Quantitative proteomics: from instrument to browser Neil Swainston, Daniel Jameson, Kathleen Carroll, Catherine Winder, Pe...
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Quantitative Proteomics: From Instrument To Browser

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Quantitative Proteomics: From Instrument To Browser

  1. 1. Quantitative proteomics: from instrument to browser Neil Swainston, Daniel Jameson, Kathleen Carroll, Catherine Winder, Pedro Mendes Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester M1 7ND, UK This work has been supported by the BBSRC/EPSRC grant: the Manchester Centre for Integrative Systems Biology 1 Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Teusink B, et al. Eur J Biochem. (2000) 267 (17):5313-29. 2 Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes. Pratt JM, et al. Nat Protoc. (2006) 1 (2):1029-43. 3 A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Herrgård MJ, et al. Nat Biotechnol. (2008) 26 (10):1155-60. 4 PRIDE Converter: making proteomics data-sharing easy. Barsnes H, et al. Nat Biotechnol. (2009) 27 (7):598-9. Introduction The Manchester Centre for Integrative Systems Biology is following a bottom-up systems biology approach to develop a quantitative, kinetic model of yeast metabolism. In contrast to previous approaches 1 in which enzyme kinetic assays were performed on cell lysate to determine v max parameters, we are following an approach in which assays are performed in vitro on known concentrations of purified enzymes to determine k cat values. By combining this approach with absolute protein concentrations , we separate kinetic parameters from concentration variables , allowing us to determine the influence of isoenzymes and fluctuating enzyme concentrations on the system (such as those caused by gene expression). Determination of absolute enzyme concentrations is performed using LC-MS and the QconCAT 2 approach, in which known concentrations of labelled signature peptides are spiked into the sample, allowing absolute quantitation to be performed by determination of relative peak intensities. An informatics workflow has been developed to support the full cycle of work from labelled peptide selection, to identification, quantitation and ultimately data browsing and model parameterisation. Modelling A genome-scale model of yeast metabolism 3 is used to select individual pathways to be studied. As the model is fully annotated according to the MIRIAM 4 specification, enzymes of interest can be easily extracted as UniProt terms. Data acquisition Labelled peptides are spiked into the sample and data acquired by LC- MSMS. Any instrument may be used provided that data can be exported in a common, vendor-independent format (e.g. mzData, mzXML). Metadata capture with PRIDE Converter The EBI-developed tool 4 is used to allow the addition of metadata to the data; providing information on sample conditions and instrument acquisition parameters in the standard PRIDE XML format. Peptide selection with PepSelecta Signature peptides must be found for each protein to be quantified. PepSelecta has been developed to Automate the process of finding suitable signature peptides for a given set of UniProt terms. Model parameterisation with Taverna A web service has been developed allowing protein concentrations to be extracted from the PRIDE XML database. Taverna 7 workflows can be written to query the database and parameterise the SBML model. Data querying and browsing A queryable web interface has been developed on an XML database, allowing the identifications and quantitations, along with spectra and chromatograms to be queried and viewed. Identification and quantitation The Pride Wizard 5 was extended for QconCAT analyses. Spectra are submitted to Mascot 6 , with labelled peptides identified and used for automated quantitation of analyte Peptides by peak area comparison. References 5 An informatic pipeline for the data capture and submission of quantitative proteomic data using iTRAQ. Siepen JA, et al. Proteome Sci. (2007) 1 ;5:4. 6 Probability-based protein identification by searching sequence databases using mass spectrometry data. Perkins DN, et al. Electrophoresis. (1999) 20 (18):3551-67. 7 Taverna: a tool for building and running workflows of services. Hull D, et al. Nucleic Acids Res. (2006) 34 :W729-32.

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