Comparison of alternative MS/MS and bioinformatics approaches for confident phosphorylation site localization.

Over the past years, phosphoproteomics has advanced to a prime tool in signaling research. Since then, an enormous amount of information about in vivo protein phosphorylation events has been collected providing a treasure trove for gaining a better understanding of the molecular processes involved in cell signaling. Yet, we still face the problem of how to achieve correct modification site localization. Here we use alternative fragmentation and different bioinformatics approaches for the identification and confident localization of phosphorylation sites. Phosphopeptide-enriched fractions were analyzed by multistage activation, collision-induced dissociation and electron transfer dissociation (ETD), yielding complementary phosphopeptide identifications. We further found that MASCOT, OMSSA and Andromeda each identified a distinct set of phosphopeptides allowing the number of site assignments to be increased. The postsearch engine SLoMo provided confident phosphorylation site localization, whereas different versions of PTM-Score integrated in MaxQuant differed in performance. Based on high-resolution ETD and higher collisional dissociation (HCD) data sets from a large synthetic peptide and phosphopeptide reference library reported by Marx et al. [Nat. Biotechnol. 2013, 31 (6), 557-564], we show that an Andromeda/PTM-Score probability of 1 is required to provide an false localization rate (FLR) of 1% for HCD data, while 0.55 is sufficient for high-resolution ETD spectra. Additional analyses of HCD data demonstrated that for phosphotyrosine peptides and phosphopeptides containing two potential phosphorylation sites, PTM-Score probability cutoff values of <1 can be applied to ensure an FLR of 1%. Proper adjustment of localization probability cutoffs allowed us to significantly increase the number of confident sites with an FLR of <1%.Our findings underscore the need for the systematic assessment of FLRs for different score values to report confident modification site localization.

[1]  J. Porath,et al.  Metal chelate affinity chromatography, a new approach to protein fractionation , 1975, Nature.

[2]  G. Murphy,et al.  LNCaP model of human prostatic carcinoma. , 1983, Cancer research.

[3]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[4]  J. DeGnore,et al.  Fragmentation of phosphopeptides in an ion trap mass spectrometer , 1998, Journal of the American Society for Mass Spectrometry.

[5]  D. N. Perkins,et al.  Probability‐based protein identification by searching sequence databases using mass spectrometry data , 1999, Electrophoresis.

[6]  J. Shabanowitz,et al.  Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[7]  S. Bryant,et al.  Open mass spectrometry search algorithm. , 2004, Journal of proteome research.

[8]  J. Shabanowitz,et al.  A neutral loss activation method for improved phosphopeptide sequence analysis by quadrupole ion trap mass spectrometry. , 2004, Analytical chemistry.

[9]  A. Heck,et al.  Selective isolation at the femtomole level of phosphopeptides from proteolytic digests using 2D-NanoLC-ESI-MS/MS and titanium oxide precolumns. , 2004, Analytical chemistry.

[10]  Gilbert S Omenn,et al.  An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specificity analysis , 2005, Proteomics.

[11]  P. Roepstorff,et al.  Highly Selective Enrichment of Phosphorylated Peptides from Peptide Mixtures Using Titanium Dioxide Microcolumns* , 2005, Molecular & Cellular Proteomics.

[12]  M. Mann,et al.  Global, In Vivo, and Site-Specific Phosphorylation Dynamics in Signaling Networks , 2006, Cell.

[13]  Steven P Gygi,et al.  A probability-based approach for high-throughput protein phosphorylation analysis and site localization , 2006, Nature Biotechnology.

[14]  J. Peters,et al.  Titanium dioxide as a chemo-affinity solid phase in offline phosphopeptide chromatography prior to HPLC-MS/MS analysis , 2007, Nature Protocols.

[15]  M. Mann,et al.  Higher-energy C-trap dissociation for peptide modification analysis , 2007, Nature Methods.

[16]  Yingxin Zhao,et al.  Toward a global characterization of the phosphoproteome in prostate cancer cells: Identification of phosphoproteins in the LNCaP cell line , 2007, Electrophoresis.

[17]  G. Reid,et al.  Mechanistic insights into the multistage gas-phase fragmentation behavior of phosphoserine- and phosphothreonine-containing peptides. , 2008, Journal of proteome research.

[18]  Kai A Reidegeld,et al.  An easy‐to‐use Decoy Database Builder software tool, implementing different decoy strategies for false discovery rate calculation in automated MS/MS protein identifications , 2008, Proteomics.

[19]  A. Alpert Electrostatic repulsion hydrophilic interaction chromatography for isocratic separation of charged solutes and selective isolation of phosphopeptides. , 2008, Analytical chemistry.

[20]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

[21]  K. Mechtler,et al.  Enhanced detection and identification of multiply phosphorylated peptides using TiO2 enrichment in combination with MALDI TOF/TOF MS , 2008, Proteomics.

[22]  M. Mann,et al.  Quantitative phosphoproteome analysis of a mouse liver cell line reveals specificity of phosphatase inhibitors , 2008, Proteomics.

