Dataset Information

QuiXoT+ project: General Statistical framework for quantitative proteomics by stable isotope labeling

ABSTRACT: Protein identification was carried out by searching against a joint Human + Yeast Swissprot database (Uniprot release 57.3 May 2009, 26885 entries), in order to increase statistical power of peptide identification, which was supplemented with porcine trypsin, and using SEQUEST algorithm (Bioworks 3.2 package, Thermo Finnigan). For SILAC, we used variable (methionine oxidation, lysine and arginine modification of +6 Da) and fixed modifications (cysteine carboxamidomethylation). Precursor mass tolerance was set to 2Da, fragment mass tolerance to 1.2Da and up to two missed cleavage sites for trypsin were allowed. The same collections of MS/MS spectra were also searched against inverted databases constructed from the same target databases. SEQUEST results were analyzed using the probability ratio method 22b, taking into account isoelectric points of peptides to improve peptide identification 9b. False discovery rates (FDR) of peptide identifications were calculated from the search results against the inverted databases using the refined method 9b. The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large datasets generated by these techniques remains a challenge since appropriate statistical standards are lacking. Here we present the first generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was similar or better than that of other existing methods. The model also provides a general framework to integrate quantitative information, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data. Except for TOF/TOF MS/MS files, protein identification was carried out by searching against a joint Human+YeastSwissprot database (Uniprot release 57.3 May 2009, 26885 entries), in order to increase statistical power of peptide identification, which was supplemented with porcine trypsin, and using SEQUEST algorithm (Bioworks 3.2 package, Thermo Finnigan). For 18O-labeled samples variable (methionine oxidation, lysine and arginine modification of +4 Da) and fixed modifications (cysteine carboxamidomethylation) were used. For iTRAQ, we allowed variable (methionine oxidation) and fixed modifications (cysteine carboxamidomethylation, lysine and N- terminal modification of +144.1020 Da). For SILAC, we used variable (methionine oxidation, lysine and arginine modification of +6 Da) and fixed modifications (cysteine carboxamidomethylation). Precursor mass tolerance was set to 2Da, fragment mass tolerance to 1.2Da and up to two missed cleavage sites for trypsin were allowed. The same collections of MS/MS spectra were also searched against inverted databases constructed from the same target databases. SEQUEST results were analyzed using the probability ratio method 22b, taking into account isoelectric points of peptides to improve peptide identification 9b. False discovery rates (FDR) of peptide identifications were calculated from the search results against the inverted databases using the refined method 9b. TOF/TOF MS/MS spectra were converted to MGF files by using the 4000 Series Explorer V.3.5.1 internal processor and the following parameters: mass range from precursor -20 Da to 60 Da, peak density of 50 peaks per 200 Da with S/N >1 and minimum area >1 with a maximum number of 100 peaks per precursor. These files were analyzed with the Mascot V2.2.06 search engine using the database and search conditions indicated above.

INSTRUMENT(S): LTQ Orbitrap, LTQ

ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Marco Trevisan-Herraz

LAB HEAD: Jesús Vázquez

PROVIDER: PXD000325 | Pride | 2015-05-11

REPOSITORIES: Pride

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Publications

General statistical framework for quantitative proteomics by stable isotope labeling.

Navarro Pedro P Trevisan-Herraz Marco M Bonzon-Kulichenko Elena E Núñez Estefanía E Martínez-Acedo Pablo P Pérez-Hernández Daniel D Pérez-Hernández Daniel D Jorge Inmaculada I Mesa Raquel R Calvo Enrique E Carrascal Montserrat M Hernáez María Luisa ML García Fernando F Bárcena José Antonio JA Ashman Keith K Abian Joaquín J Gil Concha C Redondo Juan Miguel JM Vázquez Jesús J

Journal of proteome research 20140210 3

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including (18)O, iTRAQ, and SILAC labeling, and different M ...[more]

PMID: 24512137

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Project description:Many protein activities are driven by ATP binding and hydrolysis. Here, we explore the ATP binding proteome of the model plant Arabidopsis thaliana using Acyl-ATP (AcATP) probes. These probes target ATP binding sites and covalently label lysine residues in the ATP binding pocket. Protein extracts were labelled with NHS-LC-Biotin, purified over neutravidin and eluted with formic acid. Eluted proteins were separated by 1-D SDS Gel and digested with Trypsin. Peptide mixtures were analysed by LC-MS/MS on an LTQ Velos. Spectra were searched using Mascot 2.3 using a fixed modification of cysteine (57.02 Da for carbamidomethyl), and variable modifications of methionine (15.99 Da for oxidation) and lysine (339.16 and 355.16 Da for BHAc and OBHAc labeling, respectively). A mass tolerance was set to 0.3 Da for the precursor ions and 0.4 Da for fragment ions. Up to two mis-cleavages for trypsin were permitted since the labeling would prevent cleavage at the labeled lysine. The MS2 spectra were searched against the TAIR10_pep_2012 (version November 2012) containing 35386 proteins of Arabidopsis thaliana, supplemented with a small database with 1095 artefact proteins and a reversed decoy database of the same proteins (totals 72962 entries). An oxidized version of the modifier was observed. Alternatively Arabidopsis leaf proteome was labeled with desthiobiotin-acyl ATP and digested with trypsin. Labeled peptides were purified on streptavidin matrix and sequenced by LC-MS/MS on an LTQ linear ion trap. Here the MS2 spectra were searched using SEQUEST against the TAIR9_pep_20090619 database (version June 2009) containing 32,769 proteins of Arabidopsis thaliana. MS2 searches included fixed iodoacetamide modification of cysteine (57 Da for alkylation), and variable modifications of methionine (16 Da for oxidation) and lysine (196 Da for DBAcATP labeling). Up to three mis-cleavages with trypsin were allowed and non-tryptic or half-tryptic peptides were excluded. A mass tolerance was set to 3 Da for the precursor ions and 1 Da for fragment ions.

Dataset Information

QuiXoT+ project: General Statistical framework for quantitative proteomics by stable isotope labeling

Publications

General statistical framework for quantitative proteomics by stable isotope labeling.

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