BioMagResBank (BMRB) as a partner in the Worldwide Protein Data Bank (wwPDB): new policies affecting biomolecular NMR depositions

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

Vinpocetine mitigates DMH-induce pre-neoplastic colon damage in rats through inhibition of pro-inflammatory cytokines

Colorectal cancer (CRC) is currently recognized as the third most prevalent cancer worldwide. Vinpocetine is a synthetic derivative of the vinca alkaloid vincamine. It has been found effective in ameliorating the growth and progression of cancerous cells. However, its pharmacological effect on colon damage remains elusive. Hence, in this study, we have shown the role of vinpocetine in DMH-induced colon carcinogenesis. At first, male albino Wistar rats were administered with DMH consistently for four weeks to induce pre-neoplastic colon damage. Afterward, animals were treated with vinpocetine (4.2 and 8.4 mg/kg/day p.o.) for 15 days. Serum samples were collected to assess the physiological parameters, including ELISA and NMR metabolomics. Colon from all the groups was collected and processed separately for histopathology and western blot analysis. Vinpocetine attenuated the altered plasma parameters; lipid profile and showed anti-proliferative action as evidenced by suppressed COX-2 stimulation and decreased levels of IL-1β, IL-2, IL-6, and IL-10. Vinpocetine is significantly effective in preventing CRC which may be associated with its anti-inflammatory and antioxidant potential. Accordingly, vinpocetine could serve as a potential anticancer agent for CRC treatment and thus be considered for future clinical and therapeutic research.

PDBms-An online tool for PDB file splitting and interactive molecular visualization

The rapid growth of biological databases has resulted in the vast development of many state-of-the-art molecular analysis tools for accurate disease diagnosis and drug discovery. Protein Data Bank (PDB) is a leading molecular database consisting of three-dimensional (3D) experimental structures of macromolecules and small molecules. The most significant role of PDB in Bioinformatics includes molecular modelling and computer-aided drug design (CADD). PDBms is a web tool for splitting PDB file and interactive visualization of molecules. It parses coordinate section records in the PDB file and categorizes them into a group of molecules. Moreover, it supports 3D graphic visualization in the various model for polymers and target-ligand interactions of complex structures. The web interface of PDBms is designed using NGL Viewer/WebGL JavaScript package and programming languages such as PHP, HTML, CSS, JavaScript, AJAX, and jQuery. PDBms is freely accessible at https://www.biogem.org/tool/pdbms/ .

Biological Magnetic Resonance Data Bank

The Biological Magnetic Resonance Data Bank (BMRB, https://bmrb.io) is the international open data repository for biomolecular nuclear magnetic resonance (NMR) data. Comprised of both empirical and derived data, BMRB has applications in the study of biomacromolecular structure and dynamics, biomolecular interactions, drug discovery, intrinsically disordered proteins, natural products, biomarkers, and metabolomics. Advances including GHz-class NMR instruments, national and trans-national NMR cyberinfrastructure, hybrid structural biology methods and machine learning are driving increases in the amount, type, and applications of NMR data in the biosciences. BMRB is a Core Archive and member of the World-wide Protein Data Bank (wwPDB).

Leveraging the HMBC to Facilitate Metabolite Identification

The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) and 2D 1H-1H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D 1H-13C HSQC-TOCSY and 2D 1H-13C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete 1H-1H and 1H-13C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a 13C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D 1H-13C HMBC spectra was collected for 94 common metabolites. 13C-13C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the 1H-13C HSQC-TOCSY spectra. The resulting 13C-13C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.

SMART-Miner: A convolutional neural network-based metabolite identification from 1 H-13 C HSQC spectra

The identification of metabolites from complex biofluids and extracts of tissues is an essential process for understanding metabolic profiles. Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomics studies for identification and quantification of metabolites. However, the accurate identification of individual metabolites is still a challenging process with higher peak intensity or similar chemical shifts from different metabolites. In this study, we applied a convolutional neural network (CNN) to ¹ H-¹³ C HSQC NMR spectra to achieve accurate peak identification in complex mixtures. The results reveal that the neural network was successfully trained on metabolite identification from these 2D NMR spectra and achieved very good performance compared with other NMR-based metabolomic tools.

Pleiotrophin Interaction with Synthetic Glycosaminoglycan Mimetics

Chondroitin sulfate (CS) E is the natural ligand for pleiotrophin (PTN) in the central nervous system (CNS) of the embryo. Some structures of PTN in solution have been solved, but no precise location of the binding site has been reported yet. Using ¹⁵N-labelled PTN and HSQC NMR experiments, we studied the interactions with a synthetic CS-E tetrasaccharide corresponding to the minimum binding sequence. The results agree with the data for larger GAG (glycosaminoglycans) sequences and confirm our hypothesis that a synthetic tetrasaccharide is long enough to fully interact with PTN. We hypothesize that the central region of PTN is an intrinsically disordered region (IDR) and could modify its properties upon binding. The second tetrasaccharide has two benzyl groups and shows similar effects on PTN. Finally, the last measured compound aggregated but beforehand, showed a behavior compatible with a slow exchange in the NMR time scale. We propose the same binding site and mode for the tetrasaccharides with and without benzyl groups.

