Protein NMR spectroscopy in structural genomics

Fluorine Labeling and 19F NMR Spectroscopy to Study Biological Molecules and Molecular Complexes

High-resolution nuclear magnetic resonance (NMR) spectroscopy represents a key methodology for studying biomolecules and their interplay with other molecules. Recent developments in labeling strategies have made it possible to incorporate fluorine into proteins and peptides reliably, with manageable efforts and, importantly, in a highly site-specific manner. Paired with its excellent NMR spectroscopic properties and absence in most biological systems, fluorine has enabled scientists to investigate a rather wide range of scientific objectives, including protein folding, protein dynamics and drug discovery. Furthermore, NMR spectroscopic experiments can be conducted in complex environments, such as cell lysate or directly inside living cells. This review presents selected studies demonstrating how 19F NMR spectroscopic approaches enable to contribute to the understanding of biomolecular processes. Thereby the focus has been set to labeling strategies available and specific NMR experiments performed to answer the underlying scientific objective.

Global atlas of predicted functional domains in Legionella pneumophila Dot/Icm translocated effectors

Legionella pneumophila utilizes the Dot/Icm type IVB secretion system to deliver hundreds of effector proteins inside eukaryotic cells to ensure intracellular replication. Our understanding of the molecular functions of the largest pathogenic arsenal known to the bacterial world remains incomplete. By leveraging advancements in 3D protein structure prediction, we provide a comprehensive structural analysis of 368 L. pneumophila effectors, representing a global atlas of predicted functional domains summarized in a database ( https://pathogens3d.org/legionella-pneumophila ). Our analysis identified 157 types of diverse functional domains in 287 effectors, including 159 effectors with no prior functional annotations. Furthermore, we identified 35 cryptic domains in 30 effector models that have no similarity with experimentally structurally characterized proteins, thus, hinting at novel functionalities. Using this analysis, we demonstrate the activity of thirteen functional domains, including three cryptic domains, predicted in L. pneumophila effectors to cause growth defects in the Saccharomyces cerevisiae model system. This illustrates an emerging strategy of exploring synergies between predictions and targeted experimental approaches in elucidating novel effector activities involved in infection.

Next-generation microbiology: from comparative genomics to gene function

Microbiology is at a turning point in its 120-year history. Widespread next-generation sequencing has revealed genetic complexity among bacteria that could hardly have been imagined by pioneers such as Pasteur, Escherich and Koch. This data cascade brings enormous potential to improve our understanding of individual bacterial cells and the genetic basis of phenotype variation. However, this revolution in data science cannot replace established microbiology practices, presenting the challenge of how to integrate these new techniques. Contrasting comparative and functional genomic approaches, we evoke molecular microbiology theory and established practice to present a conceptual framework and practical roadmap for next-generation microbiology.

Understanding Leishmania parasites through proteomics and implications for the clinic

INTRODUCTION

Leishmania spp. are causative agents of leishmaniasis, a broad-spectrum neglected vector-borne disease. Genomic and transcriptional studies are not capable of solving intricate biological mysteries, leading to the emergence of proteomics, which can provide insights into the field of parasite biology and its interactions with the host. Areas covered: The combination of genomics and informatics with high throughput proteomics may improve our understanding of parasite biology and pathogenesis. This review analyses the roles of diverse proteomic technologies that facilitate our understanding of global protein profiles and definition of parasite development, survival, virulence and drug resistance mechanisms for disease intervention. Additionally, recent innovations in proteomics have provided insights concerning the drawbacks associated with conventional chemotherapeutic approaches and Leishmania biology, host-parasite interactions and the development of new therapeutic approaches. Expert commentary: With progressive breakthroughs in the foreseeable future, proteome profiles could provide target molecules for vaccine development and therapeutic intervention. Furthermore, proteomics, in combination with genomics and informatics, could facilitate the elimination of several diseases. Taken together, this review provides an outlook on developments in Leishmania proteomics and their clinical implications.

POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins

Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25-78%, with the largest improvements observed for ¹Hα and ¹³C'. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org .

