From pathways to networks: Connecting dots by establishing protein-protein interaction networks in signaling pathways using affinity purification and mass spectrometry

Proteomics

Volumn 15, Issue 2-3, 2015, Pages 188-202

  Li, Xu ^a   Wang, Wenqi ^a   Chen, Junjie ^a  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Affinity purification; Animal proteomics; Hippo signaling; Mass spectrometry; Protein protein interaction; Signaling pathways

Indexed keywords

HIPPO PROTEIN; LOW DENSITY LIPOPROTEIN RECEPTOR RELATED PROTEIN; LYMPHOID ENHANCER FACTOR 1; MERLIN; PROTEIN; UNCLASSIFIED DRUG; WNT PROTEIN; PROTEIN SERINE THREONINE KINASE;

CLINICAL RESEARCH; DATA ANALYSIS; DNA DAMAGE; MASS SPECTROMETRY; NONHUMAN; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; PROTEIN PURIFICATION; PROTEOMICS; REVIEW; SIGNAL TRANSDUCTION; WNT SIGNALING PATHWAY; AFFINITY CHROMATOGRAPHY; ANIMAL; HUMAN; METABOLISM; PROCEDURES; PROTEIN ANALYSIS;

ANIMALIA;

ANIMALS; CHROMATOGRAPHY, AFFINITY; HUMANS; MASS SPECTROMETRY; PROTEIN INTERACTION MAPPING; PROTEIN INTERACTION MAPS; PROTEIN-SERINE-THREONINE KINASES; PROTEINS; PROTEOMICS; SIGNAL TRANSDUCTION;

EID: 84921273143     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201400147     Document Type: Review

References (145)

1. Derynck, R., Zhang, Y. E., Smad-dependent and Smad-independent pathways in TGF-beta family signalling. Nature 2003, 425, 577-584.
2. Aebersold, R., Mann, M., Mass spectrometry-based proteomics. Nature 2003, 422, 198-207.
3. Matthiesen, R., Azevedo, L., Amorim, A., Carvalho, A. S., Discussion on common data analysis strategies used in MS-based proteomics. Proteomics 2011, 11, 604-619.
4. Wolters, D. A., Washburn, M. P., Yates, J. R., 3rd. An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 2001, 73, 5683-5690.
5. Domon, B., Aebersold, R., Mass spectrometry and protein analysis. Science 2006, 312, 212-217.
6. Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F. et al., Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 1999, 17, 994-999.
7. Ong, S. E., Blagoev, B., Kratchmarova, I., Kristensen, D. B. et al., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 2002, 1, 376-386.
8. Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B. et al., Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 2004, 3, 1154-1169.
9. Dayon, L., Hainard, A., Licker, V., Turck, N. et al., Relative quantification of proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal. Chem. 2008, 80, 2921-2931.
10. Thompson, A., Schafer, J., Kuhn, K., Kienle, S. et al., Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 2003, 75, 1895-1904.
11. Ting, L., Rad, R., Gygi, S. P., Haas, W., MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 2011, 8, 937-940.
12. Choe, L., D'Ascenzo, M., Relkin, N. R., Pappin, D. et al., 8-plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer's disease. Proteomics 2007, 7, 3651-3660.
13. Murphy, J. P., Everley, R. A., Coloff, J. L., Gygi, S. P., Combining amine metabolomics and quantitative proteomics of cancer cells using derivatization with isobaric tags. Anal. Chem. 2014, 86, 3585-3593.
14. Everley, R. A., Kunz, R. C., McAllister, F. E., Gygi, S. P., Increasing throughput in targeted proteomics assays: 54-plex quantitation in a single mass spectrometry run. Anal. Chem. 2013, 85, 5340-5346.
15. Altelaar, A. F., Frese, C. K., Preisinger, C., Hennrich, M. L. et al., Benchmarking stable isotope labeling based quantitative proteomics. J. Proteomics 2013, 88, 14-26.