Metabolomic and transcriptomic responses of ticks during recovery from cold shock reveal mechanisms of survival

Ticks are blood-feeding ectoparasites but spend most of their life off-host where they may have to tolerate low winter temperatures. Rapid cold-hardening (RCH) is a process commonly used by arthropods, including ticks, to improve survival of acute low temperature exposure. However, little is known about the underlying mechanisms in ticks associated with RCH, cold shock, and recovery from these stresses. In the present study, we investigated the extent to which RCH influences gene expression and metabolism during recovery from cold stress in Dermacentor variabilis, the American dog tick, using a combined transcriptomics and metabolomics approach. Following recovery from RCH, 1,860 genes were differentially expressed in ticks, whereas only 99 genes responded during recovery to direct cold shock. Recovery from RCH resulted in an upregulation of various pathways associated with ion binding, transport, metabolism, and cellular structures seen in the response of other arthropods to cold. The accumulation of various metabolites, including several amino acids and betaine, corresponded to transcriptional shifts in the pathways associated with these molecules, suggesting congruent metabolome and transcriptome changes. Ticks receiving exogenous betaine and valine demonstrated enhanced cold tolerance, suggesting cryoprotective effects of these metabolites. Overall, many of the responses during recovery from cold shock in ticks were similar to those observed in other arthropods, but several adjustments may be distinct from other currently examined taxa.

Metabolic signatures of Arabidopsis thaliana abiotic stress responses elucidate patterns in stress priming, acclimation, and recovery

Temperature, water, and light are three abiotic stress factors that have major influences on plant growth, development, and reproduction. Plants can be primed by a prior mild stress to enhance their resistance to future stress. We used an untargeted metabolomics approach to examine Arabidopsis thaliana 11-day-old seedling's abiotic stress responses including heat (with and without priming), cold (with and without priming), water-deficit and high-light before and after a 2-day-recovery period. Analysis of the physiological phenotypes showed that seedlings with stress treatment resulted in a reduction in fresh weight, hypocotyl and root length but remained viable. Several stress responsive metabolites were identified, confirmed with reference standards, quantified, and clustered. We identified shared and specific stress signatures for cold, heat, water-deficit, and high-light treatments. Central metabolism including amino acid metabolism, sugar metabolism, glycolysis, TCA cycle, GABA shunt, glutathione metabolism, purine metabolism, and urea cycle were found to undergo changes that are fundamentally different, although some shared commonalities in response to different treatments. Large increases in cysteine abundance and decreases in reduced glutathione were observed following multiple stress treatments highlighting the importance of oxidative stress as a general phenomenon in abiotic stress. Large fold increases in low-turnover amino acids and maltose demonstrate the critical role of protein and starch autolysis in early abiotic stress responses.

POKY software tools encapsulating assignment strategies for solution and solid-state protein NMR data

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New tools support efficient analysis of solution and solid-state NMR spectra of proteins.

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POKY integrates a powerful suite of software packages for automated assignments.

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The Versatile Assigner module validates assignments through probabilistic analysis.

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The operation of these tools is supported by on-line guidance.

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The performance of these tools is evaluated in reference to competing software.

NMR spectroscopy provides structural and functional information about biomolecules and their complexes. The complexity of these systems can make the NMR data difficult to interpret, particularly for newer users of NMR technology, who may have limited understanding of the tools available and how they are used. To alleviate this problem, we have created software based on standardized workflows for both solution and solid-state NMR spectroscopy of proteins. These tools assist with manual and automated peak picking and with chemical shift assignment and validation. They provide users with an optimized path through spectral analysis that can help them perform the necessary tasks more efficiently.

Searching for drug leads targeted to the hydrophobic cleft of dengue virus capsid protein

We synthesised and screened 18 aromatic derivatives of guanylhydrazones and oximes aromatic for their capacity to bind to dengue virus capsid protein (DENVC). The intended therapeutic target was the hydrophobic cleft of DENVC, which is a region responsible for its anchoring in lipid droplets in the infected cells. The inhibition of this process completely suppresses virus infectivity. Using NMR, we describe five compounds able to bind to the α1-α2 interface in the hydrophobic cleft. Saturation transfer difference experiments showed that the aromatic protons of the ligands are important for the interaction with DENVC. Fluorescence binding isotherms indicated that the selected compounds bind at micromolar affinities, possibly leading to binding-induced conformational changes. NMR-derived docking calculations of ligands showed that they position similarly in the hydrophobic cleft. Cytotoxicity experiments and calculations of in silico drug properties suggest that these compounds may be promising candidates in the search for antivirals targeting DENVC.