SERS Quantification and Characterization of Proteins and Other Biomolecules

Changes in protein expression levels and protein structure may indicate genomic mutations and may be related to some diseases. Therefore, the precise quantification and characterization of proteins can be used for disease diagnosis. Compared with several other alternative methods, surface-enhanced Raman scattering (SERS) spectroscopy is regarded as an excellent choice for the quantification and structural characterization of proteins. Herein, we review the main advance of using plasmonic nanostructures as SERS sensing platform for this purpose. Three design approaches, including direct SERS, indirect SERS, and SERS-encoded nanoparticles, are discussed in the direction of developing new precise approaches of quantification and characterization of proteins. While this Review is focused on proteins, in order to highlight concepts of SERS-based sensors also detection of other biomolecules will be discussed.

NMR studies of the non-haem Fe(II) and 2-oxoglutarate-dependent oxygenases

The non-haem Fe(II) and 2-oxoglutarate (2OG)-dependent oxygenases belong to a superfamily of structurally-related enzymes that play important biological roles in plants, microorganisms and animals. Structural, mechanistic and functional studies of 2OG oxygenases require efficient and effective biophysical tools. Nuclear magnetic resonance (NMR) spectroscopy is a useful tool to study this enzyme superfamily. It has been applied to obtain information about enzyme kinetics, identify and characterise 2OG oxygenase-catalysed oxidation products, elucidate the catalytic mechanism, monitor ligand binding and study protein dynamics. This review summarises the types of information that NMR spectroscopy can provide in the studies of 2OG oxygenases, highlights the advantages of the technique and describes its drawbacks.

Prediction of Structural and Functional Aspects of Protein

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

NMR in structural genomics to increase structural coverage of the protein universe: Delivered by Prof. Kurt Wüthrich on 7 July 2013 at the 38th FEBS Congress in St. Petersburg, Russia

For more than a decade, the Joint Center for Structural Genomics (JCSG; www.jcsg.org) worked toward increased three-dimensional structure coverage of the protein universe. This coordinated quest was one of the main goals of the four high-throughput (HT) structure determination centers of the Protein Structure Initiative (PSI; www.nigms.nih.gov/Research/specificareas/PSI). To achieve the goals of the PSI, the JCSG made use of the complementarity of structure determination by X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy to increase and diversify the range of targets entering the HT structure determination pipeline. The overall strategy, for both techniques, was to determine atomic resolution structures for representatives of large protein families, as defined by the Pfam database, which had no structural coverage and could make significant contributions to biological and biomedical research. Furthermore, the experimental structures could be leveraged by homology modeling to further expand the structural coverage of the protein universe and increase biological insights. Here, we describe what could be achieved by this structural genomics approach, using as an illustration the contributions from 20 NMR structure determinations out of a total of 98 JCSG NMR structures, which were selected because they are the first three-dimensional structure representations of the respective Pfam protein families. The information from this small sample is representative for the overall results from crystal and NMR structure determination in the JCSG. There are five new folds, which were classified as domains of unknown functions (DUF), three of the proteins could be functionally annotated based on three-dimensional structure similarity with previously characterized proteins, and 12 proteins showed only limited similarity with previous deposits in the Protein Data Bank (PDB) and were classified as DUFs.

Protocol To Make Protein NMR Structures Amenable to Stable Long Time Scale Molecular Dynamics Simulations

A robust protocol for the treatment of NMR protein structures is presented that makes them amenable to long time scale molecular dynamics (MD) simulations that are stable. The protocol embeds an NMR structure in a native low energy region of the recently developed ff99SB_φψ(g24;CS) molecular mechanics force field. Extended MD trajectories that start from these structures show good consistency with proton-proton nuclear Overhauser effect data, and they reproduce NMR chemical shift data better than the original NMR structures as is demonstrated for four protein systems. Moreover, for all proteins studied here the simulations spontaneously approach the X-ray crystal structures, thereby improving the effective resolution of the initial structural models.

JPred4: a protein secondary structure prediction server

JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction. In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials.

Hydrogen-deuterium exchange mass spectrometry for determining protein structural changes in drug discovery

Protein structures are dynamically changed in response to post-translational modifications, ligand or chemical binding, or protein-protein interactions. Understanding the structural changes that occur in proteins in response to potential candidate drugs is important for predicting the modes of action of drugs and their functions and regulations. Recent advances in hydrogen/deuterium exchange mass spectrometry (HDX-MS) have the potential to offer a tool for obtaining such understanding similarly to other biophysical techniques, such as X-ray crystallography and high resolution NMR. We present here, a review of basic concept and methodology of HDX-MS, how it is being applied for identifying the sites and structural changes in proteins following their interactions with other proteins and small molecules, and the potential of this tool to help in drug discovery.

