BioMagResBank

Chapter 3: Small molecules and disease

“Big” molecules such as proteins and genes still continue to capture the imagination of most biologists, biochemists and bioinformaticians. “Small” molecules, on the other hand, are the molecules that most biologists, biochemists and bioinformaticians prefer to ignore. However, it is becoming increasingly apparent that small molecules such as amino acids, lipids and sugars play a far more important role in all aspects of disease etiology and disease treatment than we realized. This particular chapter focuses on an emerging field of bioinformatics called “chemical bioinformatics” – a discipline that has evolved to help address the blended chemical and molecular biological needs of toxicogenomics, pharmacogenomics, metabolomics and systems biology. In the following pages we will cover several topics related to chemical bioinformatics. First, a brief overview of some of the most important or useful chemical bioinformatic resources will be given. Second, a more detailed overview will be given on those particular resources that allow researchers to connect small molecules to diseases. This section will focus on describing a number of recently developed databases or knowledgebases that explicitly relate small molecules – either as the treatment, symptom or cause – to disease. Finally a short discussion will be provided on newly emerging software tools that exploit these databases as a means to discover new biomarkers or even new treatments for disease.

High-resolution homonuclear 2D NMR of carbon-13 enriched metabolites and their mixtures

High-resolution 2D (13)C-(13)C NMR correlation spectra of uniformly (13)C-labeled molecules in solution are obtained by homonuclear (13)C-decoupling along both dimensions by the application of indirect covariance NMR to constant-time NMR spectra. The spectra are optimally suited for chemical structure elucidation and molecular identification of the components of complex mixtures, such as ones from uniformly (13)C-labeled cell cultures.

The RCSB Protein Data Bank: new resources for research and education

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) develops tools and resources that provide a structural view of biology for research and education. The RCSB PDB web site (http://www.rcsb.org) uses the curated 3D macromolecular data contained in the PDB archive to offer unique methods to access, report and visualize data. Recent activities have focused on improving methods for simple and complex searches of PDB data, creating specialized access to chemical component data and providing domain-based structural alignments. New educational resources are offered at the PDB-101 educational view of the main web site such as Author Profiles that display a researcher’s PDB entries in a timeline. To promote different kinds of access to the RCSB PDB, Web Services have been expanded, and an RCSB PDB Mobile application for the iPhone/iPad has been released. These improvements enable new opportunities for analyzing and understanding structure data.

HMDB 3.0--The Human Metabolome Database in 2013

The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40 000 (a 600% increase). This enormous expansion is a result of the inclusion of both ‘detected’ metabolites (those with measured concentrations or experimental confirmation of their existence) and ‘expected’ metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18 September 2012.).

Biomarkers of whale shark health: a metabolomic approach

In a search for biomarkers of health in whale sharks and as exploration of metabolomics as a modern tool for understanding animal physiology, the metabolite composition of serum in six whale sharks (Rhincodon typus) from an aquarium collection was explored using (1)H nuclear magnetic resonance (NMR) spectroscopy and direct analysis in real time (DART) mass spectrometry (MS). Principal components analysis (PCA) of spectral data showed that individual animals could be resolved based on the metabolite composition of their serum and that two unhealthy individuals could be discriminated from the remaining healthy animals. The major difference between healthy and unhealthy individuals was the concentration of homarine, here reported for the first time in an elasmobranch, which was present at substantially lower concentrations in unhealthy whale sharks, suggesting that this metabolite may be a useful biomarker of health status in this species. The function(s) of homarine in sharks remain uncertain but it likely plays a significant role as an osmolyte. The presence of trimethylamine oxide (TMAO), another well-known protective osmolyte of elasmobranchs, at 0.1-0.3 mol L(-1) was also confirmed using both NMR and MS. Twenty-three additional potential biomarkers were identified based on significant differences in the frequency of their occurrence between samples from healthy and unhealthy animals, as detected by DART MS. Overall, NMR and MS provided complementary data that showed that metabolomics is a useful approach for biomarker prospecting in poorly studied species like elasmobranchs.

Novel conformation of an RNA structural switch

The RNA duplex, (5′GACGAGUGUCA)₂, has two conformations in equilibrium. The nuclear magnetic resonance solution structure reveals that the major conformation of the loop, 5′GAGU/3′UGAG, is novel and contains two unusual Watson–Crick/Hoogsteen GG pairs with G residues in the syn conformation, two A residues stacked on each other in the center of the helix with inverted sugars, and two bulged-out U residues. The structure provides a benchmark for testing approaches for predicting local RNA structure and a sequence that allows the design of a unique arrangement of functional groups and/or a conformational switch into nucleic acids.

Metabonomic analysis of water extracts from Chinese and American ginsengs by 1H nuclear magnetic resonance: identification of chemical profile for quality control

Background

With the gaining popularity of commercially prepared decoctions of herbal medicines on the market, an objective and efficient way to reveal the authenticity of such products is urgently needed. Previous attempts to use chromatographic or spectroscopic methods to identify ginseng samples made use of components derived from methanol extracts of the herb. It was not established that these herbs can be distinguished solely from consumable components, which are responsible for the clinical efficacy of the herb.

In this study, metabonomics, or metabolic profiling, based on the application of ¹H-Nuclear Magnetic Resonance (NMR), is applied to distinguish the water extracts of three closely related ginseng species: P. ginseng (from two different cultivated regions in China), P. notoginseng and P. quinquefolius.

Methods

A water extraction protocol that mimics how ginseng decoctions are made for consumption was used to prepare triplicate samples from each herb for analysis. High-resolution ¹H NMR spectroscopy was used to acquire metabolic profiles of the four ginseng samples. The spectral data were subjected to multivariate and univariate analysis to identify metabolites that were able to distinguish different types of ginseng.

Results

H NMR metabolic profiling was performed to distinguish the water extracts of P. ginseng cultivated in Hebei and Jilin of China, both of which were distinguished from extracts of P. notoginseng and P. quinquefolius, by unsupervised principle component analysis based on the entire ¹H NMR spectral fingerprint Statistically significant differences were found for several discriminating features traced to common metabolites and the ginsenosides Rg1 and Rd, in the ¹H NMR spectra.