Identification of Potential Drug Targets of Broad-Spectrum Inhibitors with a Michael Acceptor Moiety Using Shotgun Proteomics

The Michael addition reaction is a spontaneous and quick chemical reaction that is widely applied in various fields. This reaction is performed by conjugating an addition of nucleophiles with α, β-unsaturated carbonyl compounds, resulting in the bond formation of C-N, C-S, C-O, and so on. In the development of molecular materials, the Michael addition is not only used to synthesize chemical compounds but is also involved in the mechanism of drug action. Several covalent drugs that bond via Michael addition are regarded as anticarcinogens and anti-inflammatory drugs. Although drug development is mainly focused on pharmaceutical drug discovery, target-based discovery can provide a different perspective for drug usage. However, considerable time and labor are required to define a molecular target through molecular biological experiments. In this review, we systematically examine the chemical structures of current FDA-approved antiviral drugs for potential Michael addition moieties with α, β-unsaturated carbonyl groups, which may exert an unidentified broad-spectrum inhibitory mechanism to target viral or host factors. We thus propose that profiling the targets of antiviral agents, such as Michael addition products, can be achieved by employing a high-throughput LC-MS approach to comprehensively analyze the interaction between drugs and targets, and the subsequent drug responses in the cellular environment to facilitate drug repurposing and/or identify potential adverse effects, with a particular emphasis on the pros and cons of this shotgun proteomic approach.

Exploring the quality of protein structural models from a Bayesian perspective

We explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. The basic premise of our approach is that the evaluation of a Bayesian statistical model's fit may reveal aspects of the quality of a structure when the fitted data is related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear regression model to experimental and theoretical ¹³ C^α chemical shifts. Then, we propose two complementary approaches for the evaluation of such fitting: (a) in terms of the expected differences between experimental and posterior predicted values; (b) in terms of the leave-one-out cross-validation point-wise predictive accuracy. Finally, we present visualizations that can help interpret these evaluations. The analyses presented in this article are aimed to aid in detecting problematic residues in protein structures. The code developed for this work is available on: https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation.

Moving beyond static snapshots: Protein dynamics and the Protein Data Bank

Proteins are the molecular machines of living systems. Their dynamics are an intrinsic part of their evolutionary selection in carrying out their biological functions. Although the dynamics are more difficult to observe than a static, average structure, we are beginning to observe these dynamics and form sound mechanistic connections between structure, dynamics, and function. This progress is highlighted in case studies from myoglobin and adenylate kinase to the ribosome and molecular motors where these molecules are being probed with a multitude of techniques across many timescales. New approaches to time-resolved crystallography are allowing simple “movies” to be taken of proteins in action, and new methods of mapping the variations in cryo-electron microscopy are emerging to reveal a more complete description of life’s machines. The results of these new methods are aided in their dissemination by continual improvements in curation and distribution by the Protein Data Bank and their partners around the world.

Recent Force Field Strategies for Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are widely distributed across eukaryotic cells, playing important roles in molecular recognition, molecular assembly, post-translational modification, and other biological processes. IDPs are also associated with many diseases such as cancers, cardiovascular diseases, and neurodegenerative diseases. Due to their structural flexibility, conventional experimental methods cannot reliably capture their heterogeneous structures. Molecular dynamics simulation becomes an important complementary tool to quantify IDP structures. This review covers recent force field strategies proposed for more accurate molecular dynamics simulations of IDPs. The strategies include adjusting dihedral parameters, adding grid-based energy correction map (CMAP) parameters, refining protein-water interactions, and others. Different force fields were found to perform well on specific observables of specific IDPs but also are limited in reproducing all available experimental observables consistently for all tested IDPs. We conclude the review with perspective areas for improvements for future force fields for IDPs.

Polycythemia Vera and Essential Thrombocythemia Patients Exhibit Unique Serum Metabolic Profiles Compared to Healthy Individuals and Secondary Thrombocytosis Patients

Simple Summary

Current diagnosis of myeloproliferative neoplasms (MPNs), including polycythemia vera (PV) and essential thrombocythemia (ET), is controversial due to limitations associated with the lack of reproducibility, subjectivity and the presence of common somatic mutations in the driver genes. Metabolomics represents a powerful approach to identify altered metabolites that can differentiate between disease status at the time of diagnosis. The objective of this study was to characterize the serum metabolic profile of MPNs patients (PV and ET) and compare it with healthy controls (HC) and secondary thrombocytosis (ST) patients. The analysis revealed metabolites following similar trends between PV and ET patients, as well as unique significant differences in the serum metabolite levels of MNPs patients compared to HC and ST patients. These results could contribute to better differentiate patients with these diseases from HC and ST patients.

Abstract

Most common myeloproliferative neoplasms (MPNs) include polycythemia vera (PV) and essential thrombocythemia (ET). Accurate diagnosis of these disorders remains a clinical challenge due to the lack of specific clinical or molecular features in some patients enabling their discrimination. Metabolomics has been shown to be a powerful tool for the discrimination between different hematological diseases through the analysis of patients’ serum metabolic profiles. In this pilot study, the potential of NMR-based metabolomics to characterize the serum metabolic profile of MPNs patients (PV, ET), as well as its comparison with the metabolic profile of healthy controls (HC) and secondary thrombocytosis (ST) patients, was assessed. The metabolic profile of PV and ET patients, compared with HC, exhibited higher levels of lysine and decreased levels of acetoacetic acid, glutamate, polyunsaturated fatty acids (PUFAs), scyllo-inositol and 3-hydroxyisobutyrate. Furthermore, ET patients, compared with HC and ST patients, were characterized by decreased levels of formate, N-acetyl signals from glycoproteins (NAC) and phenylalanine, while the serum profile of PV patients, compared with HC, showed increased concentrations of lactate, isoleucine, creatine and glucose, as well as lower levels of choline-containing metabolites. The overall analysis revealed significant metabolic alterations mainly associated with energy metabolism, the TCA cycle, along with amino acid and lipid metabolism. These results underscore the potential of metabolomics for identifying metabolic alterations in the serum of MPNs patients that could contribute to improving the clinical management of these diseases.