Protein Crystallography from the Perspective of Technology Developments

Early on, crystallography was a domain of mineralogy and mathematics and dealt mostly with symmetry properties and imaginary crystal lattices. This changed when Wilhelm Conrad Röntgen discovered X-rays in 1895, and in 1912 Max von Laue and his associates discovered X-ray irradiated salt crystals would produce diffraction patterns that could reveal the internal atomic periodicity of the crystals. In the same year the father-and-son team, Henry and Lawrence Bragg successfully solved the first crystal structure of sodium chloride and the era of modern crystallography began. Protein crystallography (PX) started some 20 years later with the pioneering work of British crystallographers. In the past 50-60 years, the achievements of modern crystallography and particularly those in protein crystallography have been due to breakthroughs in theoretical and technical advancements such as phasing and direct methods; to more powerful X-ray sources such as synchrotron radiation (SR); to more sensitive and efficient X-ray detectors; to ever faster computers and to improvements in software. The exponential development of protein crystallography has been accelerated by the invention and applications of recombinant DNA technology that can yield nearly any protein of interest in large amounts and with relative ease. Novel methods, informatics platforms, and technologies for automation and high-throughput have allowed the development of large-scale, high efficiency macromolecular crystallography efforts in the field of structural genomics (SG). Very recently, the X-ray free-electron laser (XFEL) sources and its applications in protein crystallography have shown great potential for revolutionizing the whole field again in the near future.

Changes in signal transducer and activator of transcription 3 (STAT3) dynamics induced by complexation with pharmacological inhibitors of Src homology 2 (SH2) domain dimerization

The activity of the transcription factor signal transducer and activator of transcription 3 (STAT3) is dysregulated in a number of hematological and solid malignancies. Development of pharmacological STAT3 Src homology 2 (SH2) domain interaction inhibitors holds great promise for cancer therapy, and a novel class of salicylic acid-based STAT3 dimerization inhibitors that includes orally bioavailable drug candidates has been recently developed. The compounds SF-1-066 and BP-1-102 are predicted to bind to the STAT3 SH2 domain. However, given the highly unstructured and dynamic nature of the SH2 domain, experimental confirmation of this prediction was elusive. We have interrogated the protein-ligand interaction of STAT3 with these small molecule inhibitors by means of time-resolved electrospray ionization hydrogen-deuterium exchange mass spectrometry. Analysis of site-specific evolution of deuterium uptake induced by the complexation of STAT3 with SF-1-066 or BP-1-102 under physiological conditions enabled the mapping of the in silico predicted inhibitor binding site to the STAT3 SH2 domain. The binding of both inhibitors to the SH2 domain resulted in significant local decreases in dynamics, consistent with solvent exclusion at the inhibitor binding site and increased rigidity of the inhibitor-complexed SH2 domain. Interestingly, inhibitor binding induced hot spots of allosteric perturbations outside of the SH2 domain, manifesting mainly as increased deuterium uptake, in regions of STAT3 important for DNA binding and nuclear localization.

Compensatory adaptations of structural dynamics in an intrinsically disordered protein complex

Intrinsically disordered proteins (IDPs) play crucial roles in protein interaction networks and in this context frequently constitute important hubs and interfaces. Here we show by a combination of NMR and EPR spectroscopy that the binding of the cytokine osteopontin (OPN) to its natural ligand, heparin, is accompanied by thermodynamically compensating structural adaptations. The core segment of OPN expands upon binding. This "unfolding-upon-binding" is governed primarily through electrostatic interactions between heparin and charged patches along the protein backbone and compensates for entropic penalties due to heparin-OPN binding. It is shown how structural unfolding compensates for entropic losses through ligand binding in IDPs and elucidates the interplay between structure and thermodynamics of rapid substrate-binding and -release events in IDP interaction networks.

Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions.

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.

Fast automated NMR spectroscopy of short-lived biological samples

Faster than death: NMR techniques that make use of nonlinear sampling and hyperdimensional processing enable the recording of complete NMR data sets for the automated assignment of the backbone and side-chain resonances of short-lived protein samples of cell lysates.