16. Karp, N. A., Huber, W., Sadowski, P. G., Charles, P. D. et al., Addressing accuracy and precision issues in iTRAQ quantitation. Mol. Cell. Proteomics 2010, 9, 1885-1897.
17. Shirran, S. L., Botting, C. H., A comparison of the accuracy of iTRAQ quantification by nLC-ESI MSMS and nLC-MALDI MSMS methods. J. Proteomics 2010, 73, 1391-1403.
18. Kohlbacher, O., Reinert, K., Gropl, C., Lange, E. et al., TOPP-the OpenMS proteomics pipeline. Bioinformatics 2007, 23, e191-e197.
19. Rost, H. L., Schmitt, U., Aebersold, R., Malmstrom, L., pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics 2014, 14, 74-77.
20. 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.
21. Cox, J., Matic, I., Hilger, M., Nagaraj, N. et al., A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat. Protoc. 2009, 4, 698-705.
22. Matthiesen, R., Virtual expert mass spectrometrist v3.0: an integrated tool for proteome analysis. Methods Mol. Biol. 2007, 367, 121-138.
23. Rodriguez-Suarez, E., Gubb, E., Alzueta, I. F., Falcon-Perez, J. M. et al., Virtual expert mass spectrometrist: iTRAQ tool for database-dependent search, quantitation and result storage. Proteomics 2010, 10, 1545-1556.
24. de Godoy, L. M., Olsen, J. V., Cox, J., Nielsen, M. L. et al., Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 2008, 455, 1251-1254.
25. Raso, C., Cosentino, C., Gaspari, M., Malara, N. et al., Characterization of breast cancer interstitial fluids by TmT labeling, LTQ-Orbitrap Velos mass spectrometry, and pathway analysis. J. Proteome Res. 2012, 11, 3199-3210.
26. Dai, L., He, J., Liu, Y., Byun, J. et al., Dose-dependent proteomic analysis of glioblastoma cancer stem cells upon treatment with gamma-secretase inhibitor. Proteomics 2011, 11, 4529-4540.
27. Chen, Y. S., Mathias, R. A., Mathivanan, S., Kapp, E. A. et al., Proteomics profiling of Madin-Darby canine kidney plasma membranes reveals Wnt-5a involvement during oncogenic H-Ras/TGF-beta-mediated epithelial-mesenchymal transition. Mol. Cell. Proteomics 2011, 10, M110 001131.
28. Kim, Y. M., Kim, J., Heo, S. C., Shin, S. H. et al., Proteomic identification of ADAM12 as a regulator for TGF-beta1-induced differentiation of human mesenchymal stem cells to smooth muscle cells. PLoS One 2012, 7, e40820.
29. Ali, N. A., McKay, M. J., Molloy, M. P., Proteomics of Smad4 regulated transforming growth factor-beta signalling in colon cancer cells. Mol. Biosyst. 2010, 6, 2332-2338.
30. Macek, B., Mann, M., Olsen, J. V., Global and site-specific quantitative phosphoproteomics: principles and applications. Annu. Rev. Pharmacol. Toxicol. 2009, 49, 199-221.
31. Nita-Lazar, A., Saito-Benz, H., White, F. M., Quantitative phosphoproteomics by mass spectrometry: past, present, and future. Proteomics 2008, 8, 4433-4443.
32. Harsha, H. C., Pinto, S. M., Pandey, A., Proteomic strategies to characterize signaling pathways. Methods Mol. Biol. 2013, 1007, 359-377.
33. Kirkpatrick, D. S., Denison, C., Gygi, S. P., Weighing in on ubiquitin: the expanding role of mass-spectrometry-based proteomics. Nat. Cell Biol. 2005, 7, 750-757.
