With isotopic/isobaric labelling techniques and increasingly label-free approach, proteome-wide phosphorylation events can now be identified and quantified at a single amino acid resolution with high precision.
Recent advances in mass spectrometry (MS)-based technologies make it possible to profile proteome-wide phosphorylation events in vivo for investigating signal transduction cascades, understanding complex diseases, and develop strategies for therapeuitc intervention. However, kinase activities are often less specific in vitro compared to in vivo, and, as a result, in vitro analyses often result in a large number of false discoveries. Traditionally, protein phosphorylation has been studied largely using in vitro assays and, more recently, protein chip arrays.
It is characterized by the addition of a phosphate group by a protein kinase to a serine, threonine, or tyrosine residue on a substrate protein. Protein phosphorylation is a common type of PTM that increases the functional diversity of the proteome by altering target proteins between active and inactive forms for signal transduction and integration. Protein post-translational modifications (PTMs), which can activate or inhibit protein function/activity, have emerged as key regulators of various signaling pathways. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.Ĭell signaling controls various aspects of basic cellular processes including homeostasis, proliferation, survival, and cell fate decisions, and defects in mechanisms underlying these processes are associated with a wide range of diseases. CLUE implementation, source code, and documentation are freely available from CRAN at įunding: This work was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (RJ: 1ZIAES102625). The work is made available under the Creative Commons CC0 public domain dedicationĭata Availability: All relevant data are within the paper and its Supporting Information files.
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. Received: FebruAccepted: JPublished: August 7, 2015
Iakoucheva, University of California San Diego, UNITED STATES The proposed approach will be a valuable resource in the identification and characterizing of signaling networks from phosphoproteomics data.Ĭitation: Yang P, Zheng X, Jayaswal V, Hu G, Yang JYH, Jothi R (2015) Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data. We demonstrate the utility of the proposed approach on two time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway. Our approach utilizes prior knowledge, annotated kinase-substrate relationships mined from literature and curated databases, to first generate biologically meaningful partitioning of the phosphorylation sites and then determine key kinases associated with each cluster. Here we present a knowledge-based CLUster Evaluation (CLUE) approach for identifying the most informative partitioning of a given temporal phosphoproteomics data. Since many substrates of a given kinase have similar temporal kinetics, clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases. By analyzing the activities of phosphorylation sites over a time-course, the temporal dynamics of signaling cascades can be elucidated. In a typical phosphoproteomics study, phosphorylation sites (substrates) of active kinases are quantified proteome-wide. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions.