Computational phosphoproteomics: From identification to localization

Proteomics

Volumn 15, Issue 5-6, 2015, Pages 950-963

  Lee, Dave C H ^a   Jones, Andrew R ^b   Hubbard, Simon J ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b UNIVERSITY OF LIVERPOOL   (United Kingdom)

Author keywords

Bioinformatics; Data processing and analysis; Phosphoproteomics; Technology

Indexed keywords

PHOSPHATASE; PHOSPHATE; PHOSPHOPEPTIDE; PHOSPHOTRANSFERASE; PHOSPHOPROTEIN;

BIOINFORMATICS; BIOLOGICAL FUNCTIONS; BIOLOGY; COMPUTER PROGRAM; DATA PROCESSING; MASS SPECTROMETRY; PHOSPHORYLATION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN LOCALIZATION; PROTEIN PROCESSING; PROTEOMICS; QUALITY CONTROL; REVIEW;

PHOSPHOPEPTIDES; PHOSPHOPROTEINS; PROTEOMICS;

EID: 84924663951     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201400372     Document Type: Review

References (106)

1. Koivomagi, M., Ord, M., Iofik, A., Valk, E. et al., Multisite phosphorylation networks as signal processors for Cdk1. Nat. Struct. Mol. Biol. 2013, 20, 1415-1424.
2. Cohen, P., The role of protein phosphorylation in human health and disease. The Sir Hans Krebs Medal Lecture. Eur. J. Biochem. 2001, 268, 5001-5010.
3. Steen, 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, 172-181.
4. Ruvolo, P. P., Deng, X., May, W. S., Phosphorylation of Bcl2 and regulation of apoptosis. Leukemia 2001, 15, 515-522.
5. Mayr, B., Montminy, M., Transcriptional regulation by the phosphorylation-dependent factor CREB. Nat. Rev. Mol. Cell Biol. 2001, 2, 599-609.
6. Nishi, H., Hashimoto, K., Panchenko, A. R., Phosphorylation in protein-protein binding: effect on stability and function. Structure 2011, 19, 1807-1815.
7. Schlessinger, J., Cell signaling by receptor tyrosine kinases. Cell 2000, 103, 211-225.
8. Olsen, J. V., Blagoev, B., Gnad, F., Macek, B. et al., Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 2006, 127, 635-648.
9. Willems, A. R., Goh, T., Taylor, L., Chernushevich, I. et al., SCF ubiquitin protein ligases and phosphorylation-dependent proteolysis. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 1999, 354, 1533-1550.
10. Mandell, J. W., Phosphorylation state-specific antibodies: applications in investigative and diagnostic pathology. Am. J. Pathol. 2003, 163, 1687-1698.
11. Bodenmiller, B., Wanka, S., Kraft, C., Urban, J. et al., Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast. Sci. Signal. 2010, 3, rs4.
12. Dunn, J. D., Reid, G. E., Bruening, M. L., Techniques for phosphopeptide enrichment prior to analysis by mass spectrometry. Mass Spectrom. Rev. 2010, 29, 29-54.
13. Gafken, P. R., Lampe, P. D., Methodologies for characterizing phosphoproteins by mass spectrometry. Cell Commun. Adhes. 2006, 13, 249-262.
14. Leitner, A., Sturm, M., Hudecz, O., Mazanek, M. et al., Probing the phosphoproteome of HeLa cells using nanocast metal oxide microspheres for phosphopeptide enrichment. Anal. Chem. 2010, 82, 2726-2733.
15. Olsen, J. V., Vermeulen, M., Santamaria, A., Kumar, C. et al., Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 2010, 3, ra3.
16. Wu, R., Haas, W., Dephoure, N., Huttlin, E. L. et al., A large-scale method to measure absolute protein phosphorylation stoichiometries. Nat. Methods 2011, 8, 677-683.
17. Diella, F., Cameron, S., Gemund, C., Linding, R. et al., Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins. BMC Bioinformatics 2004, 5, 79.
18. Heazlewood, J. L., Durek, P., Hummel, J., Selbig, J. et al., PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor. Nucleic Acids Res. 2008, 36, D1015-D1021.
19. Hornbeck, P. V., Kornhauser, J. M., Tkachev, S., Zhang, B. et al., PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res. 2012, 40, D261-D270.
