PXD035159

PXD035159 is an original dataset announced via ProteomeXchange.

Title	Absolute protein quantification of Mycoplasma pneumoniae proteome by combining selected reaction monitoring of a reduced set of labeled peptides and label-free shotgun mass spectrometry.
Description	We quantified the protein abundances of Mycoplasma pneumoniae (Mpn), a genome-reduced organism, by combining selected reaction monitoring of a reduced set of labeled peptides and label-free shotgun mass spectrometry. A set of 77 labeled peptides were synthesized and used as internal standard peptides to precisely and quantitatively measure the absolute concentrations of proteins in a tandem mass spectrometer [1]. The heavy peptides were chosen based on tryptic peptides of endogenous proteins previously observed in mass spectrometry experiments and that covered more than a 1000-fold range of protein concentrations. Three time points of Mpn growth curve (48, 72 and 96 hours) and the heavy peptides were injected per triplicates in order to calculate the endogenous amount of each peptide (single reference point quantitation) [2], resulting in the absolute quantification of 73 reference proteins. A calibration curve between the absolute amount of reference proteins and the proteome-wide protein intensities allowed the estimation of absolute protein abundances for all detected proteins [3]. In order to further improve the quantification, four additional label-free mass spectrometry datasets of Mpn [4] using different protein extraction methods, from a previous project, were combined with the original dataset, which allowed to quantify the abundances of 528 proteins (75% of the proteome of Mpn). 1. Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A. 2003;100: 6940–6945. doi:10.1073/pnas.0832254100 2. Campbell J, Rezai T, Prakash A, Krastins B, Dayon L, Ward M, et al. Evaluation of absolute peptide quantitation strategies using selected reaction monitoring. Proteomics. 2011;11: 1148–1152. doi:10.1002/pmic.201000511 3. Schmidt A, Kochanowski K, Vedelaar S, Ahrné E, Volkmer B, Callipo L, et al. The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol. 2016;34: 104–110. doi:10.1038/nbt.3418 4. Miravet-Verde S, Ferrar T, Espadas-García G, Mazzolini R, Gharrab A, Sabido E, et al. Unraveling the hidden universe of small proteins in bacterial genomes. Mol Syst Biol. 2019;15: e8290. doi:10.15252/msb.20188290 5. Yus E, Maier T, Michalodimitrakis K, van Noort V, Yamada T, Chen W-H, et al. Impact of genome reduction on bacterial metabolism and its regulation. Science. 2009;326: 1263–1268. doi:10.1126/science.1177263 6. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6: 359–362. doi:10.1038/nmeth.1322 7. Picotti P, Bodenmiller B, Mueller LN, Domon B, Aebersold R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell. 2009;138: 795–806. doi:10.1016/j.cell.2009.05.051 8. Martens L, Van Damme P, Van Damme J, Staes A, Timmerman E, Ghesquière B, et al. The human platelet proteome mapped by peptide-centric proteomics: a functional protein profile. Proteomics. 2005;5: 3193–3204. doi:10.1002/pmic.200401142 9. Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, Eng J, et al. The peptideatlas project. Nucleic Acids Res. 2006;34: D655–D658. Available: https://academic.oup.com/nar/article-abstract/34/suppl_1/D655/1132687 10. Lange V, Picotti P, Domon B, Aebersold R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol. 2008;4: 222. doi:10.1038/msb.2008.61 11. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. 2010;26: 966–968. doi:10.1093/bioinformatics/btq054 12. Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. 1999;20: 3551–3567. doi:10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2 13. Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods. 2007;4: 207–214. doi:10.1038/nmeth1019
HostingRepository	PRIDE
AnnounceDate	2023-11-14
AnnouncementXML	Submission_2023-11-14_09:03:46.384.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Marc Weber
SpeciesList	scientific name: Mycoplasma pneumoniae (strain ATCC 29342 / M129); NCBI TaxID: 272634;
ModificationList	6x(13)C labeled residue; acetylated residue; monohydroxylated residue; iodoacetamide derivatized residue
Instrument	QTRAP 5500; LTQ Orbitrap Velos Pro

Revision	Datetime	Status	ChangeLog Entry
0	2022-07-07 01:19:13	ID requested
1	2023-08-31 02:24:19	announced
⏵ 2	2023-11-14 09:03:46	announced	2023-11-14: Updated project metadata.

Dataset with its publication pending

submitter keyword: Mycoplasma pneumoniae,absolute quantification, label-free, selected reaction monitoring

Luis Serrano
contact affiliation	Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology, Carrer Dr. Aiguader 88, Barcelona 08003, Spain.
contact email	luis.serrano@crg.eu
lab head
Marc Weber
contact affiliation	Centre for Genomic Regulation
contact email	marc.weber@crg.eu
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD035159
     1. Label: PRIDE project
     2. Name: Absolute protein quantification of Mycoplasma pneumoniae proteome by combining selected reaction monitoring of a reduced set of labeled peptides and label-free shotgun mass spectrometry.