Conclusion

This study demonstrated that ¹H NMR metabonomics can simultaneously distinguish different ginseng species and multiple samples of the same species that were cultivated in different regions. This technique is applicable to the authentication and quality control of ginseng products.

An assignment of intrinsically disordered regions of proteins based on NMR structures

Intrinsically disordered proteins (IDPs) do not adopt stable three-dimensional structures in physiological conditions, yet these proteins play crucial roles in biological phenomena. In most cases, intrinsic disorder manifests itself in segments or domains of an IDP, called intrinsically disordered regions (IDRs), but fully disordered IDPs also exist. Although IDRs can be detected as missing residues in protein structures determined by X-ray crystallography, no protocol has been developed to identify IDRs from structures obtained by Nuclear Magnetic Resonance (NMR). Here, we propose a computational method to assign IDRs based on NMR structures. We compared missing residues of X-ray structures with residue-wise deviations of NMR structures for identical proteins, and derived a threshold deviation that gives the best correlation of ordered and disordered regions of both structures. The obtained threshold of 3.2Å was applied to proteins whose structures were only determined by NMR, and the resulting IDRs were analyzed and compared to those of X-ray structures with no NMR counterpart in terms of sequence length, IDR fraction, protein function, cellular location, and amino acid composition, all of which suggest distinct characteristics. The structural knowledge of IDPs is still inadequate compared with that of structured proteins. Our method can collect and utilize IDRs from structures determined by NMR, potentially enhancing the understanding of IDPs.

Specific 12CβD(2)12CγD(2)S13CεHD(2) isotopomer labeling of methionine to characterize protein dynamics by 1H and 13C NMR relaxation dispersion

Protein dynamics on the micro- to millisecond time scale is increasingly found to be critical for biological function, as demonstrated by numerous NMR relaxation dispersion studies. Methyl groups are excellent probes of protein interactions and dynamics because of their favorable NMR relaxation properties, which lead to sharp signals in the ¹H and ¹³C NMR spectra. Out of the six different methyl-bearing amino acid residue types in proteins, methionine plays a special role because of its extensive side-chain flexibility and the high polarizability of the sulfur atom. Methionine is over-represented in many protein–protein recognition sites, making the methyl group of this residue type an important probe of the relationships among dynamics, interactions, and biological function. Here we present a straightforward method to label methionine residues with specific ¹³CHD₂ methyl isotopomers against a deuterated background. The resulting protein samples yield NMR spectra with improved sensitivity due to the essentially 100% population of the desired ¹³CHD₂ methyl isotopomer, which is ideal for ¹H and ¹³C spin relaxation experiments to investigate protein dynamics in general and conformational exchange in particular. We demonstrate the approach by measuring ¹H and ¹³C CPMG relaxation dispersion for the nine methionines in calcium-free calmodulin (apo-CaM). The results show that the C-terminal domain, but not the N-terminal domain, of apo-CaM undergoes fast exchange between the ground state and a high-energy state. Since target proteins are known to bind specifically to the C-terminal domain of apo-CaM, we speculate that the high-energy state might be involved in target binding through conformational selection.

Structural interactions between lipids, water and S1-S4 voltage-sensing domains

Membrane proteins serve crucial signaling and transport functions, yet relatively little is known about their structures in membrane environments or how lipids interact with these proteins. For voltage-activated ion channels, X-ray structures suggest that the mobile voltage-sensing S4 helix would be exposed to the membrane, and functional studies reveal that lipid modification can profoundly alter channel activity. Here, we use solid-state NMR to investigate structural interactions of lipids and water with S1-S4 voltage-sensing domains and to explore whether lipids influence the structure of the protein. Our results demonstrate that S1-S4 domains exhibit extensive interactions with lipids and that these domains are heavily hydrated when embedded in a membrane. We also find evidence for preferential interactions of anionic lipids with S1-S4 domains and that these interactions have lifetimes on the timescale of ≤ 10(-3)s. Arg residues within S1-S4 domains are well hydrated and are positioned in close proximity to lipids, exhibiting local interactions with both lipid headgroups and acyl chains. Comparative studies with a positively charged lipid lacking a phosphodiester group reveal that this lipid modification has only modest effects on the structure and hydration of S1-S4 domains. Taken together, our results demonstrate that Arg residues in S1-S4 voltage-sensing domains reside in close proximity to the hydrophobic interior of the membrane yet are well hydrated, a requirement for carrying charge and driving protein motions in response to changes in membrane voltage.

Therapeutic potential of conopeptides

Conopeptides from the venoms of marine snails have attracted much interest as leads in drug design. Currently, one drug, Prialt(®), is on the market as a treatment for chronic neuropathic pain. Conopeptides target a range of ion channels, receptors and transporters, and are typically small, relatively stable peptides that are generally amenable to production using solid-phase peptide synthesis. With only a small fraction of the predicted diversity of conopeptides examined so far, these peptides represent an exciting and largely untapped resource for drug discovery. Recent efforts at chemically re-engineering conopeptides to improve their biopharmaceutical properties promise to accelerate the translation of these fascinating marine peptides to the clinic.

Creating a community resource for protein science

In addition to being one of the early pioneers in protein crystallography, Carl Brändén made significant contributions to science education with his elegant and beautifully illustrated book Introduction to Protein Structure (Brändén and Tooze, New York: Garland, 1991). It is truly an honor to receive this award in their names. This award and the 40th anniversary of the Protein Data Bank (PDB; Berman et al., Structure 2012;20:391-396) have given me an opportunity to reflect on the various components that have contributed to building a resource for protein science and to try to quantify the impact of having PDB data openly available.

PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles

The combination of the wide availability of protein backbone and side-chain NMR chemical shifts with advances in understanding of their relationship to protein structure makes these parameters useful for the assessment of structural-dynamic protein models. A new chemical shift predictor (PPM) is introduced, which is solely based on physical-chemical contributions to the chemical shifts for both the protein backbone and methyl-bearing amino-acid side chains. To explicitly account for the effects of protein dynamics on chemical shifts, PPM was directly refined against 100 ns long molecular dynamics (MD) simulations of 35 proteins with known experimental NMR chemical shifts. It is found that the prediction of methyl-proton chemical shifts by PPM from MD ensembles is improved over other methods, while backbone Cα, Cβ, C', N, and H(N) chemical shifts are predicted at an accuracy comparable to the latest generation of chemical shift prediction programs. PPM is particularly suitable for the rapid evaluation of large protein conformational ensembles on their consistency with experimental NMR data and the possible improvement of protein force fields from chemical shifts.

MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data

MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw experimental data and associated metadata, maintained by one of the major open-access data providers in molecular biology. Metabolomic profiling is an important tool for research into biological functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of experimental methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their reference spectra as well as their biological roles, locations, concentrations and raw data from metabolic experiments. Studies automatically receive a stable unique accession number that can be used as a publication reference (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.

ECMDB: the E. coli Metabolome Database

The Escherichia coli Metabolome Database (ECMDB, http://www.ecmdb.ca) is a comprehensively annotated metabolomic database containing detailed information about the metabolome of E. coli (K-12). Modelled closely on the Human and Yeast Metabolome Databases, the ECMDB contains >2600 metabolites with links to ∼1500 different genes and proteins, including enzymes and transporters. The information in the ECMDB has been collected from dozens of textbooks, journal articles and electronic databases. Each metabolite entry in the ECMDB contains an average of 75 separate data fields, including comprehensive compound descriptions, names and synonyms, chemical taxonomy, compound structural and physicochemical data, bacterial growth conditions and substrates, reactions, pathway information, enzyme data, gene/protein sequence data and numerous hyperlinks to images, references and other public databases. The ECMDB also includes an extensive collection of intracellular metabolite concentration data compiled from our own work as well as other published metabolomic studies. This information is further supplemented with thousands of fully assigned reference nuclear magnetic resonance and mass spectrometry spectra obtained from pure E. coli metabolites that we (and others) have collected. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of E. coli’s importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers but also to molecular biologists, systems biologists and individuals in the biotechnology industry.

Solution structure of the cold-shock-like protein from Rickettsia rickettsii

The solution structure of the cold-shock-like protein from R. rickettsii, the causative agent of Rocky Mountain spotted fever, is reported.

Rocky Mountain spotted fever is caused by Rickettsia rickettsii infection. R. rickettsii can be transmitted to mammals, including humans, through the bite of an infected hard-bodied tick of the family Ixodidae. Since the R. rickettsii genome contains only one cold-shock-like protein and given the essential nature of cold-shock proteins in other bacteria, the structure of the cold-shock-like protein from R. rickettsii was investigated. With the exception of a short α-helix found between β-strands 3 and 4, the solution structure of the R. rickettsii cold-shock-like protein has the typical Greek-key five-stranded β-barrel structure found in most cold-shock domains. Additionally, the R. rickettsii cold-shock-like protein, with a ΔG of unfolding of 18.4 kJ mol⁻¹, has a similar stability when compared with other bacterial cold-shock proteins.

Toward the Quantum Chemical Calculation of NMR Chemical Shifts of Proteins. 3. Conformational Sampling and Explicit Solvents Model

Fragment-based quantum chemical calculations are able to accurately calculate NMR chemical shifts even for very large molecules like proteins. But even with systematic optimization of the level of theory and basis sets as well as the use of implicit solvents models, some nuclei like polar protons and nitrogens suffer from poor predictions. Two properties of the real system, strongly influencing the experimental chemical shifts but almost always neglected in the calculations, will be discussed here in great detail: (1) conformational averaging and (2) interactions with first-shell solvent molecules. Classical molecular dynamics simulations in explicit water were carried out for obtaining a representative ensemble including the arrangement of neighboring solvent molecules, which was then subjected to quantum chemical calculations. We could demonstrate with the small test system N-methyl acetamide (NMA) that the calculated chemical shifts show immense variations of up to 6 ppm and 50 ppm for protons and nitrogens, respectively, depending on the snapshot taken from a classical molecular dynamics simulation. Applying the same approach to the HA2 domain of the influenza virus glycoprotein hemagglutinin, a 32-amino-acid-long polypeptide, and comparing averaged values to the experiment, chemical shifts of nonpolar protons and carbon atoms in proteins were calculated with unprecedented accuracy. Additionally, the mean absolute error could be reduced by a factor of 2.43 for polar protons, and reasonable correlations were obtained for nitrogen and carbonyl carbon in contrast to all other studies published so far.

TOCCATA: a customized carbon total correlation spectroscopy NMR metabolomics database

A customized metabolomics NMR database, TOCCATA, is introduced, which uses (13)C chemical shift information for the reliable identification of metabolites, their spin systems, and isomeric states. TOCCATA, whose information was derived from the BMRB and HMDB databases and the literature, currently contains 463 compounds and 801 spin systems, and it can be used through a publicly accessible web server. TOCCATA allows the identification of metabolites in the submillimolar concentration range from (13)C-(13)C total correlation spectroscopy experiments of complex mixtures, which is demonstrated for an Escherichia coli cell lysate, a carbohydrate mixture, and an amino acid mixture, all of which were uniformly (13)C-labeled.

Anisotropy of the Coulomb interaction between folded proteins: consequences for mesoscopic aggregation of lysozyme

Toward quantitative description of protein aggregation, we develop a computationally efficient method to evaluate the potential of mean force between two folded protein molecules that allows for complete sampling of their mutual orientation. Our model is valid at moderate ionic strengths and accounts for the actual charge distribution on the surface of the molecules, the dielectric discontinuity at the protein-solvent interface, and the possibility of protonation or deprotonation of surface residues induced by the electric field due to the other protein molecule. We apply the model to the protein lysozyme, whose solutions exhibit both mesoscopic clusters of protein-rich liquid and liquid-liquid separation; the former requires that protein form complexes with typical lifetimes of approximately milliseconds. We find the electrostatic repulsion is typically lower than the prediction of the Derjaguin-Landau-Verwey-Overbeek theory. The Coulomb interaction in the lowest-energy docking configuration is nonrepulsive, despite the high positive charge on the molecules. Typical docking configurations barely involve protonation or deprotonation of surface residues. The obtained potential of mean force between folded lysozyme molecules is consistent with the location of the liquid-liquid coexistence, but produces dimers that are too short-lived for clusters to exist, suggesting lysozyme undergoes conformational changes during cluster formation.

