The transient nature of this modification (reversed by dephosphorylation reactions, catalyzed by protein phosphatases) generates the main molecular switch, regulating each aspect of protein function, including interactions, conformations, subcellular localization, enzymatic activity, and turnover. These enzymes catalyze the transference of γ-phosphate moiety from adenosine triphosphate (ATP) to the hydroxyl group of serine, threonine, or tyrosine residues present in substrate proteins. Protein kinases is the second largest family of enzymes, composed by 518 members in the human genome. An accurate modeling of kinase–substrate relationships could be the greatest contribution of bioinformatics to understand physiological cell signaling and its pathological impairment. Only recently, the development of phosphorylation predictors has begun to incorporate these variables, significantly improving specificity of these methods. However, in the intracellular environment the protein kinase specificity is influenced by contextual factors, such as protein–protein interactions, substrates co-expression patterns, and subcellular localization. The vast majority of predictors is based on the linear primary sequence pattern that surrounds phosphorylation sites. The greatest difficulty for these approaches is to model the complex nature that determines kinase–substrate specificity. To fill this gap, many computational algorithms have been developed, which are capable to predict kinase–substrate relationships. Although it is still largely unknown, the protein kinases are responsible for such modifications. Using mass spectrometry techniques, a profound knowledge has been achieved in the localization of phosphorylated residues at proteomic scale. Protein phosphorylation, catalyzed by protein kinases, is the main posttranslational modification in eukaryotes, regulating essential aspects of cellular function.
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