34. Shi, Y., Xu, P., Qin, J., Ubiquitinated proteome: ready for global? Mol. Cell. Proteomics 2011, 10, R110 006882.
35. Mischerikow, N., Heck, A. J., Targeted large-scale analysis of protein acetylation. Proteomics 2011, 11, 571-589.
36. Afjehi-Sadat, L., Garcia, B. A., Comprehending dynamic protein methylation with mass spectrometry. Curr. Opin. Chem. Biol. 2013, 17, 12-19.
37. Pan, S., Chen, R., Aebersold, R., Brentnall, T. A., Mass spectrometry based glycoproteomics-from a proteomics perspective. Mol. Cell. Proteomics 2011, 10, R110 003251.
38. Matsuoka, S., Ballif, B. A., Smogorzewska, A., McDonald, E. R., 3rd et al., ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage. Science 2007, 316, 1160-1166.
39. 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.
40. Ali, N. A., Molloy, M. P., Quantitative phosphoproteomics of transforming growth factor-beta signaling in colon cancer cells. Proteomics 2011, 11, 3390-3401.
41. Tang, L. Y., Deng, N., Wang, L. S., Dai, J. et al., Quantitative phosphoproteome profiling of Wnt3a-mediated signaling network: indicating the involvement of ribonucleoside-diphosphate reductase M2 subunit phosphorylation at residue serine 20 in canonical Wnt signal transduction. Mol. Cell. Proteomics 2007, 6, 1952-1967.
42. Kruger, M., Kratchmarova, I., Blagoev, B., Tseng, Y. H. et al., Dissection of the insulin signaling pathway via quantitative phosphoproteomics. Proc. Natl. Acad. Sci. USA 2008, 105, 2451-2456.
43. Amanchy, R., Zhong, J., Molina, H., Chaerkady, R. et al., Identification of c-Src tyrosine kinase substrates using mass spectrometry and peptide microarrays. J. Proteome Res. 2008, 7, 3900-3910.
44. Willert, K., Brown, J. D., Danenberg, E., Duncan, A. W. et al., Wnt proteins are lipid-modified and can act as stem cell growth factors. Nature 2003, 423, 448-452.
45. Moren, A., Hellman, U., Inada, Y., Imamura, T. et al., Differential ubiquitination defines the functional status of the tumor suppressor Smad4. J. Biol. Chem. 2003, 278, 33571-33582.
46. Matsuura, A., Ito, M., Sakaidani, Y., Kondo, T. et al., O-linked N-acetylglucosamine is present on the extracellular domain of notch receptors. J. Biol. Chem. 2008, 283, 35486-35495.
47. Schwartz, D., Gygi, S. P., An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat. Biotechnol. 2005, 23, 1391-1398.
48. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D. et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 2000, 25, 25-29.
49. Haw, R., Hermjakob, H., D'Eustachio, P., Stein, L., Reactome pathway analysis to enrich biological discovery in proteomics data sets. Proteomics 2011, 11, 3598-3613.
50. Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M. et al., STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013, 41, D808-D815.
51. Olsen, J. V., Mann, M., Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol. Cell. Proteomics 2013, 12, 3444-3452.
52. Berggard, T., Linse, S., James, P., Methods for the detection and analysis of protein-protein interactions. Proteomics 2007, 7, 2833-2842.
53. Altelaar, A. F., Munoz, J., Heck, A. J., Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 2013, 14, 35-48.
54. Huen, M. S., Grant, R., Manke, I., Minn, K. et al., RNF8 transduces the DNA-damage signal via histone ubiquitylation and checkpoint protein assembly. Cell 2007, 131, 901-914.
55. Liu, T., Ghosal, G., Yuan, J., Chen, J., Huang, J., FAN1 acts with FANCI-FANCD2 to promote DNA interstrand cross-link repair. Science 2010, 329, 693-696.
56. Yu, X., Chini, C. C., He, M., Mer, G., Chen, J., The BRCT domain is a phospho-protein binding domain. Science 2003, 302, 639-642.
57. Lou, Z., Minter-Dykhouse, K., Wu, X., Chen, J., MDC1 is coupled to activated CHK2 in mammalian DNA damage response pathways. Nature 2003, 421, 957-961.