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.

The Protein Data Bank Archive

Protein Data Bank is the single worldwide archive of experimentally determined macromolecular structure data. Established in 1971 as the first open access data resource in biology, the PDB archive is managed by the worldwide Protein Data Bank (wwPDB) consortium which has four partners-the RCSB Protein Data Bank (RCSB PDB; rcsb.org), the Protein Data Bank Japan (PDBj; pdbj.org), the Protein Data Bank in Europe (PDBe; pdbe.org), and BioMagResBank (BMRB; www.bmrb.wisc.edu ). The PDB archive currently includes ~175,000 entries. The wwPDB has established a number of task forces and working groups that bring together experts form the community who provide recommendations on improving data standards and data validation for improving data quality and integrity. The wwPDB members continue to develop the joint deposition, biocuration, and validation system (OneDep) to improve data quality and accommodate new data from emerging techniques such as 3DEM. Each PDB entry contains coordinate model and associated metadata for all experimentally determined atomic structures, experimental data for the traditional structure determination techniques (X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy), validation reports, and additional information on quaternary structures. The wwPDB partners are committed to following the FAIR (Findability, Accessibility, Interoperability, and Reproducibility) principles and have implemented a DOI resolution mechanism that provides access to all the relevant files for a given PDB entry. On average, >250 new entries are added to the archive every week and made available by each wwPDB partner via FTP area. The wwPDB partner sites also develop data access and analysis tools and make these available via their websites. wwPDB continues to work with experts in the community to establish a federation of archives for archiving structures determined using integrative/hybrid method where multiple experimental techniques are used.

Active and prospective latent tuberculosis are associated with different metabolomic profiles: clinical potential for the identification of rapid and non-invasive biomarkers

Although 23% of world population is infected with Mycobacterium tuberculosis (M. tb), only 5-10% manifest the disease. Individuals surely exposed to M. tb that remain asymptomatic are considered potential latent TB (LTB) cases. Such asymptomatic M. tb.-exposed individuals represent a reservoir for active TB cases. Although accurate discrimination and early treatment of patients with active TB and asymptomatic M. tb.-exposed individuals are necessary to control TB, identifying those individuals at risk of developing active TB still remains a tremendous clinical challenge. This study aimed to characterize the differences in the serum metabolic profile specifically associated to active TB infected individuals or to asymptomatic M. tb.-exposed population. Interestingly, significant changes in a specific set of metabolites were shared when comparing either asymptomatic house-hold contacts of active TB patients (HHC-TB) or active TB patients (A-TB) to clinically healthy controls (HC). Furthermore, this analysis revealed statistically significant lower serum levels of aminoacids such as alanine, lysine, glutamate and glutamine, and citrate and choline in patients with A-TB, when compared to HHC-TB. The predictive ability of these metabolic changes was also evaluated. Although further validation in independent cohorts and comparison with other pulmonary infectious diseases will be necessary to assess the clinical potential, this analysis enabled the discrimination between HHC-TB and A-TB patients with an AUC value of 0.904 (confidence interval 0.81-1.00, p-value < 0.0001). Overall, the strategy described in this work could provide a sensitive, specific, and minimally invasive method that could eventually be translated into a clinical tool for TB control.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

A distal regulatory region of a class I human histone deacetylase

Histone deacetylases (HDACs) are key enzymes in epigenetics and important drug targets in cancer biology. Whilst it has been established that HDACs regulate many cellular processes, far less is known about the regulation of these enzymes themselves. Here, we show that HDAC8 is allosterically regulated by shifts in populations between exchanging states. An inactive state is identified, which is stabilised by a range of mutations and resembles a sparsely-populated state in equilibrium with active HDAC8. Computational models show that the inactive and active states differ by small changes in a regulatory region that extends up to 28 Å from the active site. The regulatory allosteric region identified here in HDAC8 corresponds to regions in other class I HDACs known to bind regulators, thus suggesting a general mechanism. The presented results pave the way for the development of allosteric HDAC inhibitors and regulators to improve the therapy for several disease states.

Phosphorus NMR and Its Application to Metabolomics

Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphorus is a naturally abundant and NMR active isotope that does not require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments, but 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorus-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provide a measure of a cell's metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N-, and 31P-labeled metabolites. The protocol includes the application of a 2D 1H-31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N-, and 31P-labeled metabolites from a single NMR sample, and a data set of one-dimensional (1D) 31P NMR and 2D 1H-31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments.

