Systematic Analysis of Protein Phosphorylation Networks From Phosphoproteomic Data
2012; Elsevier BV; Volume: 11; Issue: 10 Linguagem: Inglês
10.1074/mcp.m111.012625
ISSN1535-9484
AutoresChunxia Song, Mingliang Ye, Zexian Liu, Han Cheng, Xinning Jiang, Guanghui Han, Zhou Songyang, Yexiong Tan, Hongyang Wang, Jian Ren, Yu Xue, Hanfa Zou,
Tópico(s)Glycosylation and Glycoproteins Research
ResumoIn eukaryotes, hundreds of protein kinases (PKs) specifically and precisely modify thousands of substrates at specific amino acid residues to faithfully orchestrate numerous biological processes, and reversibly determine the cellular dynamics and plasticity. Although over 100,000 phosphorylation sites (p-sites) have been experimentally identified from phosphoproteomic studies, the regulatory PKs for most of these sites still remain to be characterized. Here, we present a novel software package of iGPS for the prediction of in vivo site-specific kinase-substrate relations mainly from the phosphoproteomic data. By critical evaluations and comparisons, the performance of iGPS is satisfying and better than other existed tools. Based on the prediction results, we modeled protein phosphorylation networks and observed that the eukaryotic phospho-regulation is poorly conserved at the site and substrate levels. With an integrative procedure, we conducted a large-scale phosphorylation analysis of human liver and experimentally identified 9719 p-sites in 2998 proteins. Using iGPS, we predicted a human liver protein phosphorylation networks containing 12,819 potential site-specific kinase-substrate relations among 350 PKs and 962 substrates for 2633 p-sites. Further statistical analysis and comparison revealed that 127 PKs significantly modify more or fewer p-sites in the liver protein phosphorylation networks against the whole human protein phosphorylation network. The largest data set of the human liver phosphoproteome together with computational analyses can be useful for further experimental consideration. This work contributes to the understanding of phosphorylation mechanisms at the systemic level, and provides a powerful methodology for the general analysis of in vivo post-translational modifications regulating sub-proteomes. In eukaryotes, hundreds of protein kinases (PKs) specifically and precisely modify thousands of substrates at specific amino acid residues to faithfully orchestrate numerous biological processes, and reversibly determine the cellular dynamics and plasticity. Although over 100,000 phosphorylation sites (p-sites) have been experimentally identified from phosphoproteomic studies, the regulatory PKs for most of these sites still remain to be characterized. Here, we present a novel software package of iGPS for the prediction of in vivo site-specific kinase-substrate relations mainly from the phosphoproteomic data. By critical evaluations and comparisons, the performance of iGPS is satisfying and better than other existed tools. Based on the prediction results, we modeled protein phosphorylation networks and observed that the eukaryotic phospho-regulation is poorly conserved at the site and substrate levels. With an integrative procedure, we conducted a large-scale phosphorylation analysis of human liver and experimentally identified 9719 p-sites in 2998 proteins. Using iGPS, we predicted a human liver protein phosphorylation networks containing 12,819 potential site-specific kinase-substrate relations among 350 PKs and 962 substrates for 2633 p-sites. Further statistical analysis and comparison revealed that 127 PKs significantly modify more or fewer p-sites in the liver protein phosphorylation networks against the whole human protein