A computational analysis of the binding mode of closantel as inhibitor of the Onchocerca volvulus chitinase: insights on macrofilaricidal drug design

Onchocerciasis is a leading cause of blindness with at least 37 million people infected and more than 120 million people at risk of contracting the disease; most (99%) of this population, threatened by infection, live in Africa. The drug of choice for mass treatment is the microfilaricidal Mectizan(®) (ivermectin); it does not kill the adult stages of the parasite at the standard dose which is a single annual dose aimed at disease control. However, multiple treatments a year with ivermectin have effects on adult worms. The discovery of new therapeutic targets and drugs directed towards the killing of the adult parasites are thus urgently needed. The chitinase of filarial nematodes is a new drug target due to its essential function in the metabolism and molting of the parasite. Closantel is a potent and specific inhibitor of chitinase of Onchocerca volvulus (OvCHT1) and other filarial chitinases. However, the binding mode and specificity of closantel towards OvCHT1 remain unknown. In the absence of a crystallographic structure of OvCHT1, we developed a homology model of OvCHT1 using the currently available X-ray structures of human chitinases as templates. Energy minimization and molecular dynamics (MD) simulation of the model led to a high quality of 3D structure of OvCHIT1. A flexible docking study using closantel as the ligand on the binding site of OvCHIT1 and human chitinases was performed and demonstrated the differences in the closantel binding mode between OvCHIT1 and human chitinase. Furthermore, molecular dynamics simulations and free-energy calculation were employed to determine and compare the detailed binding mode of closantel with OvCHT1 and the structure of human chitinase. This comparative study allowed identification of structural features and properties responsible for differences in the computationally predicted closantel binding modes. The homology model and the closantel binding mode reported herein might help guide the rational development of novel drugs against the adult parasite of O. volvulus and such findings could be extrapolated to other filarial neglected diseases.

Molecular recognition in the case of flexible targets

A protein's flexibility is well recognized to underlie its capacity to engage in critical functions, such as signal transduction, biomolecular transport and biochemical reactivity. Molecular recognition is also tightly linked to the dynamics of the binding partners, yet protein flexibility has largely been ignored by the growing field of structure-based drug design (SBDD). In combination with experimentally determined structures, a number of computational methods have been proposed to model protein movements, which may be important for small molecule binding. Such techniques have the ability to expose new binding site conformations, which may in turn recognize and lead to the discovery of more potent and selective drugs through molecular docking. In this article, we discuss various methods and focus on the Relaxed Complex Scheme (RCS), which uses Molecular Dynamics (MD) simulations to model full protein flexibility and enhance virtual screening programmes. We review practical applications of the RCS and use a recent study of the HIV-1 reverse transcriptase to illustrate the various phases of the scheme. We also discuss some encouraging developments, aimed at addressing current weaknesses of the RCS.

Applying an empirical hydropathic forcefield in refinement may improve low-resolution protein X-ray crystal structures

Background

The quality of X-ray crystallographic models for biomacromolecules refined from data obtained at high-resolution is assured by the data itself. However, at low-resolution, >3.0 Å, additional information is supplied by a forcefield coupled with an associated refinement protocol. These resulting structures are often of lower quality and thus unsuitable for downstream activities like structure-based drug discovery.

Methodology

An X-ray crystallography refinement protocol that enhances standard methodology by incorporating energy terms from the HINT (Hydropathic INTeractions) empirical forcefield is described. This protocol was tested by refining synthetic low-resolution structural data derived from 25 diverse high-resolution structures, and referencing the resulting models to these structures. The models were also evaluated with global structural quality metrics, e.g., Ramachandran score and MolProbity clashscore. Three additional structures, for which only low-resolution data are available, were also re-refined with this methodology.

Results

The enhanced refinement protocol is most beneficial for reflection data at resolutions of 3.0 Å or worse. At the low-resolution limit, ≥4.0 Å, the new protocol generated models with Cα positions that have RMSDs that are 0.18 Å more similar to the reference high-resolution structure, Ramachandran scores improved by 13%, and clashscores improved by 51%, all in comparison to models generated with the standard refinement protocol. The hydropathic forcefield terms are at least as effective as Coulombic electrostatic terms in maintaining polar interaction networks, and significantly more effective in maintaining hydrophobic networks, as synthetic resolution is decremented. Even at resolutions ≥4.0 Å, these latter networks are generally native-like, as measured with a hydropathic interactions scoring tool.

