1H, 13C and 15N assignment of the C-terminal domain of GNA2132 from Neisseria meningitidis

GNA2132 (Genome-derived Neisseria Antigen 2132) is a surface-exposed lipoprotein discovered by reverse vaccinology and expressed by genetically diverse Neisseria meningitidis strains (Pizza et al. 2000). The protein induces bactericidal antibodies against most strains of Meningococccus and has been included in a multivalent recombinant vaccine against N. meningitidis serogroup B. Structure determination of GNA2132 is important for understanding the antigenic properties of the protein in view of increased efficiency vaccine development. We report practically complete 1H, 13C and 15N assignment of the detectable spectrum of a highly conserved C-terminal region of GNA2132 (residues 245–427) in micellar solution, a medium used to improve the spectral quality. The first 32 residues of our construct up to residue 277 were not visible in the spectrum, presumably because of line broadening due to solvent and/or conformational exchange. Secondary structure predictions based on chemical shift information indicate the presence of an all β-protein with eight β strands

In-cell NMR characterization of the secondary structure populations of a disordered conformation of α-Synuclein within E. coli cells

α-Synuclein is a small protein strongly implicated in the pathogenesis of Parkinson’s disease and related neurodegenerative disorders. We report here the use of in-cell NMR spectroscopy to observe directly the structure and dynamics of this protein within E. coli cells. To improve the accuracy in the measurement of backbone chemical shifts within crowded in-cell NMR spectra, we have developed a deconvolution method to reduce inhomogeneous line broadening within cellular samples. The resulting chemical shift values were then used to evaluate the distribution of secondary structure populations which, in the absence of stable tertiary contacts, are a most effective way to describe the conformational fluctuations of disordered proteins. The results indicate that, at least within the bacterial cytosol, α-synuclein populates a highly dynamic state that, despite the highly crowded environment, has the same characteristics as the disordered monomeric form observed in aqueous solution

NMR Studies of the C-Terminus of alpha4 Reveal Possible Mechanism of Its Interaction with MID1 and Protein Phosphatase 2A

Alpha4 is a regulatory subunit of the protein phosphatase family of enzymes and plays an essential role in regulating the catalytic subunit of PP2A (PP2Ac) within the rapamycin-sensitive signaling pathway. Alpha4 also interacts with MID1, a microtubule-associated ubiquitin E3 ligase that appears to regulate the function of PP2A. The C-terminal region of alpha4 plays a key role in the binding interaction of PP2Ac and MID1. Here we report on the solution structure of a 45-amino acid region derived from the C-terminus of alpha4 (alpha45) that binds tightly to MID1. In aqueous solution, alpha45 has properties of an intrinsically unstructured peptide although chemical shift index and dihedral angle estimation based on chemical shifts of backbone atoms indicate the presence of a transient α-helix. Alpha45 adopts a helix-turn-helix HEAT-like structure in 1% SDS micelles, which may mimic a negatively charged surface for which alpha45 could bind. Alpha45 binds tightly to the Bbox1 domain of MID1 in aqueous solution and adopts a structure consistent with the helix-turn-helix structure observed in 1% SDS. The structure of alpha45 reveals two distinct surfaces, one that can interact with a negatively charged surface, which is present on PP2A, and one that interacts with the Bbox1 domain of MID1

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results

Griseofulvin stabilizes microtubule dynamics, activates p53 and inhibits the proliferation of MCF-7 cells synergistically with vinblastine