The data universe of structural biology

The Protein Data Bank (PDB) has grown from a small data resource for crystallographers to a worldwide resource serving structural biology. The history of the growth of the PDB and the role that the community has played in developing standards and policies are described. This article also illustrates how other biophysics communities are collaborating with the worldwide PDB to create a network of interoperating data resources. This network will expand the capabilities of structural biology and enable the determination and archiving of increasingly complex structures.

Spatial molecular-dynamically ordered NMR spectroscopy of intact bodies and heterogeneous systems

Noninvasive evaluation of the spatial distribution of chemical composition and diffusion behavior of materials is becoming possible by advanced nuclear magnetic resonance (NMR) pulse sequence editing. However, there is room for improvement in the spectral resolution and analytical method for application to heterogeneous samples. Here, we develop applications for comprehensively evaluating compounds and their dynamics in intact bodies and heterogeneous systems from NMR data, including spatial z-position, chemical shift, and diffusion or relaxation. This experiment is collectively named spatial molecular-dynamically ordered spectroscopy (SMOOSY). Pseudo-three-dimensional (3D) SMOOSY spectra of an intact shrimp and two heterogeneous systems are recorded to evaluate this methodology. Information about dynamics is mapped onto two-dimensional (2D) chemical shift imaging spectra using a pseudo-spectral imaging method with a processing tool named SMOOSY processor. Pseudo-2D SMOOSY spectral images can non-invasively assess the different dynamics of the compounds at each spatial z-position of the shrimp's body and two heterogeneous systems.

Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (¹H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accuracy.

Transitions in DNA polymerase β μs-ms dynamics related to substrate binding and catalysis

DNA polymerase β (pol β) plays a central role in the DNA base excision repair pathway and also serves as an important model polymerase. Dynamic characterization of pol β from methyl-TROSY 13C-1H multiple quantum CPMG relaxation dispersion experiments of Ile and Met sidechains and previous backbone relaxation dispersion measurements, reveals transitions in μs-ms dynamics in response to highly variable substrates. Recognition of a 1-nt-gapped DNA substrate is accompanied by significant backbone and sidechain motion in the lyase domain and the DNA binding subdomain of the polymerase domain, that may help to facilitate binding of the apoenzyme to the segments of the DNA upstream and downstream from the gap. Backbone μs-ms motion largely disappears after formation of the pol β-DNA complex, giving rise to an increase in uncoupled μs-ms sidechain motion throughout the enzyme. Formation of an abortive ternary complex using a non-hydrolyzable dNTP results in sidechain motions that fit to a single exchange process localized to the catalytic subdomain, suggesting that this motion may play a role in catalysis.

Modeling pH-Dependent NMR Chemical Shift Perturbations in Peptides

Modeling the pH dependence of protein and peptide chemical shifts outside the range of physiological values (6.5-7) is key to understanding structure-function relationships of these systems. These capabilities are largely not available in current chemical shift prediction software. In this study, we utilize a combination of molecular dynamics and quantum mechanics to investigate the through-space and through-bond contributions of protonation-dependent chemical shift perturbations (CSPs) in model tripeptides. By altering the protonation state of the titratable group in the tripeptides, we observe a notable difference in the conformational ensembles and attendantly compute significant CSPs for all nuclei near the site of protonation. We thus demonstrate the ability to recapitulate experimental pH-dependent CSPs with good agreement (R = 0.85, 0.99, and 0.98 for ¹³C, ¹⁵N, and ¹H, respectively). Broadly, we provide the groundwork for incorporating pH effects into empirical and semiempirical chemical shift predictors.

Hepatitis C virus sequence divergence preserves p7 viroporin structural and dynamic features

The hepatitis C virus (HCV) viroporin p7 oligomerizes to form ion channels, which are required for the assembly and secretion of infectious viruses. The 63-amino acid p7 monomer has two putative transmembrane domains connected by a cytosolic loop, and has both N- and C- termini exposed to the endoplasmic reticulum (ER) lumen. NMR studies have indicated differences between p7 structures of distantly related HCV genotypes. A critical question is whether these differences arise from the high sequence variation between the different isolates and if so, how the divergent structures can support similar biological functions. Here, we present a side-by-side characterization of p7 derived from genotype 1b (isolate J4) in the detergent 6-cyclohexyl-1-hexylphosphocholine (Cyclofos-6) and p7 derived from genotype 5a (isolate EUH1480) in n-dodecylphosphocholine (DPC). The 5a isolate p7 in conditions previously associated with a disputed oligomeric form exhibits secondary structure, dynamics, and solvent accessibility broadly like those of the monomeric 1b isolate p7. The largest differences occur at the start of the second transmembrane domain, which is destabilized in the 5a isolate. The results show a broad consensus among the p7 variants that have been studied under a range of different conditions and indicate that distantly related HCVs preserve key features of structure and dynamics.