20. Sadowski, I., Breitkreutz, B. J., Stark, C., Su, T. C. et al., The PhosphoGRID Saccharomyces cerevisiae protein phosphorylation site database: version 2.0 update. Database 2013, 2013, bat026.
21. Huang, H. D., Lee, T. Y., Tzeng, S. W., Horng, J. T., KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites. Nucleic Acids Res. 2005, 33, W226-W229.
22. Iakoucheva, L. M., Radivojac, P., Brown, C. J., O'Connor, T. R. et al., The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res. 2004, 32, 1037-1049.
23. Dou, Y., Yao, B., Zhang, C., PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine. Amino Acids 2014, 46, 1459-1469.
24. Durek, P., Schmidt, R., Heazlewood, J. L., Jones, A. et al., PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update. Nucleic Acids Res. 2010, 38, D828-D834.
25. Trost, B., Kusalik, A., Computational prediction of eukaryotic phosphorylation sites. Bioinformatics 2011, 27, 2927-2935.
26. Koivomagi, M., Valk, E., Venta, R., Iofik, A. et al., Cascades of multisite phosphorylation control Sic1 destruction at the onset of S phase. Nature 2011, 480, 128-131.
27. Mann, M., Functional and quantitative proteomics using SILAC. Nat. Rev. Mol. Cell Biol. 2006, 7, 952-958.
28. Daub, H., Olsen, J. V., Bairlein, M., Gnad, F. et al., Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol. Cell 2008, 31, 438-448.
29. Dephoure, N., Zhou, C., Villen, J., Beausoleil, S. A. et al., A quantitative atlas of mitotic phosphorylation. Proc. Natl. Acad. Sci. USA 2008, 105, 10762-10767.
30. Oppermann, F. S., Grundner-Culemann, K., Kumar, C., Gruss, O. J. et al., Combination of chemical genetics and phosphoproteomics for kinase signaling analysis enables confident identification of cellular downstream targets. Mol. Cell. Proteomics 2012, 11, O111 012351.
31. Robitaille, A. M., Christen, S., Shimobayashi, M., Cornu, M. et al., Quantitative phosphoproteomics reveal mTORC1 activates de novo pyrimidine synthesis. Science 2013, 339, 1320-1323.
32. Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A. et al., Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 2011, 10, 1794-1805.
33. Craig, R., Beavis, R. C., TANDEM: matching proteins with tandem mass spectra. Bioinformatics 2004, 20, 1466-1467.
34. Perkins, D. N., Pappin, D. J., Creasy, D. M., Cottrell, J. S., Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20, 3551-3567.
35. Frank, A., Pevzner, P., PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal. Chem. 2005, 77, 964-973.
36. Ma, B., Zhang, K., Hendrie, C., Liang, C. et al., PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun. Mass Spectrom. 2003, 17, 2337-2342.
37. Jeong, K., Kim, S., Pevzner, P. A., UniNovo: a universal tool for de novo peptide sequencing. Bioinformatics 2013, 29, 1953-1962.
38. Hu, Y., Lam, H., Expanding tandem mass spectral libraries of phosphorylated peptides: advances and applications. J. Proteome Res. 2013, 12, 5971-5977.
39. Eng, J. K., Searle, B. C., Clauser, K. R., Tabb, D. L., A face in the crowd: recognizing peptides through database search. Mol. Cell. Proteomics 2011, 10, R111 009522.
40. Kall, L., Storey, J. D., Noble, W. S., QVALITY: non-parametric estimation of q-values and posterior error probabilities. Bioinformatics 2009, 25, 964-966.
41. Kall, L., Storey, J. D., MacCoss, M. J., Noble, W. S., Posterior error probabilities and false discovery rates: two sides of the same coin. J. Proteome Res. 2008, 7, 40-44.
42. Elias, J. E., Gygi, S. P., Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 2007, 4, 207-214.
43. Higdon, R., Hogan, J. M., Van Belle, G., Kolker, E., Randomized sequence databases for tandem mass spectrometry peptide and protein identification. Omics 2005, 9, 364-379.
