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Towards functional phosphoproteomics by mapping differential phosphorylation events in signaling networks
Author(s) -
de la Fuente van Bentem Sergio,
Mentzen Wieslawa I.,
de la Fuente Alberto,
Hirt Heribert
Publication year - 2008
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200800175
Subject(s) - phosphoproteomics , phosphorylation , biology , signal transduction , protein phosphorylation , computational biology , proteomics , microbiology and biotechnology , gene , bioinformatics , protein kinase a , biochemistry
Protein phosphorylation plays a central role in many signal transduction pathways that mediate biological processes. Novel quantitative mass spectrometry‐based methods have recently revealed phosphorylation dynamics in animals, yeast, and plants. These methods are important for our understanding of how differential phosphorylation participates in translating distinct signals into proper physiological responses, and shifted research towards screening for potential cancer therapies and in‐depth analysis of phosphoproteomes. In this review, we aim to describe current progress in quantitative phosphoproteomics. This emerging field has changed numerous static pathways into dynamic signaling networks, and revealed protein kinase networks that underlie adaptation to environmental stimuli. Mass spectrometry enables high‐throughput and high‐quality analysis of differential phosphorylation at a site‐specific level. Although determination of differential phosphorylation between treatments is analogous to detecting differential gene expression, the large body of statistical techniques that has been developed for analysis of differential gene expression is not generally applied for detecting differential phosphorylation. We suggest possible improvements for analysis of quantitative phosphorylation by increasing the number of biological replicates and adapting statistical tests used for gene expression profiling and widely implemented in freely available software tools.