PACSY, a relational database management system for protein structure and chemical shift analysis

PACSY (Protein structure And Chemical Shift NMR spectroscopY) is a relational database management system that integrates information from the Protein Data Bank, the Biological Magnetic Resonance Data Bank, and the Structural Classification of Proteins database. PACSY provides three-dimensional coordinates and chemical shifts of atoms along with derived information such as torsion angles, solvent accessible surface areas, and hydrophobicity scales. PACSY consists of six relational table types linked to one another for coherence by key identification numbers. Database queries are enabled by advanced search functions supported by an RDBMS server such as MySQL or PostgreSQL. PACSY enables users to search for combinations of information from different database sources in support of their research. Two software packages, PACSY Maker for database creation and PACSY Analyzer for database analysis, are available from http://pacsy.nmrfam.wisc.edu.

Defensive spiroketals from Asceles glaber (Phasmatodea): absolute configuration and effects on ants and mosquitoes

Insects are the largest and most diverse group of organisms on earth, with over 1,000,000 species identified to date. Stick insects ("walkingsticks" or "phasmids", Order Phasmatodea) are known for and name-derived from their camouflage that acts as a primary line of defense from predation. However, many species also possess a potent chemical defense spray. Recently we discovered that the spray of Asceles glaber contains spiroketals [a confirmed major component: (2S,6R)-(-)(E)-2-methyl-1,7-dioxaspiro[5.5]undecane, and a tentatively identified minor component: 2-ethyl-1,6-dioxaspiro[4.5]decane] and glucose. In this paper, we: 1) illustrate the identification of spiroketals and glucose in the defense spray of A. glaber by using Nuclear Magnetic Resonance (NMR), Gas Chromatography/Mass Spectrometry (GC/MS), and comparison with a synthetic reference sample; 2) provide the elucidation of the absolute configuration of the major spiroketal in that defense spray; and 3) demonstrate the effect of this compound and its enantiomer on both fire ants (Solenopsis invicta) and mosquitoes (Aedes aegypti).

An RNA Mapping DataBase for curating RNA structure mapping experiments

SUMMARY

We have established an RNA mapping database (RMDB) to enable structural, thermodynamic and kinetic comparisons across single-nucleotide-resolution RNA structure mapping experiments. The volume of structure mapping data has greatly increased since the development of high-throughput sequencing techniques, accelerated software pipelines and large-scale mutagenesis. For scientists wishing to infer relationships between RNA sequence/structure and these mapping data, there is a need for a database that is curated, tagged with error estimates and interfaced with tools for sharing, visualization, search and meta-analysis. Through its on-line front-end, the RMDB allows users to explore single-nucleotide-resolution mapping data in heat-map, bar-graph and colored secondary structure graphics; to leverage these data to generate secondary structure hypotheses; and to download the data in standardized and computer-friendly files, including the RDAT and community-consensus SNRNASM formats. At the time of writing, the database houses 53 entries, describing more than 2848 experiments of 1098 RNA constructs in several solution conditions and is growing rapidly.

AVAILABILITY

Freely available on the web at http://rmdb.stanford.edu.

CONTACT

rhiju@stanford.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Online.

Sample preparation of Mycobacterium tuberculosis extracts for nuclear magnetic resonance metabolomic studies

Mycobacterium tuberculosis is a major cause of mortality in human beings on a global scale. The emergence of both multi- (MDR) and extensively-(XDR) drug-resistant strains threatens to derail current disease control efforts. Thus, there is an urgent need to develop drugs and vaccines that are more effective than those currently available. The genome of M. tuberculosis has been known for more than 10 years, yet there are important gaps in our knowledge of gene function and essentiality. Many studies have since used gene expression analysis at both the transcriptomic and proteomic levels to determine the effects of drugs, oxidants, and growth conditions on the global patterns of gene expression. Ultimately, the final response of these changes is reflected in the metabolic composition of the bacterium including a few thousand small molecular weight chemicals. Comparing the metabolic profiles of wild type and mutant strains, either untreated or treated with a particular drug, can effectively allow target identification and may lead to the development of novel inhibitors with anti-tubercular activity. Likewise, the effects of two or more conditions on the metabolome can also be assessed. Nuclear magnetic resonance (NMR) is a powerful technology that is used to identify and quantify metabolic intermediates. In this protocol, procedures for the preparation of M. tuberculosis cell extracts for NMR metabolomic analysis are described. Cell cultures are grown under appropriate conditions and required Biosafety Level 3 containment, harvested, and subjected to mechanical lysis while maintaining cold temperatures to maximize preservation of metabolites. Cell lysates are recovered, filtered sterilized, and stored at ultra-low temperatures. Aliquots from these cell extracts are plated on Middlebrook 7H9 agar for colony-forming units to verify absence of viable cells. Upon two months of incubation at 37 °C, if no viable colonies are observed, samples are removed from the containment facility for downstream processing. Extracts are lyophilized, resuspended in deuterated buffer and injected in the NMR instrument, capturing spectroscopic data that is then subjected to statistical analysis. The procedures described can be applied for both one-dimensional (1D) H NMR and two-dimensional (2D) H-(13)C NMR analyses. This methodology provides more reliable small molecular weight metabolite identification and more reliable and sensitive quantitative analyses of cell extract metabolic compositions than chromatographic methods. Variations of the procedure described following the cell lysis step can also be adapted for parallel proteomic analysis.

Application of NMR metabolomics to search for human disease biomarkers

Since antiquity, humans have used body fluids like saliva, urine and sweat for the diagnosis of diseases. The amount, color and smell of body fluids are still used in many traditional medical practices to evaluate an illness and make a diagnosis. The development and application of analytical methods for the detailed analysis of body fluids has led to the discovery of numerous disease biomarkers. Recently, mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The goal of these efforts is to identify metabolites that are uniquely correlated with a specific human disease in order to accurately diagnose and treat the malady. In this review we will discuss recent developments in sample preparation, experimental techniques, the identification and quantification of metabolites, and the chemometric tools used to search for biomarkers of human diseases using NMR.