44. Blanco, L., Mead, J. A., Bessant, C., Comparison of novel decoy database designs for optimizing protein identification searches using ABRF sPRG2006 standard MS/MS data sets. J. Proteome Res. 2009, 8, 1782-1791.
45. Jeong, K., Kim, S., Bandeira, N., False discovery rates in spectral identification. BMC Bioinformatics 2012, 13(Suppl 16), S2.
46. Fu, Y., Qian, X., Transferred subgroup false discovery rate for rare post-translational modifications detected by mass spectrometry. Mol. Cell. Proteomics 2014, 13, 1359-1368.
47. Fu, Y., Bayesian false discovery rates for post-translational modification proteomics. Stat. Interface 2012, 5, 47-59.
48. Heemskerk, A. A., Busnel, J. M., Schoenmaker, B., Derks, R. J. et al., Ultra-low flow electrospray ionization-mass spectrometry for improved ionization efficiency in phosphoproteomics. Anal. Chem. 2012, 84, 4552-4559.
49. Palumbo, A. M., Smith, S. A., Kalcic, C. L., Dantus, M. et al., Tandem mass spectrometry strategies for phosphoproteome analysis. Mass Spectrom. Rev. 2011, 30, 600-625.
50. Tholey, A., Reed, J., Lehmann, W. D., Electrospray tandem mass spectrometric studies of phosphopeptides and phosphopeptide analogues. J. Mass Spectrom. 1999, 34, 117-123.
51. Beausoleil, S. A., Jedrychowski, M., Schwartz, D., Elias, J. E. et al., Large-scale characterization of HeLa cell nuclear phosphoproteins. Proc. Natl. Acad. Sci. USA 2004, 101, 12130-12135.
52. Dephoure, N., Gould, K. L., Gygi, S. P., Kellogg, D. R., Mapping and analysis of phosphorylation sites: a quick guide for cell biologists. Mol. Biol. Cell 2013, 24, 535-542.
53. Boersema, P. J., Mohammed, S., Heck, A. J., Phosphopeptide fragmentation and analysis by mass spectrometry. J. Mass Spectrom. 2009, 44, 861-878.
54. Schroeder, M. J., Shabanowitz, J., Schwartz, J. C., Hunt, D. F., Coon, J. J., A neutral loss activation method for improved phosphopeptide sequence analysis by quadrupole ion trap mass spectrometry. Anal. Chem. 2004, 76, 3590-3598.
55. Ulintz, P. J., Yocum, A. K., Bodenmiller, B., Aebersold, R. et al., Comparison of MS(2)-only, MSA, and MS(2)/MS(3) methodologies for phosphopeptide identification. J. Proteome Res. 2009, 8, 887-899.
56. Olsen, J. V., Macek, B., Lange, O., Makarov, A. et al., Higher-energy C-trap dissociation for peptide modification analysis. Nat. Methods 2007, 4, 709-712.
57. Nagaraj, N., D'Souza, R. C., Cox, J., Olsen, J. V., Mann, M., Feasibility of large-scale phosphoproteomics with higher energy collisional dissociation fragmentation. J. Proteome Res. 2010, 9, 6786-6794.
58. Savitski, M. M., Lemeer, S., Boesche, M., Lang, M. et al., Confident phosphorylation site localization using the Mascot Delta Score. Mol. Cell. Proteomics 2011, 10, M110 003830.
59. Jedrychowski, M. P., Huttlin, E. L., Haas, W., Sowa, M. E. et al., Evaluation of HCD- and CID-type fragmentation within their respective detection platforms for murine phosphoproteomics. Mol. Cell. Proteomics 2011, 10, M111 009910.
60. Syka, J. E., Coon, J. J., Schroeder, M. J., Shabanowitz, J., Hunt, D. F., Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. Proc. Natl. Acad. Sci. USA 2004, 101, 9528-9533.
61. Grimsrud, P. A., Swaney, D. L., Wenger, C. D., Beauchene, N. A., Coon, J. J., Phosphoproteomics for the masses. ACS Chem. Biol. 2010, 5, 105-119.
62. Kim, M. S., Pandey, A., Electron transfer dissociation mass spectrometry in proteomics. Proteomics 2012, 12, 530-542.
63. Good, D. M., Wenger, C. D., McAlister, G. C., Bai, D. L. et al., Post-acquisition ETD spectral processing for increased peptide identifications. J. Am. Soc. Mass Spectrom. 2009, 20, 1435-1440.
64. Good, D. M., Wenger, C. D., Coon, J. J., The effect of interfering ions on search algorithm performance for electron-transfer dissociation data. Proteomics 2010, 10, 164-167.
