Protein phosphorylation is one of the most-studied post-translational modifications: it has been estimated that up to one-third of the proteins may be modified by protein kinases (1
). This ubiquitous regulatory mechanism controls many biological processes, including cellular growth, differentiation and DNA repair (2
Knowing the phosphorylated residues in proteins is central to understanding the various signaling events in which they partake; therefore much effort has been invested in trying to identify and characterize phosphorylation sites. Traditional methods for measuring protein phosphorylation, such as mutational analysis and Edman degradation chemistry on phosphopeptides, have the disadvantage of being relatively time consuming and laborious, requiring large amounts of purified protein. On the other hand, mass spectrometry-(MS)based methods have emerged as powerful tools for the analysis of post-translational modifications due to higher sensitivity, selectively and speed. Over the past few years MS, combined with enrichment strategies for phosphorylated proteins e.g. isotope-coded affinity tags (ICAT) (3
), stable isotopic amino acids in cell culture (SILAC) (4
) and isobaric reagent iTRAQ (5
), has been increasingly employed to identify novel phosphorylation sites. One consequence of this change in phosphorylation research is that bioinformatics resources need to be adapted and expanded to accommodate the new data.
For the thousands of phosphorylation sites identified by phosphoproteomic MS the information on which kinase phosphorylates them, and consequently the pathway in which they act, is still missing. To improve the link between experimentally identified phosphorylation sites and protein kinases, Linding and collaborators (6
) have recently used the Phospho.ELM data set to develop and train a method, NetworKIN (http://networkin.info/
) that combines computational methods for predicting which group of kinases are likely to phosphorylate a given site with information about signaling pathways and protein interaction data.
The analysis of protein phosphorylation by MS will clearly prove to be an invaluable source of information for understanding cellular signaling. For this reason, we consider it increasingly important to create and maintain publicly available phospho-protein databases, where the exponentially increasing number of known phosphorylation sites (7–12
) can be easily accessed by the research community.