Literature DB >> 16335976

Added value for tandem mass spectrometry shotgun proteomics data validation through isoelectric focusing of peptides.

Manfred Heller1, Mingliang Ye, Philippe E Michel, Patrick Morier, Daniel Stalder, Martin A Jünger, Ruedi Aebersold, Frédéric Reymond, Joël S Rossier.   

Abstract

A very popular approach in proteomics is the so-called "shotgun LC-MS/MS" strategy. In its mostly used form, a total protein digest is separated by ion exchange fractionation in the first dimension followed by off- or on-line RP LC-MS/MS. We replaced the first dimension by isoelectric focusing in the liquid phase using the Off-Gel device producing 15 fractions. As peptides are separated by their isoelectric point in the first dimension and hydrophobicity in the second, those experimentally derived parameters (pI and R(T)) can be used for the validation of potentially identified peptides. We applied this strategy to a cellular extract of Drosophila Kc167 cells and identified peptides with two different database search engines, namely PHENYX and SEQUEST, with PeptideProphet validation of the SEQUEST results. PHENYX returned 7582 potential peptide identifications and SEQUEST 7629. The SEQUEST results were reduced to 2006 identifications by validation with PeptideProphet. Validation of the PeptideProphet, SEQUEST and PHENYX results by pI and R(T) parameters confirmed 1837 PeptideProphet identifications while in the remainder of the SEQUEST results another 1130 peptides were found to be likely hits. The validation on PHENYX resulted in the fixation of a solid p-value threshold of <1 x 10(-04) that sets by itself the correct identification confidence to >95%, and a final count of 2034 highly confident peptide identifications was achieved after pI and R(T) validation. Although the PeptideProphet and PHENYX datasets have a very high confidence the overlap of common identifications was only at 79.4%, to be explained by the fact that data interpretation was done searching different protein databases with two search engines of different algorithms. The approach used in this study allowed for an automated and improved data validation process for shotgun proteomics projects producing MS/MS peptide identification results of very high confidence.

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Year:  2005        PMID: 16335976     DOI: 10.1021/pr050193v

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  18 in total

1.  Absolute quantification of the glycolytic pathway in yeast: deployment of a complete QconCAT approach.

Authors:  Kathleen M Carroll; Deborah M Simpson; Claire E Eyers; Christopher G Knight; Philip Brownridge; Warwick B Dunn; Catherine L Winder; Karin Lanthaler; Pinar Pir; Naglis Malys; Douglas B Kell; Stephen G Oliver; Simon J Gaskell; Robert J Beynon
Journal:  Mol Cell Proteomics       Date:  2011-09-19       Impact factor: 5.911

2.  A robust method for quantitative high-throughput analysis of proteomes by 18O labeling.

Authors:  Elena Bonzon-Kulichenko; Daniel Pérez-Hernández; Estefanía Núñez; Pablo Martínez-Acedo; Pedro Navarro; Marco Trevisan-Herraz; María del Carmen Ramos; Saleta Sierra; Sara Martínez-Martínez; Marisol Ruiz-Meana; Elizabeth Miró-Casas; David García-Dorado; Juan Miguel Redondo; Javier S Burgos; Jesús Vázquez
Journal:  Mol Cell Proteomics       Date:  2010-08-31       Impact factor: 5.911

3.  Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution.

Authors:  Hasmik Keshishian; Terri Addona; Michael Burgess; Eric Kuhn; Steven A Carr
Journal:  Mol Cell Proteomics       Date:  2007-10-15       Impact factor: 5.911

4.  Advances in proteomic workflows for systems biology.

Authors:  Johan Malmström; Hookeun Lee; Ruedi Aebersold
Journal:  Curr Opin Biotechnol       Date:  2007-08-14       Impact factor: 9.740

5.  Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring.

Authors:  Vinzenz Lange; Johan A Malmström; John Didion; Nichole L King; Björn P Johansson; Juliane Schäfer; Jonathan Rameseder; Chee-Hong Wong; Eric W Deutsch; Mi-Youn Brusniak; Peter Bühlmann; Lars Björck; Bruno Domon; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2008-04-13       Impact factor: 5.911

6.  Building high-quality assay libraries for targeted analysis of SWATH MS data.

Authors:  Olga T Schubert; Ludovic C Gillet; Ben C Collins; Pedro Navarro; George Rosenberger; Witold E Wolski; Henry Lam; Dario Amodei; Parag Mallick; Brendan MacLean; Ruedi Aebersold
Journal:  Nat Protoc       Date:  2015-02-12       Impact factor: 13.491

7.  IQcat: multiplexed protein quantification by isoelectric QconCAT.

Authors:  Ryan J Austin; Deborah K Chang; Carly A Holstein; Lik W Lee; Jenni Risler; Jonathan H Wang; Lee Adams; Nicolle B Krusberski; Daniel B Martin
Journal:  Proteomics       Date:  2012-06-19       Impact factor: 3.984

8.  Increasing proteome coverage with offline RP HPLC coupled to online RP nanoLC-MS.

Authors:  Emine Gokce; Genna L Andrews; Ralph A Dean; David C Muddiman
Journal:  J Chromatogr B Analyt Technol Biomed Life Sci       Date:  2011-02-04       Impact factor: 3.205

Review 9.  A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics.

Authors:  Alexey I Nesvizhskii
Journal:  J Proteomics       Date:  2010-09-08       Impact factor: 4.044

10.  Shotgun proteomic analysis of cerebrospinal fluid using off-gel electrophoresis as the first-dimension separation.

Authors:  Lashanda N Waller; Kevin Shores; Daniel R Knapp
Journal:  J Proteome Res       Date:  2008-09-09       Impact factor: 4.466

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