Dataset Information

Comparative Phosphoproteomic Analysis of G2-checkpoint Recovery

ABSTRACT: Comparative phosphoproteomic analysis of checkpoint recovery identifies new regulators of the DNA damage response. How cells recover from a DNA damage-induced arrest is currently poorly understood. We have performed large-scale quantitative phosphoproteomics to identify changes in protein phosphorylation that occur during recovery from a G2 arrest. We identified 154 proteins that were differentially phosphorylated in multiple experiments. Systematic depletion of each of these differentially phosphorylated proteins by siRNA identified at least 10 potential regulators of recovery. Data processing and bioinformatics: Raw data were converted to single DTA files using DTA SuperCharge and merged into Mascot Generic Format (MGF) files, which were searched using an in-house licensed Mascot v2.2 search engine against Human Swissprot 56.2 database concatenated with reversed sequences as decoy (containing 40656 sequences, 20328 forward sequences). Carbamidomethyl cysteine and oxidized methionines were set as fixed modifications; serine, threonine, and tyrosine phosphorylations were set as variable modifications; quantification was set with dimethyl double mode. The mass tolerance of the precursor ion was 10 ppm and 0.9 Da for fragment ions. The false discovery rate (FDR) was determined as < 1% (Mascot score threshold of 31) using the decoy database approach and the MGF files were trimmed at Mascot score threshold of 31 (1% FDR) by RockerBox. MSQuant v1.5 was used to quantitate the amounts of the identified phosphopeptides and determine the exact phosphorylation site within the peptide. Every phosphopeptide quantitation was manually validated; peptides with low signal:noise ratios, low number of MS scans, or overlapping peaks were not included for quantitative purposes. Histogram plots of the ratio of whole quantified peptides in each experiment were used to normalize the ratios. A program to extract information from MSQuant output was developed in-house to identify the position of each phosphorylation site and its status in current available databases (Phospho ELM and SwissProt). Panther Classification System was used to classify proteins with regulated phosphopeptides. NetworKIN was used to predict in vivo kinases for the phosphosites.

INSTRUMENT(S): LTQ Orbitrap, LTQ

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell, Cell Culture

SUBMITTER: Teck Yew Low

LAB HEAD: Teck Yew Low

PROVIDER: PXD000222 | Pride | 2013-04-29

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	080911_VH_PLKinh2_F10.RAW	Raw
	080911_VH_PLKinh2_F11.RAW	Raw
	080911_VH_PLKinh2_F12.RAW	Raw
	080911_VH_PLKinh2_F12b.RAW	Raw
	080911_VH_PLKinh2_F13.RAW	Raw

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Publications

Comparative phosphoproteomic analysis of checkpoint recovery identifies new regulators of the DNA damage response.

Halim Vincentius A VA Alvarez-Fernández Mónica M Xu Yan Juan YJ Aprelia Melinda M van den Toorn Henk W P HW Heck Albert J R AJ Mohammed Shabaz S Medema René H RH

Science signaling 20130423 272

How cells recover from a DNA damage-induced arrest is currently poorly understood. We performed large-scale quantitative phosphoproteomics to identify changes in protein phosphorylation that occurred during recovery from arrest in the G2 phase of the cell cycle caused by DNA damage. We identified 154 proteins that were differentially phosphorylated, and systematic depletion of each of these differentially phosphorylated proteins by small interfering RNA (siRNA) identified at least 10 potential r ...[more]

PMID: 23612710

Similar Datasets

Project description:Comparative phosphoproteomic analysis of checkpoint recovery identifies new regulators of the DNA damage response. How cells recover from a DNA damage-induced arrest is currently poorly understood. We have performed large-scale quantitative phosphoproteomics to identify changes in protein phosphorylation that occur during recovery from a G2 arrest. We identified 154 proteins that were differentially phosphorylated in multiple experiments. Systematic depletion of each of these differentially phosphorylated proteins by siRNA identified at least 10 potential regulators of recovery. Data processing and bioinformatics: Raw data were converted to single DTA files using DTA SuperCharge and merged into Mascot Generic Format (MGF) files, which were searched using an in-house licensed Mascot v2.2 search engine against Human Swissprot 56.2 database concatenated with reversed sequences as decoy (containing 40656 sequences, 20328 forward sequences). Carbamidomethyl cysteine and oxidized methionines were set as fixed modifications; serine, threonine, and tyrosine phosphorylations were set as variable modifications; quantification was set with dimethyl double mode. The mass tolerance of the precursor ion was 10 ppm and 0.9 Da for fragment ions. The false discovery rate (FDR) was determined as < 1% (Mascot score threshold of 31) using the decoy database approach and the MGF files were trimmed at Mascot score threshold of 31 (1% FDR) by RockerBox. MSQuant v1.5 was used to quantitate the amounts of the identified phosphopeptides and determine the exact phosphorylation site within the peptide. Every phosphopeptide quantitation was manually validated; peptides with low signal:noise ratios, low number of MS scans, or overlapping peaks were not included for quantitative purposes. Histogram plots of the ratio of whole quantified peptides in each experiment were used to normalize the ratios. A program to extract information from MSQuant output was developed in-house to identify the position of each phosphorylation site and its status in current available databases (Phospho ELM and SwissProt). Panther Classification System was used to classify proteins with regulated phosphopeptides. NetworKIN was used to predict in vivo kinases for the phosphosites.

Project description:1D-LC-MS/MS analysis of OGE-prefractionated and TMT labeled mouse samples consiting of 2 unstimulated and 2 stimulated T-cell samples. The acquired raw-files were converted to the mascot generic file (mgf) format using the msconvert tool (part of ProteoWizard, version 3.0.4624 (2013-6-3)). Using the MASCOT algorithm (Matrix Science, Version 2.4.0), the mgf files were searched against a decoy database containing normal and reverse sequences of the predicted SwissProt entries of mus musculus (www.ebi.ac.uk, release date 16/05/2012) and commonly observed contaminants (in total 33,832 sequences) generated using the SequenceReverser tool from the MaxQuant software (Version 1.0.13.13). The precursor ion tolerance was set to 10 ppm and fragment ion tolerance was set to 0.01 Da. The search criteria were set as follows: full tryptic specificity was required (cleavage after lysine or arginine residues unless followed by proline), 2 missed cleavages were allowed, carbamidomethylation (C), TMT6plex (K and peptide n-terminus) were set as fixed modification and oxidation (M) as a variable modification. Next, the database search results were imported to the Scaffold Q+ software (version 4.1.1, Proteome Software Inc., Portland, OR) and the protein false identification rate was set to 1% based on the number of decoy hits. Specifically, peptide identifications were accepted if they could be established at greater than 94.0% probability to achieve an FDR less than 1.0% by the scaffold local FDR algorithm. Protein identifications were accepted if they could be established at greater than 6.0% probability to achieve an FDR less than 1.0% and contained at least 1 identified peptide. Protein probabilities were assigned by the Protein Prophet program (Nesvizhskii, et al, Anal. Chem. 2003; 75(17):4646-58). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters. For quantification, acquired reporter ion intensities in the experiment were globally normalized across all acquisition runs. Individual quantitative samples were normalized within each acquisition run. Intensities for each peptide identification were normalized within the assigned protein. The reference channels were normalized to produce a 1:1 fold change. All normalization calculations were performed using medians to multiplicatively normalize data. A list of identified and quantified proteins is available in the xls file.

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