65. Sadygov, R. G., Good, D. M., Swaney, D. L., Coon, J. J., A new probabilistic database search algorithm for ETD spectra. J. Proteome Res. 2009, 8, 3198-3205.
66. Baker, P. R., Medzihradszky, K. F., Chalkley, R. J., Improving software performance for peptide electron transfer dissociation data analysis by implementation of charge state- and sequence-dependent scoring. Mol. Cell. Proteomics 2010, 9, 1795-1803.
67. Molina, H., Horn, D. M., Tang, N., Mathivanan, S., Pandey, A., Global proteomic profiling of phosphopeptides using electron transfer dissociation tandem mass spectrometry. Proc. Natl. Acad. Sci. USA 2007, 104, 2199-2204.
68. Zhong, J., Molina, H., Pandey, A., Phosphoproteomics. Curr. Protoc. Protein Sci. 2007, 50, 24.4:24.4.1-24.4.21.
69. Swaney, D. L., Wenger, C. D., Thomson, J. A., Coon, J. J., Human embryonic stem cell phosphoproteome revealed by electron transfer dissociation tandem mass spectrometry. Proc. Natl. Acad. Sci. USA 2009, 106, 995-1000.
70. Swaney, D. L., McAlister, G. C., Wirtala, M., Schwartz, J. C. et al., Supplemental activation method for high-efficiency electron-transfer dissociation of doubly protonated peptide precursors. Anal. Chem. 2007, 79, 477-485.
71. Marx, H., Lemeer, S., Schliep, J. E., Matheron, L. et al., A large synthetic peptide and phosphopeptide reference library for mass spectrometry-based proteomics. Nat. Biotechnol. 2013, 31, 557-564.
72. Guthals, A., Bandeira, N., Peptide identification by tandem mass spectrometry with alternate fragmentation modes. Mol. Cell. Proteomics 2012, 11, 550-557.
73. Kim, M. S., Zhong, J., Kandasamy, K., Delanghe, B., Pandey, A., Systematic evaluation of alternating CID and ETD fragmentation for phosphorylated peptides. Proteomics 2011, 11, 2568-2572.
74. Searle, B. C., Turner, M., Nesvizhskii, A. I., Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies. J. Proteome Res. 2008, 7, 245-253.
75. Jones, A. R., Siepen, J. A., Hubbard, S. J., Paton, N. W., Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines. Proteomics 2009, 9, 1220-1229.
76. Shteynberg, D., Deutsch, E. W., Lam, H., Eng, J. K. et al., iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell. Proteomics 2011, 10, M111 007690.
77. Soderholm, S., Hintsanen, P., Ohman, T., Aittokallio, T., Nyman, T. A., PhosFox: a bioinformatics tool for peptide-level processing of LC-MS/MS-based phosphoproteomic data. Proteome Sci. 2014, 12, 36.
78. Chalkley, R. J., Clauser, K. R., Modification site localization scoring: strategies and performance. Mol. Cell. Proteomics 2012, 11, 3-14.
79. Olsen, J. V., Mann, M., Improved peptide identification in proteomics by two consecutive stages of mass spectrometric fragmentation. Proc. Natl. Acad. Sci. USA 2004, 101, 13417-13422.
80. Beausoleil, S. A., Villen, J., Gerber, S. A., Rush, J., Gygi, S. P., A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 2006, 24, 1285-1292.
81. Bailey, C. M., Sweet, S. M., Cunningham, D. L., Zeller, M. et al., SLoMo: automated site localization of modifications from ETD/ECD mass spectra. J. Proteome Res. 2009, 8, 1965-1971.
82. Taus, T., Kocher, T., Pichler, P., Paschke, C. et al., Universal and confident phosphorylation site localization using phosphoRS. J. Proteome Res. 2011, 10, 5354-5362.
83. Baker, P. R., Trinidad, J. C., Chalkley, R. J., Modification site localization scoring integrated into a search engine. Mol. Cell. Proteomics 2011, 10, M111 008078.
84. Vaudel, M., Breiter, D., Beck, F., Rahnenfuhrer, J. et al., D-score: a search engine independent MD-score. Proteomics 2013, 13, 1036-1041.
85. Kwon, T., Choi, H., Vogel, C., Nesvizhskii, A. I., Marcotte, E. M., MSblender: a probabilistic approach for integrating peptide identifications from multiple database search engines. J. Proteome Res. 2011, 10, 2949-2958.