Characterising side chains in large proteins by protonless 13C-detected NMR spectroscopy

Side chains cover protein surfaces and are fundamental to processes as diverse as substrate recognition, protein folding and enzyme catalysis. However, characterisation of side-chain motions has so far been restricted to small proteins and methyl-bearing side chains. Here we present a class of methods, based on ¹³C-detected NMR spectroscopy, to more generally quantify motions and interactions of side chains in medium-to-large proteins. A single, uniformly isotopically labelled sample is sufficient to characterise the side chains of six different amino acid types. Side-chain conformational dynamics on the millisecond time-scale can be quantified by incorporating chemical exchange saturation transfer (CEST) into the presented methods, whilst long-range ¹³C-¹³C scalar couplings reporting on nanosecond to millisecond motions can be quantified in proteins as large as 80 kDa. The presented class of methods promises characterisation of side-chain behaviour at a level that has so far been reserved for the protein backbone.

Analysis of side-chain motions by NMR has so far been restricted to small proteins and methyl-bearing side chains. Here, the authors present NMR methods based on ¹³C direct detection of highly deuterated protein samples that yield sharp and well-resolved signals and allow the characterisation of side-chain conformational dynamics of six different amino acid types in medium-to-large proteins.

Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy

Technological advances in mass spectrometry (MS), liquid chromatography (LC) separations, nuclear magnetic resonance (NMR) spectroscopy, and big data analytics have made possible studying metabolism at an "omics" or systems level. Here, we applied a multiplatform (NMR + LC-MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence. Thus far, predicting which patients will recur even after radical prostatectomy has not been possible. Correlation analysis on metabolite abundances detected on serum samples collected prior to surgery from prostate cancer patients ( n = 40 remission vs n = 40 recurrence) showed significant alterations in a number of pathways, including amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation conditions.

Identification of Unknown Metabolomics Mixture Compounds by Combining NMR, MS, and Cheminformatics

Metabolomics aims at the comprehensive identification of metabolites in complex mixtures to characterize the state of a biological system and elucidate their roles in biochemical pathways. For many biological samples, a large number of spectral features observed by NMR spectroscopy and mass spectrometry (MS) belong to unknowns, i.e., these features do not belong to metabolites that have been previously identified, and their spectral information is not available in databases. By combining NMR, MS, and combinatorial cheminformatics, the analysis of unknowns can be pursued in complex mixtures requiring minimal purification. This chapter describes the SUMMIT MS/NMR approach covering sample preparation, NMR and MS data collection and processing, and the identification of likely unknowns with the use of cheminformatics tools and the prediction of NMR spectral properties.

Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals

Various chemical shift predictive methodologies have been studied and developed, but there remains the problem of prediction accuracy. Assigning the NMR signals of metabolic mixtures requires high predictive performance owing to the complexity of the signals. Here we propose a new predictive tool that combines quantum chemistry and machine learning. A scaling factor as the objective variable to correct the errors of 2355 theoretical chemical shifts was optimized by exploring 91 machine learning algorithms and using the partial structure of 150 compounds as explanatory variables. The optimal predictive model gave RMSDs between experimental and predicted chemical shifts of 0.2177 ppm for δ 1H and 3.3261 ppm for δ 13C in the test data; thus, better accuracy was achieved compared with existing empirical and quantum chemical methods. The utility of the predictive model was demonstrated by applying it to assignments of experimental NMR signals of a complex metabolic mixture.

Chemical shift-based methods in NMR structure determination

Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.

Expanding the Coverage of the Metabolome with Nitrogen-Based NMR

Isotopically labeling a metabolite and tracing its metabolic fate has provided invaluable insights about the role of metabolism in human diseases in addition to a variety of other issues. ¹³C-labeled metabolite tracers or unlabeled ¹H-based NMR experiments are currently the most common application of NMR to metabolomics studies. Unfortunately, the coverage of the metabolome has been consequently limited to the most abundant carbon-containing metabolites. To expand the coverage of the metabolome and enhance the impact of metabolomics studies, we present a protocol for ¹⁵N-labeled metabolite tracer experiments that may also be combined with routine ¹³C tracer experiments to simultaneously detect both ¹⁵N- and ¹³C-labeled metabolites in metabolic samples. A database consisting of 2D ¹H-¹⁵N HSQC natural-abundance spectra of 50 nitrogen-containing metabolites are also presented to facilitate the assignment of ¹⁵N-labeled metabolites. The methodology is demonstrated by labeling Escherichia coli and Staphylococcus aureus metabolomes with ¹⁵N₁-ammonium chloride, ¹⁵N₄-arginine, and ¹³C₂-acetate. Efficient ¹⁵N and ¹³C metabolite labeling and identification were achieved utilizing standard cell culture and sample preparation protocols.

Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine

1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.

Structural database resources for biological macromolecules

This Briefing reviews the widely used, currently active, up-to-date databases derived from the worldwide Protein Data Bank (PDB) to facilitate browsing, finding and exploring its entries. These databases contain visualization and analysis tools tailored to specific kinds of molecules and interactions, often including also complex metrics precomputed by experts or external programs, and connections to sequence and functional annotation databases. Importantly, updates of most of these databases involves steps of curation and error checks based on specific expertise about the subject molecules or interactions, and removal of sequence redundancy, both leading to better data sets for mining studies compared with the full list of raw PDB entries. The article presents the databases in groups such as those aimed to facilitate browsing through PDB entries, their molecules and their general information, those built to link protein structure with sequence and dynamics, those specific for transmembrane proteins, nucleic acids, interactions of biomacromolecules with each other and with small molecules or metal ions, and those concerning specific structural features or specific protein families. A few webservers directly connected to active databases, and a few databases that have been discontinued but would be important to have back, are also briefly commented on. Along the Briefing, sample cases where these databases have been used to aid structural studies or advance our knowledge about biological macromolecules are referenced. A few specific examples are also given where using these databases is easier and more informative than using raw PDB data.

