Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data

Bioinformatics

Volumn 32, Issue 2, 2016, Pages 252-259

  Yang, Pengyi ^a   Humphrey, Sean J ^b   James, David E ^c   Yang, Yee Hwa ^d   Jothi, Raja ^a  

^a NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES   (United States)

^b MAX PLANCK INSTITUTE OF BIOCHEMISTRY   (Germany)

^c UNIVERSITY OF SYDNEY   (Australia)

^d UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

INSULIN; PHOSPHOPROTEIN; PROTEIN KINASE; PROTEOME;

ENZYME SPECIFICITY; HUMAN; MASS SPECTROMETRY; METABOLISM; PHOSPHORYLATION; PROCEDURES; PROTEIN DATABASE; PROTEIN PROCESSING; PROTEOMICS; SIGNAL TRANSDUCTION;

DATABASES, PROTEIN; HUMANS; INSULIN; MASS SPECTROMETRY; PHOSPHOPROTEINS; PHOSPHORYLATION; PROTEIN KINASES; PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEOME; PROTEOMICS; SIGNAL TRANSDUCTION; SUBSTRATE SPECIFICITY;

EID: 84949590508     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv550     Document Type: Article

References (40)

1. Amanchy, R. et al. (2007) A curated compendium of phosphorylation motifs. Nat. Biotechnol., 25, 285-286.
2. Chang, C.-C. And Lin, C.-J. (2011) LibSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST), 2, 27.
3. Choudhary, C. And Mann, M. (2010) Decoding signalling networks by mass spectrometry-based proteomics. Nat. Rev. Mol. Cell Biol., 11, 427-439.
4. Dang, T. H. et al. (2008) Prediction of kinase-specific phosphorylation sites using conditional random fields. Bioinformatics, 24, 2857-2864.
5. Dinkel, H. et al. (2011) Phospho. elm: A database of phosphorylation sitesupdate 2011. Nucleic Acids Res., 39(suppl 1), D261-D267.
6. Elkan, C. And Noto, K. (2008) Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, Las Vegas, Nevada, USA, pp 213-220.
7. Erickson, B. K. et al. (2014) Evaluating multiplexed quantitative phosphopeptide analysis on a hybrid quadrupole mass filter/linear ion trap/orbitrap mass spectrometer. Anal. Chem., 87, 1241-1249
8. Gao, J. et al. (2010) Musite, a tool for global prediction of general and kinasespecific phosphorylation sites. Mol. Cell. Proteomics, 9, 2586-2600.
9. Gentleman, R. et al. (2005) Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer, New York.
10. Hjerrild, M. et al. (2004) Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry. J. Proteome Res., 3, 426-433.
11. Horn, H. et al. (2014) Kinomexplorer: An integrated platform for kinome biology studies. Nat. Methods, 11, 603-604.
12. Hornbeck, P. V. et al. (2011) PhosphoSitePlus: A comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications inman andmouse. Nucleic Acids Res., 40, D261-D270.
13. Huang, H.-D. et al. (2005) Kinasephos: A web tool for identifying protein kinasespecific phosphorylation sites. Nucleic Acids Res., 33(suppl 2), W226-W229.
14. Humphrey, S. et al. (2015) High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat. Biotechnol, 33, 990-995.
15. Humphrey, S. J. And James, D. E. (2012) Uncaging akt. Sci. Signal., 5, pe20.
16. Humphrey, S. J. et al. (2013) Dynamic adipocyte phosphoproteome reveals that akt directly regulates mtorc2. Cell Metab., 17, 1009-1020.
17. Hunter, T. (1995). Protein kinases and phosphatases: The yin and yang of protein phosphorylation and signaling. Cell, 80, 225-236.
18. Kim, J. H. et al. (2004) Prediction of phosphorylation sites using SVMs. Bioinformatics, 20, 3179-3184.
19. Laplante, M. And Sabatini, D. M. (2012) mTor signaling in growth control and disease. Cell, 149, 274-293.
20. Lemmon, M. A. And Schlessinger, J. (2010) Cell signaling by receptor tyrosine kinases. Cell, 141, 1117-1134.
21. Letouzey, F. et al. (2000) Learning from positive and unlabeled examples. In: Arimura, H. et al. (eds.), Algorithmic Learning Theory. Springer, Berlin Heidelberg, pp. 71-84.
22. Linding, R. et al. (2007) Systematic discovery of in vivo phosphorylation networks. Cell, 129, 1415-1426.
23. Miller, M. L. And Blom, N. (2009) Kinase-specific prediction of protein phosphorylation sites. In: de Graauw, M. (ed), Phospho-Proteomics. Springer, pp. 299-310.
24. Miller, M. L. et al. (2008) Linear motif atlas for phosphorylation-dependent signaling. Sci. Signal., 1, ra2.
25. Obenauer, J. C. et al. (2003) Scansite 2. 0: proteome-wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Res., 31, 3635-3641.
26. Oliveira, A. P. et al. (2015) Dynamic phosphoproteomics reveals torc1-dependent regulation of yeast nucleotide and amino acid biosynthesis. Sci. Signal., 8, rs4.
27. Olsen, J. V. AndMann, M. (2013) Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol. Cell. Proteomics, 12, 3444-3452.
28. Olsen, J. V. et al. (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell, 127, 635-648.
29. Sabido, E. et al. (2012) Mass spectrometry-based proteomics for systems biology. Curr. Opin. Biotechnol., 23, 591-597.
30. Song, C. et al. (2012) Systematic analysis of protein phosphorylation networks from phosphoproteomic data. Mol. Cell. Proteomics, 11, 1070-1083.
31. Tang, Y. et al. (2009) SVMs modeling for highly imbalanced classification. IEEE Trans. Syst. Man Cybern. B Cybern., 39, 281-288.
32. Thomsen, M. C. F. And Nielsen, M. (2012) Seq2logo: A method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion. Nucleic Acids Res., 40, W281-W287.
33. Trost, B. And Kusalik, A. (2011) Computational prediction of eukaryotic phosphorylation sites. Bioinformatics, 27, 2927-2935.
34. Trost, B. And Kusalik, A. (2013) Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights. Bioinformatics, 29, 686-694.
35. Wong, Y.-H. et al. (2007) Kinasephos 2. 0: A web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns. Nucleic Acids Res., 35(suppl 2), W588-W594.
36. Xue, Y. et al. (2008) Gps 2. 0, a tool to predict kinase-specific phosphorylation sites in hierarchy. Mol. Cell. Proteomics, 7, 1598-1608.
37. Xue, Y. et al. (2010) Gps 2. 1: enhanced prediction of kinase-specific phosphorylation sites with an algorithm of motif length selection. Protein Eng. Des. Sel, 24, 255-260.
38. Yaffe, M. B. et al. (2001) A motif-based profile scanning approach for genomewide prediction of signaling pathways. Nat. Biotechnol., 19, 348-353.
39. Yang, P. et al. (2014) Sample subset optimization techniques for imbalanced and ensemble learning problems in bioinformatics applications. IEEE Trans. Cybern., 44, 445-455.
40. Yang, P. et al. (2015) Knowledge-based analysis for detecting key signaling events from time-series phosphoproteomics data. PLoS Comput. Biol., 11, e1004403.