phosphorylation network. The largest data set of the human liver phosphoproteome together with computational analyses can be useful for further experimental consideration. This work contributes to the understanding of phosphorylation mechanisms at the systemic level, and provides a powerful methodology for the general analysis of in vivo post-translational modifications regulating sub-proteomes. Protein kinase (PK) 1The abbreviations used are:PKprotein kinasePTMpost-translational modificationSLMshort linear motifp-sitephosphorylation sitessKSRsite-specific kinase-substrate relationKSRkinase-substrate relationHTP-MShigh-throughput mass spectrometryGPSgroup-based prediction systemHPNhuman phosphorylation networkiGPSGPS algorithm with the interaction filter, or in vivo GPSPPIprotein-protein interactionPPNprotein phosphorylation networkRP-RPLCreversed-phase-reversed-phase liquid chromatographyPpositive controlNnegative controlSnsensitivitySpspecificityAcaccuracyMCCMathew correlation coefficientKprkinase precisionLprlarge-scale precisionFPRfalse positive rateFDRfalse discovery rateSTKserine/threonine kinaseTKtyrosine kinaseKTFkiss-then-farewellNo PPIwithout PPIExp. PPIexperimental PPIKOWKyprides, Ouzounis, WoesePAFpolymerase-associated factorCTDC-terminal repeat domainHLPPHuman Liver Proteome ProjectMPSSmassively parallel signature sequencingCNHLPPChinese human liver proteome projectpSphosphoserinepTphosphothreoninepYphosphotyrosine.1The abbreviations used are:PKprotein kinasePTMpost-translational modificationSLMshort linear motifp-sitephosphorylation sitessKSRsite-specific kinase-substrate relationKSRkinase-substrate relationHTP-MShigh-throughput mass spectrometryGPSgroup-based prediction systemHPNhuman phosphorylation networkiGPSGPS algorithm with the interaction filter, or in vivo GPSPPIprotein-protein interactionPPNprotein phosphorylation networkRP-RPLCreversed-phase-reversed-phase liquid chromatographyPpositive controlNnegative controlSnsensitivitySpspecificityAcaccuracyMCCMathew correlation coefficientKprkinase precisionLprlarge-scale precisionFPRfalse positive rateFDRfalse discovery rateSTKserine/threonine kinaseTKtyrosine kinaseKTFkiss-then-farewellNo PPIwithout PPIExp. PPIexperimental PPIKOWKyprides, Ouzounis, WoesePAFpolymerase-associated factorCTDC-terminal repeat domainHLPPHuman Liver Proteome ProjectMPSSmassively parallel signature sequencingCNHLPPChinese human liver proteome projectpSphosphoserinepTphosphothreoninepYphosphotyrosine.-catalyzed phosphorylation is one of the most important and ubiquitous post-translational modifications (PTMs) of proteins. This process temporally and spatially modifies ∼30% of all cellular proteins and plays a crucial role in regulating a variety of biological processes such as signal transduction and the cell cycle (1Olsen J.V. Blagoev B. Gnad F. Macek B. Kumar C. Mortensen P. Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006; 127: 635-648Abstract Full Text Full Text PDF PubMed Scopus (2825) Google Scholar, 2Ubersax J.A. Ferrell Jr., J.E. Mechanisms of specificity in protein phosphorylation.Nat. Rev. Mol. Cell Biol. 2007; 8: 530-541Crossref PubMed Scopus (1001) Google Scholar, 3Manning G. Whyte D.B. Martinez R. Hunter T. Sudarsanam S. The protein kinase complement of the human genome.Science. 2002; 298: 1912-1934Crossref PubMed Scopus (6240) Google Scholar). The human genome encodes 518 PK genes (∼2% of the genome), with different PKs showing distinct recognition specificities; each PK modifies only a limited subset of substrates, thereby guaranteeing the fidelity of cell signaling (1Olsen J.V. Blagoev B. Gnad F. Macek B. Kumar C. Mortensen P. Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006; 127: 635-648Abstract Full Text Full Text PDF PubMed Scopus (2825) Google Scholar, 2Ubersax J.A. Ferrell Jr., J.E. Mechanisms of specificity in protein phosphorylation.Nat. Rev. Mol. Cell Biol. 2007; 8: 530-541Crossref PubMed Scopus (1001) Google Scholar, 3Manning G. Whyte D.B. Martinez R. Hunter T. Sudarsanam S. The protein kinase complement of the human genome.Science. 2002; 298: 1912-1934Crossref PubMed Scopus (6240) Google Scholar). It is accepted that short linear motifs (SLMs) around the phosphorylation sites (p-sites) provide primary specificity (2Ubersax J.A. Ferrell Jr., J.E. Mechanisms of specificity in protein phosphorylation.Nat. Rev. Mol. Cell Biol. 2007; 8: 530-541Crossref PubMed Scopus (1001) Google Scholar, 4Kobe B. Kampmann T. Forwood J.K. Listwan P. Brinkworth R.I. Substrate specificity of protein kinases and computational prediction of substrates.Biochim. Biophys. Acta. 2005; 1754: 200-209Crossref PubMed Scopus (88) Google Scholar, 5Kreegipuu A. Blom N. Brunak S. Järv J. Statistical analysis of protein kinase specificity determinants.FEBS Lett. 1998; 430: 45-50Crossref PubMed Scopus (112) Google Scholar, 6Songyang Z. Lu K.P. Kwon Y.T. Tsai L.H. Filhol O. Cochet C. Brickey D.A. Soderling T.R. Bartleson C. Graves D.J. DeMaggio A.J. Hoekstra M.F. Blenis J. Hunter T. Cantley L.C. A structural basis for substrate specificities of protein Ser/Thr kinases: primary sequence preference of casein kinases I and II, NIMA, phosphorylase kinase, calmodulin-dependent kinase II, CDK5, and Erk1.Mol. Cell. Biol. 1996; 16: 6486-6493Crossref PubMed Scopus (489) Google Scholar), and a variety of additional contextual factors, including co-localization, co-expression, co-complex, and physical interaction of the PKs with their targets, contribute additional specificity in vivo (7Yaffe M.B. Leparc G.G. Lai J. Obata T. Volinia S. Cantley L.C. A motif-based profile scanning approach for genome-wide prediction of signaling pathways.Nat. Biotechnol. 2001; 19: 348-353Crossref PubMed Scopus (464) Google Scholar, 8Linding R. Jensen L.J. Ostheimer G.J. van Vugt M.A. Jorgensen C. Miron I.M. Diella F. Colwill K. Taylor L. Elder K. Metalnikov P. Nguyen V. Pasculescu A. Jin J. Park J.G. Samson L.D. Woodgett J.R. Russell R.B. Bork P. Yaffe M.B. Pawson T. Systematic discovery of in vivo phosphorylation networks.Cell. 2007; 129: 1415-1426Abstract Full Text Full Text PDF PubMed Scopus (611) Google Scholar, 9Linding R. Jensen L.J. Pasculescu A. Olhovsky M. Colwill K. Bork P. Yaffe M.B. Pawson T. NetworKIN: a resource for exploring cellular phosphorylation networks.Nucleic Acids Res. 2008; 36: D695-D699Crossref PubMed Scopus (255) Google Scholar, 10Tan C.S. Linding R. Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks.Proteomics. 2009; 9: 5233-5242Crossref PubMed Scopus (18) Google Scholar). Aberrances of PKs or key substrates disrupt normal function, rewire signaling pathways, and are implicated in various diseases and cancers (3Manning G. Whyte D.B. Martinez R. Hunter T. Sudarsanam S. The protein kinase complement of the human genome.Science. 2002; 298: 1912-1934Crossref PubMed Scopus (6240) Google Scholar, 11Lahiry P. Torkamani A. Schork N.J. Hegele R.A. Kinase mutations in human disease: interpreting genotype-phenotype relationships.Nat. Rev. Genet. 