A novel strategy for NMR resonance assignment and protein structure determination

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

Fully automated high-quality NMR structure determination of small (2)H-enriched proteins

Determination of high-quality small protein structures by nuclear magnetic resonance (NMR) methods generally requires acquisition and analysis of an extensive set of structural constraints. The process generally demands extensive backbone and sidechain resonance assignments, and weeks or even months of data collection and interpretation. Here we demonstrate rapid and high-quality protein NMR structure generation using CS-Rosetta with a perdeuterated protein sample made at a significantly reduced cost using new bacterial culture condensation methods. Our strategy provides the basis for a high-throughput approach for routine, rapid, high-quality structure determination of small proteins. As an example, we demonstrate the determination of a high-quality 3D structure of a small 8 kDa protein, E. coli cold shock protein A (CspA), using <4 days of data collection and fully automated data analysis methods together with CS-Rosetta. The resulting CspA structure is highly converged and in excellent agreement with the published crystal structure, with a backbone RMSD value of 0.5 Å, an all atom RMSD value of 1.2 Å to the crystal structure for well-defined regions, and RMSD value of 1.1 Å to crystal structure for core, non-solvent exposed sidechain atoms. Cross validation of the structure with (15)N- and (13)C-edited NOESY data obtained with a perdeuterated (15)N, (13)C-enriched (13)CH(3) methyl protonated CspA sample confirms that essentially all of these independently-interpreted NOE-based constraints are already satisfied in each of the 10 CS-Rosetta structures. By these criteria, the CS-Rosetta structure generated by fully automated analysis of data for a perdeuterated sample provides an accurate structure of CspA. This represents a general approach for rapid, automated structure determination of small proteins by NMR.

NMR characterization of foldedness for the production of E3 RING domains

We summarize the use of NMR spectroscopy in the production and the screening of stability and foldedness of protein domains, and apply it to the RING domains of E3 ubiquitin-ligases. RING domains are involved in specific interactions with E2 ubiquitin-conjugating enzymes and thus play an essential role in the ubiquitination pathway. Protein production of the Zn(2+) containing and cysteine rich RING domains for molecular studies frequently turns out to be problematic. We compared the expression and solubility of 14 E3 RING/U-box domains fused to the N-terminal tags of His(6), His(6)-GB1, His(6)-Trx and His(6)-GST at small scale and analyzed, by NMR spectroscopy, their correct folding after purification. The addition of GST, Trx or GB1 to the N-terminal His(6) tag significantly improved both the expression and solubility of target proteins as compared to His(6) tag alone. More importantly most of the immobilized metal affinity chromatography (IMAC) purified proteins were largely unfolded as judged by analysis of the (1)H-(15)N HSQC spectra. We demonstrate that imidazole causes a concentration dependent decrease in stability of RING proteins ascribed to metal depletion and resulting in unfolding or precipitation. In contrast, using glutathione affinity chromatography, the His(6)-GST fused RING and U-box domains were purified as correctly folded proteins with high yields. Our data clearly demonstrate that IMAC should be avoided and that GST-fusion affinity chromatography is generally applicable for expression and purification of Zn(2+) containing proteins.

NMR assignment method for amide signals with cell-free protein synthesis system

Nuclear magnetic resonance (NMR) methods are widely used to determine the three-dimensional structures of proteins, to estimate protein folding, and to discover high-affinity ligands for proteins. However, one of the problems to apply such NMR methods to proteins is that we should obtain mg quantities of (15)N and/or (13)C labeled pure proteins of interest. Here, we describe the method to produce dual amino acid-selective (13)C-(15)N labeled proteins for NMR study using the improved wheat germ cell-free system, which enables sequence-specific assignments of amide signals simply even for very large protein.

SAIL – stereo-array isotope labeling

Optimal stereospecific and regiospecific labeling of proteins with stable isotopes enhances the nuclear magnetic resonance (NMR) method for the determination of the three-dimensional protein structures in solution. Stereo-array isotope labeling (SAIL) offers sharpened lines, spectral simplification without loss of information and the ability to rapidly collect and automatically evaluate the structural restraints required to solve a high-quality solution structure for proteins up to twice as large as before. This review gives an overview of stable isotope labeling methods for NMR spectroscopy with proteins and provides an in-depth treatment of the SAIL technology.