[23]  Brian E. Ruttenberg,et al.  PhosphoScore: an open-source phosphorylation site assignment tool for MSn data. , 2008, Journal of proteome research.

[24]  L. Huber Signal Transduction Proteomics , 2008, Proteomics.

[25]  M. Larsen,et al.  Analytical strategies for phosphoproteomics , 2009, Expert review of neurotherapeutics.

[26]  S. Lemeer,et al.  The phosphoproteomics data explosion. , 2009, Current opinion in chemical biology.

[27]  M. Mann,et al.  Global Effects of Kinase Inhibitors on Signaling Networks Revealed by Quantitative Phosphoproteomics , 2009, Molecular & Cellular Proteomics.

[28]  Martin Zeller,et al.  SLoMo: automated site localization of modifications from ETD/ECD mass spectra. , 2009, Journal of proteome research.

[29]  S. Mohammed,et al.  Phosphopeptide fragmentation and analysis by mass spectrometry. , 2009, Journal of mass spectrometry : JMS.

[30]  Lennart Martens,et al.  A guide to the Proteomics Identifications Database proteomics data repository , 2009, Proteomics.

[31]  M. Mann,et al.  Feasibility of large-scale phosphoproteomics with higher energy collisional dissociation fragmentation. , 2010, Journal of proteome research.

[32]  F. Giorgianni,et al.  Characterization of the phosphoproteome in LNCaP prostate cancer cells by in-gel isoelectric focusing and tandem mass spectrometry. , 2010, Journal of proteome research.

[33]  S. Brunak,et al.  Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis , 2010, Science Signaling.

[34]  S. Sze,et al.  Novel application of electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) in shotgun proteomics: comprehensive profiling of rat kidney proteome. , 2010, Journal of proteome research.

[35]  B. Kuster,et al.  Confident Phosphorylation Site Localization Using the Mascot Delta Score , 2010, Molecular & Cellular Proteomics.

[36]  S. Gammeltoft,et al.  Quantitative Phosphoproteomics Dissection of Seven-transmembrane Receptor Signaling Using Full and Biased Agonists* , 2010, Molecular & Cellular Proteomics.

[37]  J. Olsen,et al.  Global Phosphoproteome Profiling Reveals Unanticipated Networks Responsive to Cisplatin Treatment of Embryonic Stem Cells , 2011, Molecular and Cellular Biology.

[38]  C. Gretzmeier,et al.  Comparison of ERLIC-TiO2, HILIC-TiO2, and SCX-TiO2 for global phosphoproteomics approaches. , 2011, Journal of proteome research.

[39]  T. Köcher,et al.  Universal and confident phosphorylation site localization using phosphoRS. , 2011, Journal of proteome research.

[40]  H. Hermeking,et al.  Genome-wide Characterization of miR-34a Induced Changes in Protein and mRNA Expression by a Combined Pulsed SILAC and Microarray Analysis* , 2011, Molecular & Cellular Proteomics.

[41]  P. Jallepalli,et al.  Combination of Chemical Genetics and Phosphoproteomics for Kinase Signaling Analysis Enables Confident Identification of Cellular Downstream Targets* , 2011, Molecular & Cellular Proteomics.

[42]  R. Zahedi,et al.  Catch me if you can: Mass spectrometry‐based phosphoproteomics and quantification strategies , 2011, Proteomics.

[43]  M. Mann,et al.  Large-scale phosphosite quantification in tissues by a spike-in SILAC method , 2011, Nature Methods.

[44]  M. Mann,et al.  Andromeda: a peptide search engine integrated into the MaxQuant environment. , 2011, Journal of proteome research.

[45]  C. Lowe,et al.  Platforms for enrichment of phosphorylated proteins and peptides in proteomics. , 2012, Trends in biotechnology.

[46]  N. B. Viana,et al.  Extracellular matrix secreted by reactive stroma is a main inducer of pro-tumorigenic features on LNCaP prostate cancer cells. , 2012, Cancer letters.

[47]  Y. Kaneda,et al.  Androgen-Regulated Transcriptional Control of Sialyltransferases in Prostate Cancer Cells , 2012, PloS one.

[48]  M. Sadar,et al.  Large scale phosphoproteome analysis of LNCaP human prostate cancer cells. , 2012, Molecular bioSystems.

[49]  E. White,et al.  Effect of dual inhibition of apoptosis and autophagy in prostate cancer , 2012, The Prostate.

[50]  Y. Yatabe,et al.  Histone H1 expression in human prostate cancer tissues and cell lines , 2012, Pathology international.

[51]  S. Aerts,et al.  Variations in the exome of the LNCaP prostate cancer cell line , 2012, The Prostate.

[52]  R. Sinha,et al.  Remarkable inhibition of mTOR signaling by the combination of rapamycin and 1,4‐phenylenebis(methylene)selenocyanate in human prostate cancer cells , 2012, International journal of cancer.

[53]  M. Mann,et al.  A large synthetic peptide and phosphopeptide reference library for mass spectrometry–based proteomics , 2013, Nature Biotechnology.