2010; 11: 60-74Crossref PubMed Scopus (262) Google Scholar). In this regard, the identification of kinase-specific p-sites and the systematic elucidation of site-specific kinase-substrate relations (ssKSRs) would provide a fundamental basis for understanding cell plasticity and dynamics and for dissecting the molecular mechanisms of various diseases, whereas the ultimate progress could suggest potential drug targets for future biomedical design (8Linding R. Jensen L.J. Ostheimer G.J. van Vugt M.A. Jorgensen C. Miron I.M. Diella F. Colwill K. Taylor L. Elder K. Metalnikov P. Nguyen V. Pasculescu A. Jin J. Park J.G. Samson L.D. Woodgett J.R. Russell R.B. Bork P. Yaffe M.B. Pawson T. Systematic discovery of in vivo phosphorylation networks.Cell. 2007; 129: 1415-1426Abstract Full Text Full Text PDF PubMed Scopus (611) Google Scholar, 9Linding R. Jensen L.J. Pasculescu A. Olhovsky M. Colwill K. Bork P. Yaffe M.B. Pawson T. NetworKIN: a resource for exploring cellular phosphorylation networks.Nucleic Acids Res. 2008; 36: D695-D699Crossref PubMed Scopus (255) Google Scholar, 10Tan C.S. Linding R. Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks.Proteomics. 2009; 9: 5233-5242Crossref PubMed Scopus (18) Google Scholar). protein kinase post-translational modification short linear motif phosphorylation site site-specific kinase-substrate relation kinase-substrate relation high-throughput mass spectrometry group-based prediction system human phosphorylation network GPS algorithm with the interaction filter, or in vivo GPS protein-protein interaction protein phosphorylation network reversed-phase-reversed-phase liquid chromatography positive control negative control sensitivity specificity accuracy Mathew correlation coefficient kinase precision large-scale precision false positive rate false discovery rate serine/threonine kinase tyrosine kinase kiss-then-farewell without PPI experimental PPI Kyprides, Ouzounis, Woese polymerase-associated factor C-terminal repeat domain Human Liver Proteome Project massively parallel signature sequencing Chinese human liver proteome project phosphoserine phosphothreonine phosphotyrosine. protein kinase post-translational modification short linear motif phosphorylation site site-specific kinase-substrate relation kinase-substrate relation high-throughput mass spectrometry group-based prediction system human phosphorylation network GPS algorithm with the interaction filter, or in vivo GPS protein-protein interaction protein phosphorylation network reversed-phase-reversed-phase liquid chromatography positive control negative control sensitivity specificity accuracy Mathew correlation coefficient kinase precision large-scale precision false positive rate false discovery rate serine/threonine kinase tyrosine kinase kiss-then-farewell without PPI experimental PPI Kyprides, Ouzounis, Woese polymerase-associated factor C-terminal repeat domain Human Liver Proteome Project massively parallel signature sequencing Chinese human liver proteome project phosphoserine phosphothreonine phosphotyrosine. Conventional experimental identification of ssKSRs, performed in a one-by-one manner, is labor-intensive, time-consuming and expensive. There are only 3508 known kinase-specific p-sites in the 1390 proteins collected in the Phospho.ELM 8.2 database (released in April 2009) (12Diella F. Gould C.M. Chica C. Via A. Gibson T.J. Phospho.ELM: a database of phosphorylation sites–update 2008.Nucleic Acids Res. 2008; 36: D240-D244Crossref PubMed Scopus (209) Google Scholar). In 2005, Ptacek et al. detected more than 4000 in vitro kinase-substrate relations (KSRs) in Saccharomyces cerevisiae using protein chip technology, although the exact p-sites were not determined (13Ptacek J. Devgan G. Michaud G. Zhu H. Zhu X. Fasolo J. Guo H. Jona G. Breitkreutz A. Sopko R. McCartney R.R. Schmidt M.C. Rachidi N. Lee S.J. Mah A.S. Meng L. Stark M.J. Stern D.F. De Virgilio C. Tyers M. Andrews B. Gerstein M. Schweitzer B. Predki P.F. Snyder M. Global analysis of protein phosphorylation in yeast.Nature. 