Systematic Analysis of Protein Phosphorylation Networks From Phosphoproteomic Data

• DOI: 10.1074/mcp.m111.012625
• Publication Date: 2012-07-14T09:45:51Z
• Authors: Chunxia Song; Mingliang Ye; Zexian Liu; Han Cheng; Xinning Jiang; Guanghui Han; Zhou Songyang; Yexiong Tan; Hongyang Wang; Jian Ren; Yu Xue; Hanfa Zou
• Keywords: Phosphoproteomics

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