Mechanistic underpinnings of dehydration stress in the American dog tick revealed through RNA-Seq and metabolomics

Ticks are obligate blood feeders but spend the majority of their lifetime off-host where they must contend with a multitude of environmental stresses. Survival under desiccating conditions is a determinant for habitats where ticks can become established, and water-balance characteristics of ticks have been extensively studied. However, little is known about the underlying aspects associated with dehydration stress in ticks. In this study, we examined the response of male American dog ticks, Dermacentor variabilis, to dehydration using a combined transcriptomics and metabolomics approach. During dehydration, 497 genes were differentially expressed, including an up-regulation of stress-response and protein-catabolism genes and concurrent down-regulation of several energetically expensive biological processes. Accumulation of several metabolites, including specific amino acids, glycerol and gamma aminobutyric acid (GABA), and transcript shifts in the associated pathways for generating these metabolites indicated congruence between changes in the metabolome and gene expression. Ticks treated with exogenous glycerol and GABA demonstrated altered water-balance characteristics; specifically, increased water absorption at high relative humidity. Finally, we observed changes in locomotor activity in response to dehydration, but this change was not influenced by the accumulation of GABA. Overall, the responses to dehydration by these ticks were similar to those observed in other dehydration-tolerant arthropods, but several molecular and behavioral responses are distinct from those associated with other taxa.

Empowering pharmacoinformatics by linked life science data

With the public availability of large data sources such as ChEMBLdb and the Open PHACTS Discovery Platform, retrieval of data sets for certain protein targets of interest with consistent assay conditions is no longer a time consuming process. Especially the use of workflow engines such as KNIME or Pipeline Pilot allows complex queries and enables to simultaneously search for several targets. Data can then directly be used as input to various ligand- and structure-based studies. In this contribution, using in-house projects on P-gp inhibition, transporter selectivity, and TRPV1 modulation we outline how the incorporation of linked life science data in the daily execution of projects allowed to expand our approaches from conventional Hansch analysis to complex, integrated multilayer models.

Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive

The Protein Data Bank (PDB)--the single global repository of experimentally determined 3D structures of biological macromolecules and their complexes--was established in 1971, becoming the first open-access digital resource in the biological sciences. The PDB archive currently houses ~130,000 entries (May 2017). It is managed by the Worldwide Protein Data Bank organization (wwPDB; wwpdb.org), which includes the RCSB Protein Data Bank (RCSB PDB; rcsb.org), the Protein Data Bank Japan (PDBj; pdbj.org), the Protein Data Bank in Europe (PDBe; pdbe.org), and BioMagResBank (BMRB; www.bmrb.wisc.edu). The four wwPDB partners operate a unified global software system that enforces community-agreed data standards and supports data Deposition, Biocuration, and Validation of ~11,000 new PDB entries annually (deposit.wwpdb.org). The RCSB PDB currently acts as the archive keeper, ensuring disaster recovery of PDB data and coordinating weekly updates. wwPDB partners disseminate the same archival data from multiple FTP sites, while operating complementary websites that provide their own views of PDB data with selected value-added information and links to related data resources. At present, the PDB archives experimental data, associated metadata, and 3D-atomic level structural models derived from three well-established methods: crystallography, nuclear magnetic resonance spectroscopy (NMR), and electron microscopy (3DEM). wwPDB partners are working closely with experts in related experimental areas (small-angle scattering, chemical cross-linking/mass spectrometry, Forster energy resonance transfer or FRET, etc.) to establish a federation of data resources that will support sustainable archiving and validation of 3D structural models and experimental data derived from integrative or hybrid methods.

Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems

Although significant advances have been made in recent years, the structural elucidation of small molecules continues to remain a challenging issue for metabolite profiling. Many metabolomic studies feature unknown compounds; sometimes even in the list of features identified as "statistically significant" in the study. Such metabolic "dark matter" means that much of the potential information collected by metabolomics studies is lost. Accurate structure elucidation allows researchers to identify these compounds. This in turn, facilitates downstream metabolite pathway analysis, and a better understanding of the underlying biology of the system under investigation. This review covers a range of methods for the structural elucidation of individual compounds, including those based on gas and liquid chromatography hyphenated to mass spectrometry, single and multi-dimensional nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry and includes discussion of data standardization. Future perspectives in structure elucidation are also discussed; with a focus on the potential development of instruments and techniques, in both nuclear magnetic resonance spectroscopy and mass spectrometry that, may help solve some of the current issues that are hampering the complete identification of metabolite structure and function.