Improving small-angle X-ray scattering data for structural analyses of the RNA world

Defining the shape, conformation, or assembly state of an RNA in solution often requires multiple investigative tools ranging from nucleotide analog interference mapping to X-ray crystallography. A key addition to this toolbox is small-angle X-ray scattering (SAXS). SAXS provides direct structural information regarding the size, shape, and flexibility of the particle in solution and has proven powerful for analyses of RNA structures with minimal requirements for sample concentration and volumes. In principle, SAXS can provide reliable data on small and large RNA molecules. In practice, SAXS investigations of RNA samples can show inconsistencies that suggest limitations in the SAXS experimental analyses or problems with the samples. Here, we show through investigations on the SAM-I riboswitch, the Group I intron P4-P6 domain, 30S ribosomal subunit from Sulfolobus solfataricus (30S), brome mosaic virus tRNA-like structure (BMV TLS), Thermotoga maritima asd lysine riboswitch, the recombinant tRNA(val), and yeast tRNA(phe) that many problems with SAXS experiments on RNA samples derive from heterogeneity of the folded RNA. Furthermore, we propose and test a general approach to reducing these sample limitations for accurate SAXS analyses of RNA. Together our method and results show that SAXS with synchrotron radiation has great potential to provide accurate RNA shapes, conformations, and assembly states in solution that inform RNA biological functions in fundamental ways.

CSSI-PRO: a method for secondary structure type editing, assignment and estimation in proteins using linear combination of backbone chemical shifts

Estimation of secondary structure in polypeptides is important for studying their structure, folding and dynamics. In NMR spectroscopy, such information is generally obtained after sequence specific resonance assignments are completed. We present here a new methodology for assignment of secondary structure type to spin systems in proteins directly from NMR spectra, without prior knowledge of resonance assignments. The methodology, named Combination of Shifts for Secondary Structure Identification in Proteins (CSSI-PRO), involves detection of specific linear combination of backbone (1)H(alpha) and (13)C' chemical shifts in a two-dimensional (2D) NMR experiment based on G-matrix Fourier transform (GFT) NMR spectroscopy. Such linear combinations of shifts facilitate editing of residues belonging to alpha-helical/beta-strand regions into distinct spectral regions nearly independent of the amino acid type, thereby allowing the estimation of overall secondary structure content of the protein. Comparison of the predicted secondary structure content with those estimated based on their respective 3D structures and/or the method of Chemical Shift Index for 237 proteins gives a correlation of more than 90% and an overall rmsd of 7.0%, which is comparable to other biophysical techniques used for structural characterization of proteins. Taken together, this methodology has a wide range of applications in NMR spectroscopy such as rapid protein structure determination, monitoring conformational changes in protein-folding/ligand-binding studies and automated resonance assignment.

Contact replacement for NMR resonance assignment

MOTIVATION

Complementing its traditional role in structural studies of proteins, nuclear magnetic resonance (NMR) spectroscopy is playing an increasingly important role in functional studies. NMR dynamics experiments characterize motions involved in target recognition, ligand binding, etc., while NMR chemical shift perturbation experiments identify and localize protein-protein and protein-ligand interactions. The key bottleneck in these studies is to determine the backbone resonance assignment, which allows spectral peaks to be mapped to specific atoms. This article develops a novel approach to address that bottleneck, exploiting an available X-ray structure or homology model to assign the entire backbone from a set of relatively fast and cheap NMR experiments.

RESULTS

We formulate contact replacement for resonance assignment as the problem of computing correspondences between a contact graph representing the structure and an NMR graph representing the data; the NMR graph is a significantly corrupted, ambiguous version of the contact graph. We first show that by combining connectivity and amino acid type information, and exploiting the random structure of the noise, one can provably determine unique correspondences in polynomial time with high probability, even in the presence of significant noise (a constant number of noisy edges per vertex). We then detail an efficient randomized algorithm and show that, over a variety of experimental and synthetic datasets, it is robust to typical levels of structural variation (1-2 AA), noise (250-600%) and missings (10-40%). Our algorithm achieves very good overall assignment accuracy, above 80% in alpha-helices, 70% in beta-sheets and 60% in loop regions.

AVAILABILITY

Our contact replacement algorithm is implemented in platform-independent Python code. The software can be freely obtained for academic use by request from the authors.

Protein production and purification

In selecting a method to produce a recombinant protein, a researcher is faced with a bewildering array of choices as to where to start. To facilitate decision-making, we describe a consensus 'what to try first' strategy based on our collective analysis of the expression and purification of over 10,000 different proteins. This review presents methods that could be applied at the outset of any project, a prioritized list of alternate strategies and a list of pitfalls that trip many new investigators.