TSAR: a program for automatic resonance assignment using 2D cross-sections of high dimensionality, high-resolution spectra

While NMR studies of proteins typically aim at structure, dynamics or interactions, resonance assignments represent in almost all cases the initial step of the analysis. With increasing complexity of the NMR spectra, for example due to decreasing extent of ordered structure, this task often becomes both difficult and time-consuming, and the recording of high-dimensional data with high-resolution may be essential. Random sampling of the evolution time space, combined with sparse multidimensional Fourier transform (SMFT), allows for efficient recording of very high dimensional spectra (≥4 dimensions) while maintaining high resolution. However, the nature of this data demands for automation of the assignment process. Here we present the program TSAR (Tool for SMFT-based Assignment of Resonances), which exploits all advantages of SMFT input. Moreover, its flexibility allows to process data from any type of experiments that provide sequential connectivities. The algorithm was tested on several protein samples, including a disordered 81-residue fragment of the δ subunit of RNA polymerase from Bacillus subtilis containing various repetitive sequences. For our test examples, TSAR achieves a high percentage of assigned residues without any erroneous assignments.

Influence of ¹H chemical shift assignments of the interface residues on structure determinations of homodimeric proteins

Homodimeric proteins pose a difficulty for NMR structure determination because the degeneracy of the chemical shifts in the two identical monomers implies an ambiguity in all assignments of distance restraints. For homodimeric proteins, residues involved in the interface between two monomers provide essential intermolecular NOEs. The structure determination of homodimeric proteins hence relies strongly on chemical shift assignments of these interface residues. Our paper discusses the influence of the extent of (1)H chemical shift assignments of interface residues on the structure determinations of homodimeric proteins using the CYANA program. The results reveal that successful structure determinations of homodimeric proteins with automated NOE assignment depend on the percentage of assigned interface residues and that a high completeness of around 80-90% of the (1)H chemical shift assignment in the interface is needed for reliable NMR structure determinations of homodimeric proteins for which no experimental distinction between intra- and intermolecular NOEs, e.g. by filtered NOESY experiments, is available. Our results also show that RMSD and target function values are insufficient to judge the quality of homodimeric structures determined using automated NOE assignment. Structure determinations of homodimeric proteins by NMR using conventional NOESY experiments are thus possible but more challenging than for monomeric proteins.

Automated protein backbone assignment using the projection-decomposition approach

Spectral projection experiments by NMR in conjunction with decomposition analysis have been previously introduced for the backbone assignment of proteins; various pulse sequences as well as the behaviour with low signal-to-noise or chemical shift degeneracy have been illustrated. As a guide for routine applications of this combined tool, we provide here a systematic analysis on different types of proteins using welldefined run-time parameters. As a second result of this study, the backbone assignment module SHABBA was extensively rewritten and improved. Calculations on ubiquitin yielded again fully correct and nearly complete backbone and CHβ assignments. For the 128 residue long azurin, missing assignments mostly affect Hα and Hβ. Among the remaining backbone (plus Cβ) nuclei 97.5 % could be assigned with 1.0 % differences to a reference. Finally, the new SHABBA algorithm was applied to projections recorded for a yeast histone protein domain at room temperature, where the protein is subject to partial unfolding: this leads to unobservable resonances (about a dozen missing signals in a normal 15N-HSQC) and extensive degeneracy among the resonances. From the clearly observable residues, 97.5 % of the backbone and CHβresonances could be assigned, of which only 0.8 % showed differences to published shifts. An additional study on the protein MMP20, which exhibits spectral difficulties to an even larger extent, explores the limitations of the approach.

13C NMR reveals no evidence of n-π* interactions in proteins

An n = π* interaction between neighboring carbonyl groups has been postulated to stabilize protein structures. Such an interaction would affect the (13)C chemical shielding of the carbonyl groups, whose paramagnetic component is dominated by n = π* and π = π* excitations. Model compound calculations indicate that both the interaction energetics and the chemical shielding of the carbonyl group are instead dominated by a classical dipole-dipole interaction. A set of high-resolution protein structures with associated carbonyl (13)C chemical shift assignments verifies this correlation and provides no evidence for an inter-carbonyl n = π* interaction.

An automated system designed for large scale NMR data deposition and annotation: application to over 600 assigned chemical shift data entries to the BioMagResBank from the Riken Structural Genomics/Proteomics Initiative internal database

Biomolecular NMR chemical shift data are key information for the functional analysis of biomolecules and the development of new techniques for NMR studies utilizing chemical shift statistical information. Structural genomics projects are major contributors to the accumulation of protein chemical shift information. The management of the large quantities of NMR data generated by each project in a local database and the transfer of the data to the public databases are still formidable tasks because of the complicated nature of NMR data. Here we report an automated and efficient system developed for the deposition and annotation of a large number of data sets including (1)H, (13)C and (15)N resonance assignments used for the structure determination of proteins. We have demonstrated the feasibility of our system by applying it to over 600 entries from the internal database generated by the RIKEN Structural Genomics/Proteomics Initiative (RSGI) to the public database, BioMagResBank (BMRB). We have assessed the quality of the deposited chemical shifts by comparing them with those predicted from the PDB coordinate entry for the corresponding protein. The same comparison for other matched BMRB/PDB entries deposited from 2001-2011 has been carried out and the results suggest that the RSGI entries greatly improved the quality of the BMRB database. Since the entries include chemical shifts acquired under strikingly similar experimental conditions, these NMR data can be expected to be a promising resource to improve current technologies as well as to develop new NMR methods for protein studies.