58. Kim, J. E., Chen, J., Lou, Z., DBC1 is a negative regulator of SIRT1. Nature 2008, 451, 583-586.
59. Huen, M. S., Sy, S. M., Chen, J., BRCA1 and its toolbox for the maintenance of genome integrity. Nat. Rev. Mol. Cell Biol. 2010, 11, 138-148.
60. Knuesel, M., Wan, Y., Xiao, Z., Holinger, E. et al., Identification of novel protein-protein interactions using a versatile mammalian tandem affinity purification expression system. Mol. Cell. Proteomics 2003, 2, 1225-1233.
61. Luo, Q., Nieves, E., Kzhyshkowska, J., Angeletti, R. H., Endogenous transforming growth factor-beta receptor-mediated Smad signaling complexes analyzed by mass spectrometry. Mol. Cell. Proteomics 2006, 5, 1245-1260.
62. Major, M. B., Camp, N. D., Berndt, J. D., Yi, X. et al., Wilms tumor suppressor WTX negatively regulates WNT/beta-catenin signaling. Science 2007, 316, 1043-1046.
63. Brown, K. A., Ham, A. J., Clark, C. N., Meller, N. et al., Identification of novel Smad2 and Smad3 associated proteins in response to TGF-beta1. J. Cell. Biochem. 2008, 105, 596-611.
64. Conrotto, P., Yakymovych, I., Yakymovych, M., Souchelnytskyi, S., Interactome of transforming growth factor-beta type I receptor (TbetaRI): inhibition of TGFbeta signaling by Epac1. J. Proteome Res. 2007, 6, 287-297.
65. Chaudhry, S. S., Cain, S. A., Morgan, A., Dallas, S. L. et al., Fibrillin-1 regulates the bioavailability of TGFbeta1. J. Cell Biol. 2007, 176, 355-367.
66. Angers, S., Thorpe, C. J., Biechele, T. L., Goldenberg, S. J. et al., The KLHL12-Cullin-3 ubiquitin ligase negatively regulates the Wnt-beta-catenin pathway by targeting Dishevelled for degradation. Nat. Cell Biol. 2006, 8, 348-357.
67. Hilger, M., Mann, M., Triple SILAC to determine stimulus specific interactions in the Wnt pathway. J. Proteome Res. 2012, 11, 982-994.
68. Gavin, A. C., Bosche, M., Krause, R., Grandi, P. et al., Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002, 415, 141-147.
69. Ho, Y., Gruhler, A., Heilbut, A., Bader, G. D. et al., Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002, 415, 180-183.
70. Krogan, N. J., Cagney, G., Yu, H., Zhong, G. et al., Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 2006, 440, 637-643.
71. Guruharsha, K. G., Rual, J. F., Zhai, B., Mintseris, J. et al., A protein complex network of Drosophila melanogaster. Cell 2011, 147, 690-703.
72. Malovannaya, A., Lanz, R. B., Jung, S. Y., Bulynko, Y. et al., Analysis of the human endogenous coregulator complexome. Cell 2011, 145, 787-799.
73. Giot, L., Bader, J. S., Brouwer, C., Chaudhuri, A. et al., A protein interaction map of Drosophila melanogaster. Science 2003, 302, 1727-1736.
74. Li, S., Armstrong, C. M., Bertin, N., Ge, H. et al., A map of the interactome network of the metazoan C. elegans. Science 2004, 303, 540-543.
75. Sardiu, M. E., Cai, Y., Jin, J., Swanson, S. K. et al., Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics. Proc. Natl. Acad. Sci. USA 2008, 105, 1454-1459.
76. Sowa, M. E., Bennett, E. J., Gygi, S. P., Harper, J. W., Defining the human deubiquitinating enzyme interaction landscape. Cell 2009, 138, 389-403.
77. Joshi, P., Greco, T. M., Guise, A. J., Luo, Y. et al., The functional interactome landscape of the human histone deacetylase family. Mol. Syst. Biol. 2013, 9, 672.
