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Archive for June, 2009

The PSI Structural Genomics Knowledge Base

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I would like to point our a unique resource for the biological NMR community, the Structural Genomics Knowledgebase (http://kb.psi-structuralgenomics.org/KB/index.jsp).

This resource is organized by Prof. Helen Berman (Director of the PDB) in collaboration with the the United States Protein Structure Initiative (PSI) and the Nature publishing group.

The KB provides comprehensive information on various aspects of structural genomics. A unique feature of the PSI-KB is the ability to blast a protein sequence against the target lists of all PSI Centers. This provides information on expression vectors, which are freely available, protocols for sample production, experimental structures, and 3D models of the target protein as well as for many homologous proteins. This is a tremendous resource for obtaining information and reagents for hypothesis-driven biology and structural biology projects.

The PSI KB also provides a portal for nominating proteins targets for structure determination by the PSI Centers: (http://kb.psi-structuralgenomics.org/KB/targetlogin.jsp) This represents a unique opportunity for biologists, or structural biologists, to obtain three-dimensional structural information on proteins they are specifically interested in.

The site also highlights recent papers from the structural biology literature, as well as papers describing recent technical advances.

Another unique feature is access to a collection of protein NMR data sets for the ongoing NMR-Critical Assessment of Protein Structure Determination Methods (NMR-CASD) project. New NMR structures are placed on hold for 8 weeks in the PDB and the corresponding chemical shift, NOESY peak list, and other data are made publicly available for testing new methods for automated NMR data analysis and/or protein structure prediction.http://kb.psi-structuralgenomics.org/KB/datasets.html

The PSI Technology Portal, linked under PSI Resources, provides descriptions of many new technologies that have been developed by the PSI project. http://kb.psi-structuralgenomics.org/KB/index1.jsp?pageshow=41

Other PSI-KB modules include Models – a data base of hundreds of thousands of homology models of protein structures, Annotation – a database of functional annotations of proteins solved in PSI project, and Metrics – a summary of metrics of success of the PSI project.

The BioMedical Themes module of the PSI-KB http://kb.psi-structuralgenomics.org/KB/index1.jsp?pageshow=85 describes the biomedical themes of the four large PSI Structure Production Centers; e.g. the primary biomedical themes of the NESG Consortium include the the network of human proteins associated with cancer biology, the several Ub and Ub-like pathways in higher eukaryotes, and the repertoires of membrane-associated lipoproteins in E coli and B subtilis, which are important antibiotic drug targets.

The PSI Knowledge Base, with links to the PSI Reagent Repository of protein expression vectors, provides a valuable resource of information and reagents that are highly enabling to protein biochemistry, cell biology, immunology, neurobiology, infectious disease, as well as proteomics and structural biology.

Gaetano Montelione

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Written by Gaetano.T. Montelione

June 23rd, 2009 at 10:43 am

Posted in Welcome

NMR applications in metabolics

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“The application of system biology to drug discovery presents a powerful tool to find out toxicity and efficacy problems prior to initiating clinical trials. As a disease state reflects a certain perturbation in the operation of a network at the cellular level, the comparison of these healthy and diseased networks identifies critical intersection points that are associated with disease markers and drug activity.”1 So how can we analyze the states of these networks? One solution is the analysis of the metabolome, a study of the complete set of small molecules along with the associated biological networks within a living cell.

The perturbation of cellular metabolism can be affected by many approaches such as the use of chemical, biological, or environmental factors. For example, the function of an enzyme may be affected by drugs or genetic mutations, measuring changes in metabolite concentrations provides direct information on changes in the cellular activity of the affected enzyme.2 Other factor that may perturb an enzyme’s activity and induce variations in the metabolome include environmental factors such as temperature, time (cell phase), pH, oxygen levels, nutrient concentration or nutrient limitations.

We use NMR to detect these metabolome perturbations by following changes in the intensities of NMR resonances resulting from metabolite concentrations fluxes in cell lysates or biofluids. There are three methods that we are commonly using to follow changes in a series of NMR spectra caused by metabolome perturbations: subtraction of an average 1D 1H NMR spectra collected between two or more different cellular states, the comparison of crosspeak intensity differences between 2D 1H TOCSY or 2D 1H-13C HSQC spectra from cell lysates, and the principal component analysis (PCA) of a collection of 1D 1H NMR spectra from cell lysates.3 The first two approaches allow us to identify the metabolites, and thus the metabolic pathways, that incur a significant change in concentration (≥ 5-fold) as a result of the environmental stimulus. Specifically, wild-type cells under normal growth conditions are compared against cells grown under various environmental conditions or mutant cell lines.

Alternatively, PCA highlights global differences and similarities between NMR spectra obtain from cells grown under these various conditions. Our differential NMR metabolomics method compares NMR spectra from cell lysates collected from wild-type and mutant cells to determine if an environmental factor or drug has the same impact on the metabolome as a genetically inactivated protein. In effect, we are monitoring the cellular mechanism of the drug. Does it demonstrate in vivo efficacy and selectivity? Does it exhibit possible toxic side-effects? In a similar manner, we can follow cellular processes and address system biology questions such as: is there a metabolic signaling pathway that controls biofilm formation?

PCA data is presented as a 2D scores plot where the coordinate axis corresponds to the principal components representing the directions of the two largest variations in the NMR data set (PC1, PC2). PCA reduces a multivariable NMR spectrum into a single point in the 2D scores plot, where similar spectra will cluster together in the plot.1 Thus, NMR spectra collected from cell lysates for a wild-type cell line will cluster distinctly from NMR spectra obtained from mutant cells because the protein target of a drug has been inactivated. The NMR spectra are different because the metabolome for the mutant cells has changed because of the loss of protein activity. Conversely, NMR spectra collected for wild-type cells treated with the drug would be expected to cluster together with NMR spectra obtained from the mutant cells if the drug is active and selective. In this case the two metabolomes would be similar because the same protein was inactivated, either genetically or chemically. Different clustering patterns in the 2D scores plot are observed for inactive drugs, non-selective binding drugs and drugs that exhibit potential toxic side-effects.

You could find out more about our differential NMR metabolomics technique and access our related publications at the Powers’ Group web site ( http://bionmr-c1.unl.edu/).

Reference

1. P. Forgue, S. Halouska, M. Werth, K. Xu, S. Harris and R. Powers (2006) “NMR Metabolic Profiling of Aspergillus nidulans to Monitor Drug and Protein Activity.”, Journal of Proteome Research, 5(8):1916-1923

2. S. Halouska, O. Chacon, R. Fenton, D. Zinniel, R. Barletta, and R. Powers (2007) “Use of NMR Metabolomics to Analyze the Targets of D-cycloserine in Mycobacteria: Role of D-Alanine Racemase.”, Journal of Proteome Research, 6(12):4608-4614.

3. M. R. Sadykov, M. E. Olson, S. Halouska, Y. Zhu, P. D. Fey, R. Powers, and G. A. Somerville (2008) “Tricarboxylic acid cycle dependent regulation of Staphylococcus epidermidis polysaccharide intercellular adhesin synthesis.” Journal of Bacteriology, 130(23):7621-7632


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Written by Robert Powers

June 23rd, 2009 at 10:38 am

Posted in NMR