<p>Abstract</p> <p>Background</p> <p>Griseofulvin, an antifungal drug, has recently been shown to inhibit proliferation of various types of cancer cells and to inhibit tumor growth in athymic mice. Due to its low toxicity, griseofulvin has drawn considerable attention for its potential use in cancer chemotherapy. This work aims to understand how griseofulvin suppresses microtubule dynamics in living cells and sought to elucidate the antimitotic and antiproliferative action of the drug.</p> <p>Methods</p> <p>The effects of griseofulvin on the dynamics of individual microtubules in live MCF-7 cells were measured by confocal microscopy. Immunofluorescence microscopy, western blotting and flow cytometry were used to analyze the effects of griseofulvin on spindle microtubule organization, cell cycle progression and apoptosis. Further, interactions of purified tubulin with griseofulvin were studied <it>in vitro </it>by spectrophotometry and spectrofluorimetry. Docking analysis was performed using autodock4 and LigandFit module of Discovery Studio 2.1.</p> <p>Results</p> <p>Griseofulvin strongly suppressed the dynamic instability of individual microtubules in live MCF-7 cells by reducing the rate and extent of the growing and shortening phases. At or near half-maximal proliferation inhibitory concentration, griseofulvin dampened the dynamicity of microtubules in MCF-7 cells without significantly disrupting the microtubule network. Griseofulvin-induced mitotic arrest was associated with several mitotic abnormalities like misaligned chromosomes, multipolar spindles, misegregated chromosomes resulting in cells containing fragmented nuclei. These fragmented nuclei were found to contain increased concentration of p53. Using both computational and experimental approaches, we provided evidence suggesting that griseofulvin binds to tubulin in two different sites; one site overlaps with the paclitaxel binding site while the second site is located at the αβ intra-dimer interface. In combination studies, griseofulvin and vinblastine were found to exert synergistic effects against MCF-7 cell proliferation.</p> <p>Conclusions</p> <p>The study provided evidence suggesting that griseofulvin shares its binding site in tubulin with paclitaxel and kinetically suppresses microtubule dynamics in a similar manner. The results revealed the antimitotic mechanism of action of griseofulvin and provided evidence suggesting that griseofulvin alone and/or in combination with vinblastine may have promising role in breast cancer chemotherapy.</p

Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided

The Effect of a ΔK280 Mutation on the Unfolded State of a Microtubule-Binding Repeat in Tau

Tau is a natively unfolded protein that forms intracellular aggregates in the brains of patients with Alzheimer's disease. To decipher the mechanism underlying the formation of tau aggregates, we developed a novel approach for constructing models of natively unfolded proteins. The method, energy-minima mapping and weighting (EMW), samples local energy minima of subsequences within a natively unfolded protein and then constructs ensembles from these energetically favorable conformations that are consistent with a given set of experimental data. A unique feature of the method is that it does not strive to generate a single ensemble that represents the unfolded state. Instead we construct a number of candidate ensembles, each of which agrees with a given set of experimental constraints, and focus our analysis on local structural features that are present in all of the independently generated ensembles. Using EMW we generated ensembles that are consistent with chemical shift measurements obtained on tau constructs. Thirty models were constructed for the second microtubule binding repeat (MTBR2) in wild-type (WT) tau and a ΔK280 mutant, which is found in some forms of frontotemporal dementia. By focusing on structural features that are preserved across all ensembles, we find that the aggregation-initiating sequence, PHF6*, prefers an extended conformation in both the WT and ΔK280 sequences. In addition, we find that residue K280 can adopt a loop/turn conformation in WT MTBR2 and that deletion of this residue, which can adopt nonextended states, leads to an increase in locally extended conformations near the C-terminus of PHF6*. As an increased preference for extended states near the C-terminus of PHF6* may facilitate the propagation of β-structure downstream from PHF6*, these results explain how a deletion at position 280 can promote the formation of tau aggregates

The N-Terminal residues 43 to 60 form the interface for dopamine mediated α-synuclein dimerisation

α-synuclein (α-syn) is a major component of the intracellular inclusions called Lewy bodies, which are a key pathological feature in the brains of Parkinson's disease patients. The neurotransmitter dopamine (DA) inhibits the fibrillisation of α-syn into amyloid, and promotes α-syn aggregation into SDS-stable soluble oligomers. While this inhibition of amyloid formation requires the oxidation of both DA and the methionines in α-syn, the molecular basis for these processes is still unclear. This study sought to define the protein sequences required for the generation of oligomers. We tested N- (α-syn residues 43-140) and C-terminally (1-95) truncated α-syn, and found that similar to full-length protein both truncated species formed soluble DA: α-syn oligomers, albeit 1-95 had a different profile. Using nuclear magnetic resonance (NMR), and the N-terminally truncated α-syn 43-140 protein, we analysed the structural characteristics of the DA:α-syn 43-140 dimer and α-syn 43-140 monomer and found the dimerisation interface encompassed residues 43 to 60. Narrowing the interface to this small region will help define the mechanism by which DA mediates the formation of SDS-stable soluble DA:α-syn oligomers