78. Behrends, C., Sowa, M. E., Gygi, S. P., Harper, J. W., Network organization of the human autophagy system. Nature 2010, 466, 68-76.
79. Gingras, A. C., Gstaiger, M., Raught, B., Aebersold, R., Analysis of protein complexes using mass spectrometry. Nat. Rev. Mol. Cell Biol. 2007, 8, 645-654.
80. Couzens, A. L., Knight, J. D., Kean, M. J., Teo, G. et al., Protein interaction network of the mammalian Hippo pathway reveals mechanisms of kinase-phosphatase interactions. Sci. Signal. 2013, 6, rs15.
81. Hauri, S., Wepf, A., van Drogen, A., Varjosalo, M. et al., Interaction proteome of human Hippo signaling: modular control of the co-activator YAP1. Mol. Syst. Biol. 2013, 9, 713.
82. Rigaut, G., Shevchenko, A., Rutz, B., Wilm, M. et al., A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 1999, 17, 1030-1032.
83. Rohila, J. S., Chen, M., Chen, S., Chen, J. et al., Protein-protein interactions of tandem affinity purification-tagged protein kinases in rice. Plant J. 2006, 46, 1-13.
84. Veraksa, A., Bauer, A., Artavanis-Tsakonas, S., Analyzing protein complexes in Drosophila with tandem affinity purification-mass spectrometry. Dev. Dyn. 2005, 232, 827-834.
85. Listgarten, J., Emili, A., Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol. Cell. Proteomics 2005, 4, 419-434.
86. Liu, H., Sadygov, R. G., Yates, J. R., 3rd., A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 2004, 76, 4193-4201.
87. Sarraf, S. A., Raman, M., Guarani-Pereira, V., Sowa, M. E. et al., Landscape of the PARKIN-dependent ubiquitylome in response to mitochondrial depolarization. Nature 2013, 496, 372-376.
88. Blagoev, B., Kratchmarova, I., Ong, S. E., Nielsen, M. et al., A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling. Nat. Biotechnol. 2003, 21, 315-318.
89. Kratchmarova, I., Blagoev, B., Haack-Sorensen, M., Kassem, M., Mann, M., Mechanism of divergent growth factor effects in mesenchymal stem cell differentiation. Science 2005, 308, 1472-1477.
90. Nesvizhskii, A. I., Vitek, O., Aebersold, R., Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 2007, 4, 787-797.
91. 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.
92. Kall, L., Canterbury, J. D., Weston, J., Noble, W. S., MacCoss, M. J., Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 2007, 4, 923-925.
93. Eng, J. K., McCormack, A. L., Yates, J. R., An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5, 976-989.
94. 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.
95. Fenyo, D., Beavis, R. C., A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes. Anal. Chem. 2003, 75, 768-774.
96. Choi, H., Larsen, B., Lin, Z. Y., Breitkreutz, A. et al., SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat. Methods 2011, 8, 70-73.
97. Foulds, C. E., Feng, Q., Ding, C., Bailey, S. et al., Proteomic analysis of coregulators bound to ERalpha on DNA and nucleosomes reveals coregulator dynamics. Mol. Cell 2013, 51, 185-199.
98. Christianson, J. C., Olzmann, J. A., Shaler, T. A., Sowa, M. E. et al., Defining human ERAD networks through an integrative mapping strategy. Nat. Cell Biol. 2012, 14, 93-105.
99. Breitkreutz, A., Choi, H., Sharom, J. R., Boucher, L. et al., A global protein kinase and phosphatase interaction network in yeast. Science 2010, 328, 1043-1046.
100. Kwon, Y., Vinayagam, A., Sun, X., Dephoure, N. et al., The Hippo signaling pathway interactome. Science 2013, 342, 737-740.
101. Wang, W., Li, X., Huang, J., Feng, L. et al., Defining the protein-protein interaction network of the human hippo pathway. Mol. Cell. Proteomics 2014, 13, 119-131.
