Abstract

Motivation: Kinase-mediated phosphorylation is the central mecha-nism of post-translational modication to regulate cellular responses and phenotypes. Signalling defects associated with protein phospho-rylation are linked to many diseases, particularly cancer. Characte-rizing protein kinases and their substrates enhances our ability to understand and treat such diseases and broadens our knowledge of signaling networks in general. While most or all protein kinases have been identied in well-studied eukaryotes, the sites that they phosphorylate have been only partially elucidated. Experimental methods for identifying phospho-rylation sites are resource-intensive, so the ability to computationally predict potential sites has considerable value. Results: Many computational techniques for phosphorylation site prediction have been proposed, most of which are available on the web. These techniques differ in several ways, including the machine learning technique used; the amount of sequence information used; whether or not structural information is used in addition to sequence information; whether predictions are made for specic kinases or for kinases in general; and sources of training and testing data. This review summarizes, categorizes, and compares the available methods for phosphorylation site prediction, and provides an over-view of the challenges that are faced when designing predictors and how they have been addressed. It should therefore be useful both for those wishing to choose a phosphorylation site predictor for their particular biological application, and for those attempting to improve upon established techniques in the future. Contact: