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J Proteome Res. 2015 Jul 2;14(7):2745-57. doi: 10.1021/acs.jproteome.5b00249. Epub 2015 Jun 11.

Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks.

Zhang M1,2, Li H2,3, He Y2,3, Sun H2,3, Xia L4, Wang L4, Sun B1,2, Ma L2, Zhang G2, Li J1, Li Y1,2,3, Xie L2.

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†Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China.
§Key Laboratory of Systems Biology, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai 200031, China.
⊥Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, Shanghai 200025, China.


Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.


cancer; database; network; phosphorylation; signal transduction

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