102. Choi, H., Glatter, T., Gstaiger, M., Nesvizhskii, A. I., SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments. J. Proteome Res. 2012, 11, 2619-2124.
103. Teo, G., Liu, G., Zhang, J., Nesvizhskii, A. I. et al., SAINTexpress: Improvements and additional features in Significance Analysis of Interactome software. J. Proteomics 2014, 100, 37-43.
104. Mellacheruvu, D., Wright, Z., Couzens, A. L., Lambert, J. P. et al., The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nat. Methods 2013, 10, 730-736.
105. Varjosalo, M., Sacco, R., Stukalov, A., van Drogen, A. et al., Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS. Nat. Methods 2013, 10, 307-314.
106. Cline, M. S., Smoot, M., Cerami, E., Kuchinsky, A. et al., Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2007, 2, 2366-2382.
107. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S. et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498-2504.
108. Saito, R., Smoot, M. E., Ono, K., Ruscheinski, J. et al., A travel guide to Cytoscape plugins. Nat. Methods 2012, 9, 1069-1076.
109. Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B. et al., Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5, R80.
110. Yan, Y., Marriott, G., Analysis of protein interactions using fluorescence technologies. Curr. Opin. Chem. Biol. 2003, 7, 635-640.
111. Piehler, J., New methodologies for measuring protein interactions in vivo and in vitro. Curr. Opin. Struct. Biol. 2005, 15, 4-14.
112. Fields, S., Song, O., A novel genetic system to detect protein-protein interactions. Nature 1989, 340, 245-246.
113. Endoh, H., Walhout, A. J., Vidal, M., A green fluorescent protein-based reverse two-hybrid system: application to the characterization of large numbers of potential protein-protein interactions. Methods Enzymol. 2000, 328, 74-88.
114. von Mering, C., Huynen, M., Jaeggi, D., Schmidt, S. et al., STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003, 31, 258-261.
115. Stark, C., Breitkreutz, B. J., Reguly, T., Boucher, L. et al., BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006, 34, D535-D539.
116. Kerrien, S., Alam-Faruque, Y., Aranda, B., Bancarz, I. et al., IntAct-open source resource for molecular interaction data. Nucleic Acids Res. 2007, 35, D561-D565.
117. Bader, G. D., Betel, D., Hogue, C. W., BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 2003, 31, 248-250.
118. Xenarios, I., Rice, D. W., Salwinski, L., Baron, M. K. et al., DIP: the database of interacting proteins. Nucleic Acids Res. 2000, 28, 289-291.
119. Prasad, T. S., Kandasamy, K., Pandey, A., Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology. Methods Mol. Biol. 2009, 577, 67-79.
120. Kandasamy, K., Mohan, S. S., Raju, R., Keerthikumar, S. et al., NetPath: a public resource of curated signal transduction pathways. Genome Biol. 2010, 11, R3.
121. Bouwmeester, T., Bauch, A., Ruffner, H., Angrand, P. O. et al., A physical and functional map of the human TNF-alpha/NF-kappa B signal transduction pathway. Nat. Cell Biol. 2004, 6, 97-105.
122. Glatter, T., Schittenhelm, R. B., Rinner, O., Roguska, K. et al., Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin interaction proteome. Mol. Syst. Biol. 2011, 7, 547.
123. Justice, R. W., Zilian, O., Woods, D. F., Noll, M., Bryant, P. J., The Drosophila tumor suppressor gene warts encodes a homolog of human myotonic dystrophy kinase and is required for the control of cell shape and proliferation. Genes Dev. 1995, 9, 534-546.
124. Xu, T., Wang, W., Zhang, S., Stewart, R. A., Yu, W., Identifying tumor suppressors in genetic mosaics: the Drosophila lats gene encodes a putative protein kinase. Development 1995, 121, 1053-1063.