86. Fermin, D., Walmsley, S. J., Gingras, A. C., Choi, H., Nesvizhskii, A. I., LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach. Mol. Cell. Proteomics 2013, 12, 3409-3419.
87. Knudsen, G. M., Chalkley, R. J., The effect of using an inappropriate protein database for proteomic data analysis. PLoS One 2011, 6, e20873.
88. Blakeley, P., Overton, I. M., Hubbard, S. J., Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies. J. Proteome Res. 2012, 11, 5221-5234.
89. Deutsch, E. W., Mendoza, L., Shteynberg, D., Farrah, T. et al., A guided tour of the trans-proteomic pipeline. Proteomics 2010, 10, 1150-1159.
90. Palumbo, A. M., Reid, G. E., Evaluation of gas-phase rearrangement and competing fragmentation reactions on protein phosphorylation site assignment using collision induced dissociation-MS/MS and MS3. Anal. Chem. 2008, 80, 9735-9747.
91. Aguiar, M., Haas, W., Beausoleil, S. A., Rush, J., Gygi, S. P., Gas-phase rearrangements do not affect site localization reliability in phosphoproteomics data sets. J. Proteome Res. 2010, 9, 3103-3107.
92. Courcelles, M., Bridon, G., Lemieux, S., Thibault, P., Occurrence and detection of phosphopeptide isomers in large-scale phosphoproteomics experiments. J. Proteome Res. 2012, 11, 3753-3765.
93. Deutsch, E. W., File formats commonly used in mass spectrometry proteomics. Mol. Cell. Proteomics 2012, 11, 1612-1621.
94. Barsnes, H., Vaudel, M., Colaert, N., Helsens, K. et al., Compomics-utilities: an open-source Java library for computational proteomics. BMC Bioinformatics 2011, 12, 70.
95. Cox, J., Mann, M., MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367-1372.
96. Martens, L., Chambers, M., Sturm, M., Kessner, D. et al., mzML-a community standard for mass spectrometry data. Mol. Cell. Proteomics 2011, 10, R110 000133.
97. Jones, A. R., Eisenacher, M., Mayer, G., Kohlbacher, O. et al., The mzIdentML data standard for mass spectrometry-based proteomics results. Mol. Cell. Proteomics 2012, 11, M111 014381.
98. Walzer, M., Qi, D., Mayer, G., Uszkoreit, J. et al., The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics. Mol. Cell. Proteomics 2013, 12, 2332-2340.
99. Vizcaino, J. A., Deutsch, E. W., Wang, R., Csordas, A. et al., ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 2014, 32, 223-226.
100. Seymour, S. L., Farrah, T., Binz, P.-A., Chalkley, R. J. et al., A standardized framing for reporting protein identificaitons in mzIdentML 1.2. Proteomics 2014, 14, 2389-2399.
101. Ghali, F., Krishna, R., Lukasse, P., Martinez-Bartolome, S. et al., Tools (Viewer, Library and Validator) that facilitate use of the peptide and protein identification standard format, termed mzIdentML. Mol. Cell. Proteomics 2013, 12, 3026-3035.
102. Gonzalez-Galarza, F. F., Qi, D., Fan, J., Bessant, C., Jones, A. R., A tutorial for software development in quantitative proteomics using PSI standard formats. Biochim. Biophys. Acta 2014, 1844, 88-97.
103. Griss, J., Jones, A. R., Sachsenberg, T., Walzer, M. et al., The mzTab Data Exchange Format: communicating MS-based proteomics and metabolomics experimental results to a wider audience. Mol. Cell. Proteomics 2014, 13, 2765-2775.
104. Ruttenberg, B. E., Pisitkun, T., Knepper, M. A., Hoffert, J. D., PhosphoScore: an open-source phosphorylation site assignment tool for MSn data. J. Proteome Res. 2008, 7, 3054-3059.
105. Wan, Y., Cripps, D., Thomas, S., Campbell, P. et al., PhosphoScan: a probability-based method for phosphorylation site prediction using MS2/MS3 pair information. J. Proteome Res. 2008, 7, 2803-2811.
106. Saeed, F., Pisitkun, T., Hoffert, J. D., Rashidian, S. et al., PhosSA: Fast and accurate phosphorylation site assignment algorithm for mass spectrometry data. Proteome Sci. 2013, 11, S14.