A new algorithm for reliable and general NMR resonance assignment

The new FLYA automated resonance assignment algorithm determines NMR chemical shift assignments on the basis of peak lists from any combination of multidimensional through-bond or through-space NMR experiments for proteins. Backbone and side-chain assignments can be determined. All experimental data are used simultaneously, thereby exploiting optimally the redundancy present in the input peak lists and circumventing potential pitfalls of assignment strategies in which results obtained in a given step remain fixed input data for subsequent steps. Instead of prescribing a specific assignment strategy, the FLYA resonance assignment algorithm requires only experimental peak lists and the primary structure of the protein, from which the peaks expected in a given spectrum can be generated by applying a set of rules, defined in a straightforward way by specifying through-bond or through-space magnetization transfer pathways. The algorithm determines the resonance assignment by finding an optimal mapping between the set of expected peaks that are assigned by definition but have unknown positions and the set of measured peaks in the input peak lists that are initially unassigned but have a known position in the spectrum. Using peak lists obtained by purely automated peak picking from the experimental spectra of three proteins, FLYA assigned correctly 96-99% of the backbone and 90-91% of all resonances that could be assigned manually. Systematic studies quantified the impact of various factors on the assignment accuracy, namely the extent of missing real peaks and the amount of additional artifact peaks in the input peak lists, as well as the accuracy of the peak positions. Comparing the resonance assignments from FLYA with those obtained from two other existing algorithms showed that using identical experimental input data these other algorithms yielded significantly (40-142%) more erroneous assignments than FLYA. The FLYA resonance assignment algorithm thus has the reliability and flexibility to replace most manual and semi-automatic assignment procedures for NMR studies of proteins.

Conformational selection and substrate binding regulate the monomer/dimer equilibrium of the C-terminal domain of Escherichia coli enzyme I

The bacterial phosphotransferase system (PTS) is a signal transduction pathway that couples phosphoryl transfer to active sugar transport across the cell membrane. The PTS is initiated by the binding of phosphoenolpyruvate (PEP) to the C-terminal domain (EIC) of enzyme I (EI), a highly conserved protein that is common to all sugar branches of the PTS. EIC exists in a dynamic monomer/dimer equilibrium that is modulated by ligand binding and is thought to regulate the overall PTS. Isolation of EIC has proven challenging, and conformational dynamics within the EIC domain during the catalytic cycle are still largely unknown. Here, we present a robust protocol for expression and purification of recombinant EIC from Escherichia coli and show that isolated EIC is capable of hydrolyzing PEP. NMR analysis and residual dipolar coupling measurements indicate that the isolated EIC domain in solution adopts a stable tertiary fold and quaternary structure that is consistent with previously reported crystallographic data. NMR relaxation dispersion measurements indicate that residues around the PEP binding site and in the β3α3 turn (residues 333-366), which is located at the dimer interface, undergo a rapid transition on the sub-millisecond time scale (with an exchange rate constant of ∼1500 s(-1)) between major open (∼97%) and minor closed (∼3%) conformations. Upon PEP binding, the β3α3 turn is effectively locked in the closed state by the formation of salt bridges between the phosphate group of PEP and the side chains of Lys(340) and Arg(358), thereby stabilizing the dimer.

NMR structure of lipoprotein YxeF from Bacillus subtilis reveals a calycin fold and distant homology with the lipocalin Blc from Escherichia coli

The soluble monomeric domain of lipoprotein YxeF from the Gram positive bacterium B. subtilis was selected by the Northeast Structural Genomics Consortium (NESG) as a target of a biomedical theme project focusing on the structure determination of the soluble domains of bacterial lipoproteins. The solution NMR structure of YxeF reveals a calycin fold and distant homology with the lipocalin Blc from the Gram-negative bacterium E.coli. In particular, the characteristic β-barrel, which is open to the solvent at one end, is extremely well conserved in YxeF with respect to Blc. The identification of YxeF as the first lipocalin homologue occurring in a Gram-positive bacterium suggests that lipocalins emerged before the evolutionary divergence of Gram positive and Gram negative bacteria. Since YxeF is devoid of the α-helix that packs in all lipocalins with known structure against the β-barrel to form a second hydrophobic core, we propose to introduce a new lipocalin sub-family named ‘slim lipocalins’, with YxeF and the other members of Pfam family PF11631 to which YxeF belongs constituting the first representatives. The results presented here exemplify the impact of structural genomics to enhance our understanding of biology and to generate new biological hypotheses.

Analysis of bacterial biofilms using NMR-based metabolomics

Infectious diseases can be difficult to cure, especially if the pathogen forms a biofilm. After decades of extensive research into the morphology, physiology and genomics of biofilm formation, attention has recently been directed toward the analysis of the cellular metabolome in order to understand the transformation of a planktonic cell to a biofilm. Metabolomics can play an invaluable role in enhancing our understanding of the underlying biological processes related to the structure, formation and antibiotic resistance of biofilms. A systematic view of metabolic pathways or processes responsible for regulating this 'social structure' of microorganisms may provide critical insights into biofilm-related drug resistance and lead to novel treatments. This review will discuss the development of NMR-based metabolomics as a technology to study medically relevant biofilms. Recent advancements from case studies reviewed in this manuscript have shown the potential of metabolomics to shed light on numerous biological problems related to biofilms.

Direct observation of phenylalanine orientations in statherin bound to hydroxyapatite surfaces

Extracellular biomineralization proteins such as salivary statherin control the growth of hydroxyapatite (HAP), the principal component of teeth and bones. Despite the important role that statherin plays in the regulation of hard tissue formation in humans, the surface recognition mechanisms involved are poorly understood. The protein-surface interaction likely involves very specific contacts between the surface atoms and the key protein side chains. This study demonstrates for the first time the power of combining near-edge X-ray absorption fine structure (NEXAFS) spectroscopy with element labeling to quantify the orientation of individual side chains. In this work, the 15 amino acid N-terminal binding domain of statherin has been adsorbed onto HAP surfaces, and the orientations of phenylalanine rings F7 and F14 have been determined using NEXAFS analysis and fluorine labels at individual phenylalanine sites. The NEXAFS-derived phenylalanine tilt angles have been verified with sum frequency generation spectroscopy.

Carbon backbone topology of the metabolome of a cell

The complex metabolic makeup of a biological system, such as a cell, is a key determinant of its biological state providing unique insights into its function. Here we characterize the metabolome of a cell by a novel homonuclear (13)C 2D NMR approach applied to a nonfractionated uniformly (13)C-enriched lysate of E. coli cells and determine de novo their carbon backbone topologies that constitute the "topolome". A protocol was developed, which first identifies traces in a constant-time (13)C-(13)C TOCSY NMR spectrum that are unique for individual mixture components and then assembles for each trace the corresponding carbon-bond topology network by consensus clustering. This led to the determination of 112 topologies of unique metabolites from a single sample. The topolome is dominated by carbon topologies of carbohydrates (34.8%) and amino acids (45.5%) that can constitute building blocks of more complex structures.