125. Kango-Singh, M., Nolo, R., Tao, C., Verstreken, P. et al., Shar-pei mediates cell proliferation arrest during imaginal disc growth in Drosophila. Development 2002, 129, 5719-5730.
126. Tapon, N., Harvey, K. F., Bell, D. W., Wahrer, D. C. et al., Salvador promotes both cell cycle exit and apoptosis in Drosophila and is mutated in human cancer cell lines. Cell 2002, 110, 467-478.
127. Harvey, K. F., Pfleger, C. M., Hariharan, I. K., The Drosophila Mst ortholog, hippo, restricts growth and cell proliferation and promotes apoptosis. Cell 2003, 114, 457-467.
128. Wu, S., Huang, J., Dong, J., Pan, D., Hippo encodes a Ste-20 family protein kinase that restricts cell proliferation and promotes apoptosis in conjunction with salvador and warts. Cell 2003, 114, 445-456.
129. Lai, Z. C., Wei, X., Shimizu, T., Ramos, E. et al., Control of cell proliferation and apoptosis by mob as tumor suppressor, mats. Cell 2005, 120, 675-685.
130. Huang, J., Wu, S., Barrera, J., Matthews, K., Pan, D., The Hippo signaling pathway coordinately regulates cell proliferation and apoptosis by inactivating Yorkie, the Drosophila Homolog of YAP. Cell 2005, 122, 421-434.
131. Hamaratoglu, F., Willecke, M., Kango-Singh, M., Nolo, R. et al., The tumour-suppressor genes NF2/Merlin and Expanded act through Hippo signalling to regulate cell proliferation and apoptosis. Nat. Cell Biol. 2006, 8, 27-36.
132. Pan, D., Hippo signaling in organ size control. Genes Dev. 2007, 21, 886-897.
133. Zhao, B., Lei, Q. Y., Guan, K. L., The Hippo-YAP pathway: new connections between regulation of organ size and cancer. Curr. Opin. Cell Biol. 2008, 20, 638-646.
134. Harvey, K. F., Zhang, X., Thomas, D. M., The Hippo pathway and human cancer. Nat. Rev. Cancer 2013, 13, 246-257.
135. Friedman, A. A., Tucker, G., Singh, R., Yan, D. et al., Proteomic and functional genomic landscape of receptor tyrosine kinase and ras to extracellular signal-regulated kinase signaling. Sci. Signal. 2011, 4, rs10.
136. Kyriakakis, P., Tipping, M., Abed, L., Veraksa, A., Tandem affinity purification in Drosophila: the advantages of the GS-TAP system. Fly (Austin) 2008, 2, 229-235.
137. Roux, K. J., Kim, D. I., Raida, M., Burke, B., A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J. Cell Biol. 2012, 196, 801-810.
138. Gandhi, T. K., Zhong, J., Mathivanan, S., Karthick, L. et al., Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat. Genet. 2006, 38, 285-293.
139. von Mering, C., Krause, R., Snel, B., Cornell, M. et al., Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002, 417, 399-403.
140. Moya, I. M., Halder, G., Discovering the Hippo pathway protein-protein interactome. Cell Res. 2014, 24, 137-138.
141. Figeys, D., McBroom, L. D., Moran, M. F., Mass spectrometry for the study of protein-protein interactions. Methods 2001, 24, 230-239.
142. McHugh, L., Arthur, J. W., Computational methods for protein identification from mass spectrometry data. PLoS Comput. Biol. 2008, 4, e12.
143. Bell, A. W., Deutsch, E. W., Au, C. E., Kearney, R. E. et al., A HUPO test sample study reveals common problems in mass spectrometry-based proteomics. Nat. Methods 2009, 6, 423-430.
144. Major, M. B., Moon, R. T., "Omic" risk assessment. Sci. Signal. 2009, 2, eg7.
145. Tomson, S. N., Narayan, M., Allen, G. I., Eagleman, D. M., Neural networks of colored sequence synesthesia. J. Neurosci. 2013, 33, 14098-14106.