Quantitative proteomics:from instrument to browserNeil 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

1Can 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):5313-29.2Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes. Pratt JM, et al. Nat Protoc. (2006) 1(2):1029-43.3A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Herrgård MJ, et al. Nat Biotechnol. (2008) 26(10):1155-60.4PRIDE 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 approaches1 in which enzyme kinetic assays were performed on cell lysate to determine vmax parameters, we are following an approach in which assays are performed in vitro on known concentrations of purified enzymes to determine kcat 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 QconCAT2 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 metabolism3 is used to selectindividual pathways to be studied.

As the model is fully annotated according to the MIRIAM4

specification, enzymes of interest can be easily extracted asUniProt terms.

Data acquisition

Labelled peptides are spiked intothe sample and data acquired by LC-MSMS. Any instrument may be usedprovided that data can be exportedin a common, vendor-independentformat (e.g. mzData, mzXML).

Metadata capture with PRIDE Converter

The EBI-developed tool4 is used toallow the addition of metadata tothe data; providing information onsample conditions and instrumentacquisition parameters in thestandard PRIDE XML format.

Peptide selection with PepSelecta

Signature peptides must be foundfor each protein to be quantified.PepSelecta has been developed to Automate the process of findingsuitable signature peptides for agiven set of UniProt terms.

Model parameterisation with Taverna

A web service has been developedallowing protein concentrations tobe extracted from the PRIDE XMLdatabase. Taverna7 workflows can bewritten to query the database andparameterise the SBML model.

Data querying and browsing

A queryable web interface has beendeveloped on an XML database,allowing the identifications andquantitations, along with spectraand chromatograms to be queriedand viewed.

Identification and quantitation

The Pride Wizard5 was extendedfor QconCAT analyses. Spectra aresubmitted to Mascot6, with labelledpeptides identified and used forautomated quantitation of analytePeptides by peak area comparison.

References5An informatic pipeline for the data capture and submission of quantitative proteomic data using iTRAQ. Siepen JA, et al. Proteome Sci. (2007) 1;5:4.6Probability-based protein identification by searching sequence databases using mass spectrometry data. Perkins DN, et al. Electrophoresis. (1999) 20(18):3551-67.7Taverna: a tool for building and running workflows of services. Hull D, et al. Nucleic Acids Res. (2006) 34:W729-32.