NMR-based metabolic profiling of rice wines by F(2)-selective total correlation spectra

In this study, we performed NMR-based metabolic profiling of major rice wines (Japanese sake, Chinese Shaoxing wine, and Korean makgeolli). In the (1)H NMR spectra, the rice wines showed broad resonances in the region of about 7.9-9.0 ppm. These resonances showed many and complex correlations with approximately 0.5-4.5 ppm in the F(2)-selective TOCSY (total correlation spectroscopy) spectra, and these correlations were attributed mainly to peptides. These spectral patterns were characteristic of individual rice wines, and the combination of F(2)-selective TOCSY spectra and principal component analysis enabled us to classify the rice wine species. Furthermore, it also provided information about raw materials, namely, what type of koji (rice koji or wheat koji) was used. These spectra may be useful as a new "fingerprint" for quality control or food authentication.

NMR metabolomics of MTLn3E breast cancer cells identifies a role for CXCR4 in lipid and choline regulation

The alpha chemokine receptor CXCR4 is up-regulated in certain types of breast cancer. Truncation of the C-terminus of this receptor alters cell morphology and increases invasiveness and metastatic potential. Here, to better understand the effects of CXCR4 expression and truncation in breast cancer cells, we have used high resolution magic angle spinning (HR-MAS) NMR studies of rat breast carcinoma MtLn3E cells to characterize the metabolite complement of cells heterologously expressing human CXCR4 or its C-terminal truncation mutant, Δ34-CXCR4. Notable reductions in choline levels were detected when either cells expressing wild-type CXCR4 or Δ34-CXCR4 were compared with cells containing an empty expression vector. Cells expressing CXCR4-Δ34 had reduced lipid content when compared with either the wild-type CXCR4 expressing cells or those containing the empty expression vector. Taken together, our results show that distinct effects on the metabolite complement can be linked to either CXCR4 expression or CXCR4 regulation. The metabolite markers for these two effects identified in the present study can, in turn, be used to further investigate the role of CXCR4 in metastasis.

Use of 1H and 31P HRMAS to evaluate the relationship between quantitative alterations in metabolite concentrations and tissue features in human brain tumour biopsies

Quantitative multinuclear high-resolution magic angle spinning was performed in order to determine the tissue pH values of and the absolute metabolite concentrations in 33 samples of human brain tumour tissue. Metabolite concentrations were quantified by 1D (1)H and (31)P HRMAS using the electronic reference to in vivo concentrations (ERETIC) synthetic signal. (1)H-(1)H homonuclear and (1)H-(31)P heteronuclear correlation experiments enabled the direct assessment of the (1)H-(31)P spin systems for signals that suffered from overlapping in the 1D (1)H spectra, and linked the information present in the 1D (1)H and (31)P spectra. Afterwards, the main histological features were determined, and high heterogeneity in the tumour content, necrotic content and nonaffected tissue content was observed. The metabolite profiles obtained by HRMAS showed characteristics typical of tumour tissues: rather low levels of energetic molecules and increased concentrations of protective metabolites. Nevertheless, these characteristics were more strongly correlated with the total amount of living tissue than with the tumour cell contents of the samples alone, which could indicate that the sampling conditions make a significant contribution aside from the effect of tumour development in vivo. The use of methylene diphosphonic acid as a chemical shift and concentration reference for the (31)P HRMAS spectra of tissues presented important drawbacks due to its interaction with the tissue. Moreover, the pH data obtained from (31)P HRMAS enabled us to establish a correlation between the pH and the distance between the N(CH(3))(3) signals of phosphocholine and choline in (1)H spectra of the tissue in these tumour samples.

Are Protein Force Fields Getting Better? A Systematic Benchmark on 524 Diverse NMR Measurements

Recent hardware and software advances have enabled simulation studies of protein systems on biophysically-relevant timescales, often revealing the need for improved force fields. Although early force field development was limited by the lack of direct comparisons between simulation and experiment, recent work from several labs has demonstrated direct calculation of NMR observables from protein simulations. Here we quantitatively evaluate recent molecular dynamics force fields against a suite of 524 chemical shift and J coupling ((3)JH(N)H(α), (3)JH(N)C(β), (3)JH(α)C', (3)JH(N)C', and (3)JH(α)N) measurements on dipeptides, tripeptides, tetra-alanine, and ubiquitin. Of the force fields examined (ff96, ff99, ff03, ff03*, ff03w, ff99sb*, ff99sb-ildn, ff99sb-ildn-phi, ff99sb-ildn-nmr, CHARMM27, OPLS-AA), two force fields (ff99sb-ildn-phi, ff99sb-ildn-nmr) combining recent side chain and backbone torsion modifications achieve high accuracy in our benchmark. For the two optimal force fields, the calculation error is comparable to the uncertainty in the experimental comparison. This observation suggests that extracting additional force field improvements from NMR data may require increased accuracy in J coupling and chemical shift prediction. To further investigate the limitations of current force fields, we also consider conformational populations of dipeptides, which were recently estimated using vibrational spectroscopy.

Concentration of metabolites from low-density planktonic communities for environmental metabolomics using nuclear magnetic resonance spectroscopy

