| Literature DB >> 30563849 |
Karsten Krug1, Philipp Mertins1,2,3, Bin Zhang4, Peter Hornbeck4, Rajesh Raju5, Rushdy Ahmad1, Matthew Szucs1,6, Filip Mundt1, Dominique Forestier7, Judit Jane-Valbuena1, Hasmik Keshishian1, Michael A Gillette1,8, Pablo Tamayo1,9,10, Jill P Mesirov1,9,10, Jacob D Jaffe1, Steven A Carr1, D R Mani11.
Abstract
Signaling pathways are orchestrated by post-translational modifications (PTMs) such as phosphorylation. However, pathway analysis of PTM data sets generated by mass spectrometry (MS)-based proteomics is typically performed at a gene-centric level because of the lack of appropriately curated PTM signature databases and bioinformatic tools that leverage PTM site-specific information. Here we present the first version of PTMsigDB, a database of modification site-specific signatures of perturbations, kinase activities and signaling pathways curated from more than 2,500 publications. We adapted the widely used single sample Gene Set Enrichment Analysis approach to utilize PTMsigDB, enabling PTM Signature Enrichment Analysis (PTM-SEA) of quantitative MS data. We used a well-characterized data set of epidermal growth factor (EGF)-perturbed cancer cells to evaluate our approach and demonstrated better representation of signaling events compared with gene-centric methods. We then applied PTM-SEA to analyze the phosphoproteomes of cancer cells treated with cell-cycle inhibitors and detected mechanism-of-action specific signatures of cell cycle kinases. We also applied our methods to analyze the phosphoproteomes of PI3K-inhibited human breast cancer cells and detected signatures of compounds inhibiting PI3K as well as targets downstream of PI3K (AKT, MAPK/ERK) covering a substantial fraction of the PI3K pathway. PTMsigDB and PTM-SEA can be freely accessed at https://github.com/broadinstitute/ssGSEA2.0.Entities:
Keywords: Computational Biology; Database design; Pathway Analysis; Phosphorylation; Post-translational modifications*
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Year: 2018 PMID: 30563849 PMCID: PMC6398202 DOI: 10.1074/mcp.TIR118.000943
Source DB: PubMed Journal: Mol Cell Proteomics ISSN: 1535-9476 Impact factor: 5.911