Rapid measurement of 3J(H N-H alpha) and 3J(N-H beta) coupling constants in polypeptides

We present two NMR experiments, (3,2)D HNHA and (3,2)D HNHB, for rapid and accurate measurement of 3J(H N-H alpha) and 3J(N-H beta) coupling constants in polypeptides based on the principle of G-matrix Fourier transform NMR spectroscopy and quantitative J-correlation. These experiments, which facilitate fast acquisition of three-dimensional data with high spectral/digital resolution and chemical shift dispersion, will provide renewed opportunities to utilize them for sequence specific resonance assignments, estimation/characterization of secondary structure with/without prior knowledge of resonance assignments, stereospecific assignment of prochiral groups and 3D structure determination, refinement and validation. Taken together, these experiments have a wide range of applications from structural genomics projects to studying structure and folding in polypeptides.

Building native protein conformation from NMR backbone chemical shifts using Monte Carlo fragment assembly

We have been analyzing the extent to which protein secondary structure determines protein tertiary structure in simple protein folds. An earlier paper demonstrated that three-dimensional structure can be obtained successfully using only highly approximate backbone torsion angles for every residue. Here, the initial information is further diluted by introducing a realistic degree of experimental uncertainty into this process. In particular, we tackle the practical problem of determining three-dimensional structure solely from backbone chemical shifts, which can be measured directly by NMR and are known to be correlated with a protein's backbone torsion angles. Extending our previous algorithm to incorporate these experimentally determined data, clusters of structures compatible with the experimentally determined chemical shifts were generated by fragment assembly Monte Carlo. The cluster that corresponds to the native conformation was then identified based on four energy terms: steric clash, solvent-squeezing, hydrogen-bonding, and hydrophobic contact. Currently, the method has been applied successfully to five small proteins with simple topology. Although still under development, this approach offers promise for high-throughput NMR structure determination.

Production of protein for nuclear magnetic resonance study using the wheat germ cell-free system

Nuclear magnetic resonance (NMR) methods have been developed to determine the three-dimensional structures of proteins, to estimate protein folding, and to discover high-affinity ligands for proteins. However, one of the difficulties encountered in the application of such NMR methods to proteins is that we should obtain milligram quantities of 15N and/or 13C-labeled pure proteins of interest. Here, we describe the method to produce proteins for NMR experiments using the improved wheat germ cell-free system, which exhibits several attractive features for high-throughput NMR study of proteins.

NMR resonance assignments for sparsely 15N labeled proteins

For larger proteins, and proteins not amenable to expression in bacterial hosts, it is difficult to deduce structures using NMR methods based on uniform (13)C, (15)N isotopic labeling and observation of just nuclear Overhauser effects (NOEs). In these cases, sparse labeling with selected (15)N enriched amino acids and extraction of a wider variety of backbone-centered structural constraints is providing an alternate approach. A limitation, however, is the absence of resonance assignment strategies that work without uniform (15)N, (13)C labeling or preparation of numerous samples labeled with pairs of isotopically labeled amino acids. In this paper an approach applicable to a single sample prepared with sparse (15)N labeling in selected amino acids is presented. It relies on correlation of amide proton exchange rates, measured from data on the intact protein and on digested and sequenced peptides. Application is illustrated using the carbohydrate binding protein, Galectin-3. Limitations and future applications are discussed.

Ultra-small-sample molecular structure detection using microslot waveguide nuclear spin resonance

We here report on the design of a planar microslot waveguide NMR probe with an induction element that can be fabricated at scales from centimeters to nanometers to allow analysis of biomolecules at nano- or picomole quantities, reducing the required amount of materials by several orders of magnitude. This device demonstrates the highest signal-to-noise ratio for a planar detector to date, measured by using the anomeric proton signal from a 15.6-nmol sample of sucrose. This probe had a linewidth of 1.1 Hz for pure water without susceptibility matching. Analysis of 1.57 nmol of ribonuclease-A shows high sensitivity in one- and two-dimensional NMR spectra. Along with reducing required sample volumes, this integrated geometry can be packed in parallel arrays and combined with microfluidic systems. Further development of this device may have broad implications not only for advancing our understanding of many intractable protein structures and their folding, molecular interactions, and dynamic behaviors, but also for high-sensitivity diagnosis of a number of protein conformational diseases.