in eLDL-loaded macrophages. Additional analyses of lipid extracts by NMRS and mass spectrometry (MS) reflected these changes in lipid content and in fatty acid composition only partially. In summary, in-cell NMRS represents a unique lipidomics tool to investigate structural changes within the mobile lipid pool following atherogenic triggers that can be not detected by the analysis of lipid extracts by MS or NMRS.

Utility of 1H NMR chemical shifts in determining RNA structure and dynamics

The development of methods for predicting NMR chemical shifts with high accuracy and speed is increasingly allowing use of these abundant, readily accessible measurements in determining the structure and dynamics of proteins. For nucleic acids, however, despite the availability of semiempirical methods for predicting (1)H chemical shifts, their use in determining the structure and dynamics has not yet been examined. Here, we show that (1)H chemical shifts offer powerful restraints for RNA structure determination, allowing discrimination of native structure from non-native states to within 2-4 Å, and <3 Å when highly flexible residues are ignored. Theoretical simulations shows that although (1)H chemical shifts can provide valuable information for constructing RNA dynamic ensembles, large uncertainties in the chemical shift predictions and inherent degeneracies lead to higher uncertainties as compared to residual dipolar couplings.

Vivaldi: visualization and validation of biomacromolecular NMR structures from the PDB

We describe Vivaldi (VIsualization and VALidation DIsplay; http://pdbe.org/vivaldi), a web-based service for the analysis, visualization, and validation of NMR structures in the Protein Data Bank (PDB). Vivaldi provides access to model coordinates and several types of experimental NMR data using interactive visualization tools, augmented with structural annotations and model-validation information. The service presents information about the modeled NMR ensemble, validation of experimental chemical shifts, residual dipolar couplings, distance and dihedral angle constraints, as well as validation scores based on empirical knowledge and databases. Vivaldi was designed for both expert NMR spectroscopists and casual non-expert users who wish to obtain a better grasp of the information content and quality of NMR structures in the public archive. © Proteins 2013. © 2012 Wiley Periodicals, Inc.

Recent advances in computational methods for nuclear magnetic resonance data processing

Although three-dimensional protein structure determination using nuclear magnetic resonance (NMR) spectroscopy is a computationally costly and tedious process that would benefit from advanced computational techniques, it has not garnered much research attention from specialists in bioinformatics and computational biology. In this paper, we review recent advances in computational methods for NMR protein structure determination. We summarize the advantages of and bottlenecks in the existing methods and outline some open problems in the field. We also discuss current trends in NMR technology development and suggest directions for research on future computational methods for NMR.

MetaboID: a graphical user interface package for assignment of 1H NMR spectra of bodyfluids and tissues

Nuclear magnetic resonance based measurements of small molecule mixtures continues to be confronted with the challenge of spectral assignment. While multi-dimensional experiments are capable of addressing this challenge, the imposed time constraint becomes prohibitive, particularly with the large sample sets commonly encountered in metabolomic studies. Thus, one-dimensional spectral assignment is routinely performed, guided by two-dimensional experiments on a selected sample subset; however, a publicly available graphical interface for aiding in this process is currently unavailable. We have collected spectral information for 360 unique compounds from publicly available databases including chemical shift lists and authentic full resolution spectra, supplemented with spectral information for 25 compounds collected in-house at a proton NMR frequency of 900 MHz. This library serves as the basis for MetaboID, a Matlab-based user interface designed to aid in the one-dimensional spectral assignment process. The tools of MetaboID were built to guide resonance assignment in order of increasing confidence, starting from cursory compound searches based on chemical shift positions to analysis of authentic spike experiments. Together, these tools streamline the often repetitive task of spectral assignment. The overarching goal of the integrated toolbox of MetaboID is to centralize the one dimensional spectral assignment process, from providing access to large chemical shift libraries to providing a straightforward, intuitive means of spectral comparison. Such a toolbox is expected to be attractive to both experienced and new metabolomic researchers as well as general complex mixture analysts.

HASH: a program to accurately predict protein Hα shifts from neighboring backbone shifts

Chemical shifts provide not only peak identities for analyzing nuclear magnetic resonance (NMR) data, but also an important source of conformational information for studying protein structures. Current structural studies requiring H(α) chemical shifts suffer from the following limitations. (1) For large proteins, the H(α) chemical shifts can be difficult to assign using conventional NMR triple-resonance experiments, mainly due to the fast transverse relaxation rate of C(α) that restricts the signal sensitivity. (2) Previous chemical shift prediction approaches either require homologous models with high sequence similarity or rely heavily on accurate backbone and side-chain structural coordinates. When neither sequence homologues nor structural coordinates are available, we must resort to other information to predict H(α) chemical shifts. Predicting accurate H(α) chemical shifts using other obtainable information, such as the chemical shifts of nearby backbone atoms (i.e., adjacent atoms in the sequence), can remedy the above dilemmas, and hence advance NMR-based structural studies of proteins. By specifically exploiting the dependencies on chemical shifts of nearby backbone atoms, we propose a novel machine learning algorithm, called HASH, to predict H(α) chemical shifts. HASH combines a new fragment-based chemical shift search approach with a non-parametric regression model, called the generalized additive model, to effectively solve the prediction problem. We demonstrate that the chemical shifts of nearby backbone atoms provide a reliable source of information for predicting accurate H(α) chemical shifts. Our testing results on different possible combinations of input data indicate that HASH has a wide rage of potential NMR applications in structural and biological studies of proteins.