The archiving and dissemination of biological structure data

The global Protein Data Bank (PDB) was the first open-access digital archive in biology. The history and evolution of the PDB are described, together with the ways in which molecular structural biology data and information are collected, curated, validated, archived, and disseminated by the members of the Worldwide Protein Data Bank organization (wwPDB; http://wwpdb.org). Particular emphasis is placed on the role of community in establishing the standards and policies by which the PDB archive is managed day-to-day.

Revealing structural views of biology

The first protein structures were determined in the 1950s. In the decades that followed, development of new methods for sample preparation, crystallization, data collection, and structure analysis yielded tens of thousands of biomolecular structures. This short review highlights some of the major technical advances exemplified with selected structures.

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models?

Bioinformatics-Aided Venomics

Venomics is a modern approach that combines transcriptomics and proteomics to explore the toxin content of venoms. This review will give an overview of computational approaches that have been created to classify and consolidate venomics data, as well as algorithms that have helped discovery and analysis of toxin nucleic acid and protein sequences, toxin three-dimensional structures and toxin functions. Bioinformatics is used to tackle specific challenges associated with the identification and annotations of toxins. Recognizing toxin transcript sequences among second generation sequencing data cannot rely only on basic sequence similarity because toxins are highly divergent. Mass spectrometry sequencing of mature toxins is challenging because toxins can display a large number of post-translational modifications. Identifying the mature toxin region in toxin precursor sequences requires the prediction of the cleavage sites of proprotein convertases, most of which are unknown or not well characterized. Tracing the evolutionary relationships between toxins should consider specific mechanisms of rapid evolution as well as interactions between predatory animals and prey. Rapidly determining the activity of toxins is the main bottleneck in venomics discovery, but some recent bioinformatics and molecular modeling approaches give hope that accurate predictions of toxin specificity could be made in the near future.

(13)C NMR Metabolomics: INADEQUATE Network Analysis

The many advantages of (13)C NMR are often overshadowed by its intrinsically low sensitivity. Given that carbon makes up the backbone of most biologically relevant molecules, (13)C NMR offers a straightforward measurement of these compounds. Two-dimensional (13)C-(13)C correlation experiments like INADEQUATE (incredible natural abundance double quantum transfer experiment) are ideal for the structural elucidation of natural products and have great but untapped potential for metabolomics analysis. We demonstrate a new and semiautomated approach called INETA (INADEQUATE network analysis) for the untargeted analysis of INADEQUATE data sets using an in silico INADEQUATE database. We demonstrate this approach using isotopically labeled Caenorhabditis elegans mixtures.

Metabolic profiling of Commiphora wightii (guggul) reveals a potential source for pharmaceuticals and nutraceuticals

Guggul gum resin from Commiphora wightii (syn. Commiphoramukul) has been used for centuries in Ayurveda to treat a variety of ailments. The NMR and GC-MS based non-targeted metabolite profiling identified 118 chemically diverse metabolites including amino acids, fatty acids, organic acids, phenolic acids, pregnane-derivatives, steroids, sterols, sugars, sugar alcohol, terpenoids, and tocopherol from aqueous and non-aqueous extracts of leaves, stem, roots, latex and fruits of C. wightii. Out of 118, 51 structurally diverse aqueous metabolites were characterized by NMR spectroscopy. For the first time quinic acid and myo-inositol were identified as the major metabolites in C. wightii. Very high concentration of quinic acid was found in fruits (553.5 ± 39.38 mg g(-1) dry wt.) and leaves (212.9 ± 10.37 mg g(-1) dry wt.). Similarly, high concentration of myo-inositol (168.8 ± 13.84 mg g(-1) dry wt.) was observed from fruits. The other metabolites of cosmeceutical, medicinal, nutraceutical and industrial significance such as α-tocopherol, n-methylpyrrolidone (NMP), trans-farnesol, prostaglandin F2, protocatechuic, gallic and cinnamic acids were identified from non-aqueous extracts using GC-MS. These important metabolites have thus far not been reported from this plant. Isolation of a fungal endophyte, (Nigrospora sps.) from this plant is the first report. The fungal endophyte produced a substantial quantity of bostrycin and deoxybostrycin known for their antitumor properties. Very high concentrations of quinic acid and myo-inositol in leaves and fruits; a substantial quantity of α-tocopherol and NMP in leaves, trans-farnesol in fruits, bostrycin and deoxybostrycin from its endophyte makes the taxa distinct, since these metabolites with medicinal properties find immense applications as dietary supplements and nutraceuticals.

Back to the Future: Nuclear Magnetic Resonance and Bioinformatics Studies on Intrinsically Disordered Proteins

From the 1970s to the present, regions of missing electron density in protein structures determined by X-ray diffraction and the characterization of the functions of these regions have suggested that not all protein regions depend on prior 3D structure to carry out function. Motivated by these observations, in early 1996 we began to use bioinformatics approaches to study these intrinsically disordered proteins (IDPs) and IDP regions. At just about the same time, several laboratory groups began to study a collection of IDPs and IDP regions using nuclear magnetic resonance. The temporal overlap of the bioinformatics and NMR studies played a significant role in the development of our understanding of IDPs. Here the goal is to recount some of this history and to project from this experience possible directions for future work.