2005; 438: 679-684Crossref PubMed Scopus (819) Google Scholar). Recently, rapid advances in phosphoproteomics have provided a great opportunity to systematically assess phosphorylation (1Olsen J.V. Blagoev B. Gnad F. Macek B. Kumar C. Mortensen P. Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006; 127: 635-648Abstract Full Text Full Text PDF PubMed Scopus (2825) Google Scholar, 14Villén J. Beausoleil S.A. Gerber S.A. Gygi S.P. Large-scale phosphorylation analysis of mouse liver.Proc. Natl. Acad. Sci. U.S.A. 2007; 104: 1488-1493Crossref PubMed Scopus (628) Google Scholar, 15Han G. Ye M. Zhou H. Jiang X. Feng S. Jiang X. Tian R. Wan D. Zou H. Gu J. Large-scale phosphoproteome analysis of human liver tissue by enrichment and fractionation of phosphopeptides with strong anion exchange chromatography.Proteomics. 2008; 8: 1346-1361Crossref PubMed Scopus (181) Google Scholar, 16Han G. Ye M. Liu H. Song C. Sun D. Wu Y. Jiang X. Chen R. Wang C. Wang L. Zou H. Phosphoproteome analysis of human liver tissue by long-gradient nanoflow LC coupled with multiple stage MS analysis.Electrophoresis. 2010; 31: 1080-1089PubMed Google Scholar, 17Tan C.S. Bodenmiller B. Pasculescu A. Jovanovic M. Hengartner M.O. Jørgensen C. Bader G.D. Aebersold R. Pawson T. Linding R. Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases.Sci. Signal. 2009; 2: ra39Crossref PubMed Scopus (160) Google Scholar, 18Xu C.F. Lu Y. Ma J. Mohammadi M. Neubert T.A. Identification of phosphopeptides by MALDI Q-TOF MS in positive and negative ion modes after methyl esterification.Mol. Cell. Proteomics. 2005; 4: 809-818Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar, 19Steen H. Jebanathirajah J.A. Rush J. Morrice N. Kirschner M.W. Phosphorylation analysis by mass spectrometry: myths, facts, and the consequences for qualitative and quantitative measurements.Mol. Cell. Proteomics. 2006; 5: 172-181Abstract Full Text Full Text PDF PubMed Scopus (294) Google Scholar, 20Li X. Gerber S.A. Rudner A.D. Beausoleil S.A. Haas W. Villén J. Elias J.E. Gygi S.P. Large-scale phosphorylation analysis of alpha-factor-arrested Saccharomyces cerevisiae.J. Proteome Res. 2007; 6: 1190-1197Crossref PubMed Scopus (259) Google Scholar, 21Matsuoka S. Ballif B.A. Smogorzewska A. McDonald 3rd, E.R. Hurov K.E. Luo J. Bakalarski C.E. Zhao Z. Solimini N. Lerenthal Y. Shiloh Y. Gygi S.P. Elledge S.J. ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage.Science. 2007; 316: 1160-1166Crossref PubMed Scopus (2361) Google Scholar). State-of-the-art high-throughput mass spectrometry (HTP-MS) techniques have the ability to detect thousands of p-sites in cells or tissues in a single experiment (1Olsen J.V. Blagoev B. Gnad F. Macek B. Kumar C. Mortensen P. Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006; 127: 635-648Abstract Full Text Full Text PDF PubMed Scopus (2825) Google Scholar, 14Villén J. Beausoleil S.A. Gerber S.A. Gygi S.P. Large-scale phosphorylation analysis of mouse liver.Proc. Natl. Acad. Sci. U.S.A. 2007; 104: 1488-1493Crossref PubMed Scopus (628) Google Scholar, 16Han G. Ye M. Liu H. Song C. Sun D. Wu Y. Jiang X. Chen R. Wang C. Wang L. Zou H. Phosphoproteome analysis of human liver tissue by long-gradient nanoflow LC coupled with multiple stage MS analysis.Electrophoresis. 2010; 31: 1080-1089PubMed Google Scholar, 22Song C. Ye M. Han G. Jiang X. Wang F. Yu Z. Chen R. Zou H. Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides.Anal. Chem. 2010; 82: 53-56Crossref PubMed Scopus (136) Google Scholar). We have collected 145,646 eukaryotic p-sites, primarily from these large-scale assays (supplemental Table S1); the regulatory PKs for 97.6% of these sites remain to be characterized. Alternatively, the in silico prediction of ssKSRs can generate useful information for subsequent experimental manipulation. In 2001, Yaffe et al. developed the SLM-based software Scansite for the prediction of ssKSRs directly from protein primary sequences (7Yaffe M.B. Leparc G.G. Lai J. Obata T. Volinia S. Cantley L.C. A motif-based profile scanning approach for genome-wide prediction of signaling pathways.Nat. Biotechnol. 2001; 19: 348-353Crossref PubMed Scopus (464) Google Scholar). Later, the strategy was employed in a variety of kinase-specific predictors (23Xue Y. Gao X. Cao J. Liu Z. Jin C. Wen L. Yao X. Ren J. A summary of computational resources for protein phosphorylation.Curr. Protein Pept. Sci. 2010; 11: 485-496Crossref PubMed Scopus (52) Google Scholar), including our group-based prediction system (GPS) program (24Xue Y. Ren J. Gao X. Jin C. Wen L. Yao X. GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy.Mol. Cell. Proteomics. 2008; 7: 1598-1608Abstract Full Text Full Text PDF PubMed Scopus (534) Google Scholar). These tools may guarantee partially correct predictions for in vitro phosphorylation, but they are far from being adequate for in vivo hits because the contributions of various contextual factors cannot be neglected. To address this problem, Linding et al. developed a predictor of NetworKIN by combining an SLM-based approach with network contextual information to predict in vivo ssKSRs, and a potential in vivo human phosphorylation network (HPN) was modeled by annotating the phosphoproteomic data (8Linding R. Jensen L.J. Ostheimer G.J. van Vugt M.A. Jorgensen C. Miron I.M. Diella F. Colwill K. Taylor L. Elder K. Metalnikov P. Nguyen V. Pasculescu A. Jin J. Park J.G. Samson L.D. Woodgett J.R. Russell R.B. Bork P. Yaffe M.B. Pawson T. Systematic discovery of in vivo phosphorylation networks.Cell. 2007; 129: 1415-1426Abstract Full Text Full Text PDF PubMed Scopus (611) Google Scholar, 9Linding R. Jensen L.J. Pasculescu A. Olhovsky M. Colwill K. Bork P. Yaffe M.B. Pawson T. NetworKIN: a resource for exploring cellular phosphorylation networks.Nucleic Acids Res. 2008; 36: D695-D699Crossref PubMed Scopus (255) Google Scholar). In this work, we developed a software package of iGPS (GPS algorithm with the interaction filter, or in vivo GPS) mainly for the prediction of in vivo ssKSRs. Eukaryotic PKs were classified into a hierarchy with four levels: group, family, subfamily, and single PK (3Manning G. Whyte D.B. Martinez R. Hunter T. Sudarsanam S. The protein kinase complement of the human genome.Science. 2002; 298: 1912-1934Crossref PubMed Scopus (6240) Google Scholar). Based on the hypothesis that similar PKs recognize similar SLMs, we selected a predictor in GPS 2.0 (24Xue Y. Ren J. Gao X. Jin C. Wen L. Yao X. GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy.Mol. Cell. Proteomics. 2008; 7: 1598-1608Abstract Full Text Full Text PDF PubMed Scopus (534) Google Scholar) for each PK and directly predicted the potential PKs for the un-annotated p-sites from the phosphoproteomic studies. Consequently, protein–protein interaction (PPI) information was used as the major contextual factor to reduce over 95% potentially false-positive hits. The performance of iGPS was shown by critical evaluations and comparisons to be promising for the accurate prediction of in vivo ssKSRs. Based on the prediction results of iGPS, we modeled eukaryotic protein phosphorylation networks (PPNs) and observed that phosphorylation regulation changes dramatically over the course of evolution, with poor conservation at both the site and substrate levels. This observation is consistent with previous studies (17Tan C.S. Bodenmiller B. Pasculescu A. Jovanovic M. Hengartner M.O. Jørgensen C. Bader G.D. Aebersold R. Pawson T. Linding R. Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases.Sci. Signal. 2009; 2: ra39Crossref PubMed Scopus (160) Google Scholar, 25Boekhorst J. van Breukelen B. Heck Jr., A. Snel B. Comparative phosphoproteomics reveals evolutionary and functional conservation of phosphorylation across eukaryotes.Genome Biol. 2008; 9: R144Crossref PubMed Scopus (65) Google Scholar). Furthermore, we combined a new multidimensional separation approach using reversed-phase-reversed-phase liquid chromatography (RP-RPLC) (22Song C. Ye M. Han G. Jiang X. Wang F. Yu Z. Chen R. Zou H. Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides.Anal. Chem. 2010; 82: 53-56Crossref PubMed Scopus (136) Google Scholar), with HTP-MS and a new data process platform of ArMone (26Jiang X. Ye M. Cheng K. Zou H. ArMone: a software suite specially designed for processing and analysis of phosphoproteome data.J. Proteome Res. 2010; 9: 2743-2751Crossref PubMed Scopus (24) Google Scholar) to conduct a large-scale phosphorylation analysis of the human liver. Totally, 9719 p-sites of 2998 substrates were identified from 10,644 non-redundant phosphopeptides. The potential ssKSRs were predicted for the human liver phosphoproteome, whereas further statistical analysis suggested that 60 and 67 PKs preferentially regulate more or fewer p-sites in the human liver PPN (p value<0.01). A number of results are consistent with previous observations, whereas other predictions can be useful for further experimental manipulation. The experimentally identified p-sites were taken from several major databases, including PhosphoPep v2.0 (27Bodenmiller B. Campbell D. Gerrits B. Lam H. Jovanovic M. Picotti P. Schlapbach R. Aebersold R. PhosphoPep–a database of protein phosphorylation sites in model organisms.Nat. Biotechnol. 2008; 26: 1339-1340Crossref PubMed Scopus (178) Google Scholar), Phospho.ELM 8.3 (released in April 2010) (12Diella F. Gould C.M. Chica C. Via A. Gibson T.J. Phospho.ELM: a database of phosphorylation sites–update 2008.Nucleic Acids Res. 2008; 36: D240-D244Crossref PubMed Scopus (209) Google Scholar, 28Diella F. Cameron S. Gemünd C. Linding R. Via A. Kuster B. Sicheritz-Pontén T. Blom N. Gibson T.J. Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins.BMC Bioinformatics. 2004; 5: 79Crossref PubMed Scopus (305) Google Scholar), SysPTM 1.1 (29Li H. Xing X. Ding G. Li Q. Wang C. Xie L. Zeng R. Li Y. SysPTM - a systematic resource for proteomic research of post-translational modifications.Mol. Cell. Proteomics. 2009; 8: 1839-1849Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar), PhosphoSitePlus (30Hornbeck P.V. Chabra I. Kornhauser J.M. Skrzypek E. Zhang B. PhosphoSite: A bioinformatics resource dedicated to physiological protein phosphorylation.Proteomics. 2004; 4: 1551-1561Crossref PubMed Scopus (445) Google Scholar), and HPRD 9.0 (31Keshava Prasad T.S. Goel R. Kandasamy K. Keerthikumar S. Kumar S. Mathivanan S. Telikicherla D. Raju R. Shafreen B. Venugopal A. Balakrishnan L. Marimuthu A. Banerjee S. Somanathan D.S. Sebastian A. Rani S. Ray S. Harrys Kishore C.J. Kanth S. Ahmed M. Kashyap M.K. Mohmood R. Ramachandra Y.L. Krishna V. Rahiman B.A. Mohan S. Ranganathan P. Ramabadran S. Chaerkady R. Pander A. Human Protein Reference Database–2009 update.Nucleic Acids Res. 2009; 37: D767-D772Crossref PubMed Scopus (2526) Google Scholar). We also collected thousands of p-sites from several published articles (14Villén J. Beausoleil S.A. Gerber S.A. Gygi S.P. Large-scale phosphorylation analysis of mouse liver.Proc. Natl. Acad. Sci. U.S.A. 2007; 104: 1488-1493Crossref PubMed Scopus (628) Google Scholar, 17Tan C.S. Bodenmiller B. Pasculescu A. Jovanovic M. Hengartner M.O. Jørgensen C. Bader G.D. Aebersold R. Pawson T. Linding R. Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases.Sci. Signal. 2009; 2: ra39Crossref PubMed Scopus (160) Google Scholar, 18Xu C.F. Lu Y. Ma J. Mohammadi M. Neubert T.A. Identification of phosphopeptides by MALDI Q-TOF MS in positive and negative ion modes after methyl esterification.Mol. Cell. Proteomics. 2005; 4: 809-818Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar, 19Steen H. Jebanathirajah J.A. Rush J. Morrice N. Kirschner M.W. Phosphorylation analysis by mass spectrometry: myths, facts, and the consequences for qualitative and quantitative measurements.Mol. Cell. Proteomics. 2006; 5: 172-181Abstract Full Text Full Text PDF PubMed Scopus (294) Google Scholar, 20Li X. Gerber S.A. Rudner A.D. Beausoleil S.A. Haas W. Villén J. Elias J.E. Gygi S.P. Large-scale phosphorylation analysis of alpha-factor-arrested Saccharomyces cerevisiae.J. Proteome Res. 2007; 6: 1190-1197Crossref PubMed Scopus (259) Google Scholar, 21Matsuoka S. Ballif B.A. Smogorzewska A. McDonald 3rd, E.R. Hurov K.E. Luo J. Bakalarski C.E. Zhao Z. Solimini N. Lerenthal Y. Shiloh Y. Gygi S.P. Elledge S.J. ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage.Science. 2007; 316: 1160-1166Crossref PubMed Scopus (2361) Google Scholar). The organism-specific information was distinguished from the database or data set comments. All p-sites with their protein sequences were mapped to the UniProt benchmark sequences (More details in supplemental Experimental Procedures). In total, the final phosphorylation data set contains 145,646 p-sites in 28,457 substrates, with 14,534, 5555, 15,622, 49,119, and 60,816 p-sites in S. cerevisiae, C. elegans, D. melanogaster, M. musculus, and H. sapiens, respectively (supplemental Table S1). To evaluate the prediction performance of iGPS, we took 3508 experimentally verified kinase-specific p-sites in 1,390 proteins from Phospho.ELM 8.2 (12Diella F. Gould C.M. Chica C. Via A. Gibson T.J. Phospho.ELM: a database of phosphorylation sites–update 2008.Nucleic Acids Res. 2008; 36: D240-D244Crossref PubMed Scopus (209) Google Scholar, 28Diella F. Cameron S. Gemünd C. Linding R. Via A. Kuster B. Sicheritz-Pontén T. Blom N. Gibson T.J. Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins.BMC Bioinformatics. 2004; 5: 79Crossref PubMed Scopus (305) Google Scholar) as the positive control (P) (supplemental Table S2), whereas all other Ser/Thr or Tyr residues in the same substrates were regarded as the negative control (N). Thus:P=TP+FN (Eq. 1)N=TN+FP (Eq. 2) To compare iGPS with NetworKIN (8Linding R. Jensen L.J. Ostheimer G.J. van Vugt M.A. Jorgensen C. Miron I.M. Diella F. Colwill K. Taylor L. Elder K. Metalnikov P. Nguyen V. Pasculescu A. Jin J. Park J.G. Samson L.D. Woodgett J.R. Russell R.B. Bork P. Yaffe M.B. Pawson T. Systematic discovery of in vivo phosphorylation networks.Cell. 2007; 129: 1415-1426Abstract Full Text Full Text PDF PubMed Scopus (611) Google Scholar, 9Linding R. Jensen L.J. Pasculescu A. Olhovsky M. Colwill K. Bork P. Yaffe M.B. Pawson T. NetworKIN: a resource for exploring cellular phosphorylation networks.Nucleic Acids Res. 2008; 36: D695-D699Crossref PubMed Scopus (255) Google Scholar), we collected 1701 kinase-specific p-sites in 830 substrates from Phospho.ELM 9.0 (released in September 2010) (12Diella F. Gould C.M. Chica C. Via A. Gibson T.J. Phospho.ELM: a database of phosphorylation sites–update 2008.Nucleic Acids Res. 2008; 36: D240-D244Crossref PubMed Scopus (209) Google Scholar, 28Diella F. Cameron S. Gemünd C. Linding R. Via A. Kuster B. Sicheritz-Pontén T. Blom
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