Environmental metabolomics is an emerging field that is promoting new understanding in how organisms respond to and interact with the environment and each other at the biochemical level. Nuclear magnetic resonance (NMR) spectroscopy is one of several technologies, including gas chromatography-mass spectrometry (GC-MS), with considerable promise for such studies. Advantages of NMR are that it is suitable for untargeted analyses, provides structural information and spectra can be queried in quantitative and statistical manners against recently available databases of individual metabolite spectra. In addition, NMR spectral data can be combined with data from other omics levels (e.g. transcriptomics, genomics) to provide a more comprehensive understanding of the physiological responses of taxa to each other and the environment. However, NMR is less sensitive than other metabolomic techniques, making it difficult to apply to natural microbial systems where sample populations can be low-density and metabolite concentrations low compared to metabolites from well-defined and readily extractable sources such as whole tissues, biofluids or cell-cultures. Consequently, the few direct environmental metabolomic studies of microbes performed to date have been limited to culture-based or easily defined high-density ecosystems such as host-symbiont systems, constructed co-cultures or manipulations of the gut environment where stable isotope labeling can be additionally used to enhance NMR signals. Methods that facilitate the concentration and collection of environmental metabolites at concentrations suitable for NMR are lacking. Since recent attention has been given to the environmental metabolomics of organisms within the aquatic environment, where much of the energy and material flow is mediated by the planktonic community, we have developed a method for the concentration and extraction of whole-community metabolites from planktonic microbial systems by filtration. Commercially available hydrophilic poly-1,1-difluoroethene (PVDF) filters are specially treated to completely remove extractables, which can otherwise appear as contaminants in subsequent analyses. These treated filters are then used to filter environmental or experimental samples of interest. Filters containing the wet sample material are lyophilized and aqueous-soluble metabolites are extracted directly for conventional NMR spectroscopy using a standardized potassium phosphate extraction buffer. Data derived from these methods can be analyzed statistically to identify meaningful patterns, or integrated with other omics levels for comprehensive understanding of community and ecosystem function.

RNA-PAIRS: RNA probabilistic assignment of imino resonance shifts

The significant biological role of RNA has further highlighted the need for improving the accuracy, efficiency and the reach of methods for investigating RNA structure and function. Nuclear magnetic resonance (NMR) spectroscopy is vital to furthering the goals of RNA structural biology because of its distinctive capabilities. However, the dispersion pattern in the NMR spectra of RNA makes automated resonance assignment, a key step in NMR investigation of biomolecules, remarkably challenging. Herein we present RNA Probabilistic Assignment of Imino Resonance Shifts (RNA-PAIRS), a method for the automated assignment of RNA imino resonances with synchronized verification and correction of predicted secondary structure. RNA-PAIRS represents an advance in modeling the assignment paradigm because it seeds the probabilistic network for assignment with experimental NMR data, and predicted RNA secondary structure, simultaneously and from the start. Subsequently, RNA-PAIRS sets in motion a dynamic network that reverberates between predictions and experimental evidence in order to reconcile and rectify resonance assignments and secondary structure information. The procedure is halted when assignments and base-parings are deemed to be most consistent with observed crosspeaks. The current implementation of RNA-PAIRS uses an initial peak list derived from proton-nitrogen heteronuclear multiple quantum correlation ((1)H-(15)N 2D HMQC) and proton-proton nuclear Overhauser enhancement spectroscopy ((1)H-(1)H 2D NOESY) experiments. We have evaluated the performance of RNA-PAIRS by using it to analyze NMR datasets from 26 previously studied RNAs, including a 111-nucleotide complex. For moderately sized RNA molecules, and over a range of comparatively complex structural motifs, the average assignment accuracy exceeds 90%, while the average base pair prediction accuracy exceeded 93%. RNA-PAIRS yielded accurate assignments and base pairings consistent with imino resonances for a majority of the NMR resonances, even when the initial predictions are only modestly accurate. RNA-PAIRS is available as a public web-server at http://pine.nmrfam.wisc.edu/RNA/.

Structural characterisation of a histone domain by projection-decomposition

We demonstrate that two projection experiments, a (15)N-HSQC-NOESY-(15)N-HSQC and a (13)C-HSQC-NOESY-(15)N-HSQC, recorded for a histone domain from yeast, contain enough information to support a structural characterisation of the protein. At the temperature used, 298 K, the histone domain exhibits a very high extent of chemical shift degeneracy that is uncharacteristic for a fully folded domain. Nonetheless, a structured core of 67 residues, which is formed by three α-helices and a two-stranded β-sheet is defined by this NOESY data; this core structure was shown earlier to be present at lower temperature. The above two experiments, which together required 18 h of instrument time, are part of a set of five projection experiments acquired during 2.5 days with the goal of complete characterisation of proteins, including full resonance assignment and structure.

Assignments of backbone ¹H, ¹³C and ¹⁵N resonances in H-Ras (1-166) complexed with GppNHp at physiological pH

The small GTPase Ras is an important signaling molecule acting as a molecular switch in eukaryotic cells. Recent findings of global conformational exchange and a putative allosteric binding site in the G domain of Ras opened an avenue to understanding novel aspects of Ras function. To facilitate detailed NMR studies of Ras in physiological solution conditions, we performed backbone resonance assignments of Ras bound to slowly hydrolysable GTP mimic, guanosine 5'-[ß, γ-imido]triphosphate at pH 7.2. Out of 163 non-proline residues of the G domain, signals from backbone amide proton, nitrogen and carbon spins of 127 residues were confidently assigned with the remaining unassigned residues mostly located at the exchange-broadened effectors interface.

Resolving nitrogen-15 and proton chemical shifts for mobile segments of elastin with two-dimensional NMR spectroscopy

In this study, one- and two-dimensional NMR experiments are applied to uniformly (15)N-enriched synthetic elastin, a recombinant human tropoelastin that has been cross-linked to form an elastic hydrogel. Hydrated elastin is characterized by large segments that undergo "liquid-like" motions that limit the efficiency of cross-polarization. The refocused insensitive nuclei enhanced by polarization transfer experiment is used to target these extensive, mobile regions of this protein. Numerous peaks are detected in the backbone amide region of the protein, and their chemical shifts indicate the completely unstructured, "random coil" model for elastin is unlikely. Instead, more evidence is gathered that supports a characteristic ensemble of conformations in this rubber-like protein.

Implementing an X-ray validation pipeline for the Protein Data Bank

There is an increasing realisation that the quality of the biomacromolecular structures deposited in the Protein Data Bank (PDB) archive needs to be assessed critically using established and powerful validation methods. The Worldwide Protein Data Bank (wwPDB) organization has convened several Validation Task Forces (VTFs) to advise on the methods and standards that should be used to validate all of the entries already in the PDB as well as all structures that will be deposited in the future. The recommendations of the X-ray VTF are currently being implemented in a software pipeline. Here, ongoing work on this pipeline is briefly described as well as ways in which validation-related information could be presented to users of structural data.