Revisiting protein kinase-substrate interactions: Toward therapeutic development

• Published in: Science signaling Vol. 9; no. 420; p. re3
• Main Authors: de Oliveira, Paulo Sérgio L, Ferraz, Felipe Augusto N, Pena, Darlene A, Pramio, Dimitrius T, Morais, Felipe A, Schechtman, Deborah
• Format: Journal Article
• Language: English
• Published: United States 22.03.2016
• Subjects: Amino Acid Motifs; Animals; Computational Biology - methods; Computational Biology - trends; Drug Delivery Systems - methods; Drug Delivery Systems - trends; Humans; Protein Kinase Inhibitors - chemistry; Protein Kinase Inhibitors - therapeutic use; Protein Kinases - chemistry; Protein Kinases - metabolism; Structure-Activity Relationship; Substrate Specificity
• ISSN: 1937-9145
• DOI: 10.1126/scisignal.aad4016

Despite the efforts of pharmaceutical companies to develop specific kinase modulators, few drugs targeting kinases have been completely successful in the clinic. This is primarily due to the conserved nature of kinases, especially in the catalytic domains. Consequently, many currently available inhibitors lack sufficient selectivity for effective clinical application. Kinases phosphorylate their substrates to modulate their activity. One of the important steps in the catalytic reaction of protein phosphorylation is the correct positioning of the target residue within the catalytic site. This positioning is mediated by several regions in the substrate binding site, which is typically a shallow crevice that has critical subpockets that anchor and orient the substrate. The structural characterization of this protein-protein interaction can aid in the elucidation of the roles of distinct kinases in different cellular processes, the identification of substrates, and the development of specific inhibitors. Because the region of the substrate that is recognized by the kinase can be part of a linear consensus motif or a nonlinear motif, advances in technology beyond simple linear sequence scanning for consensus motifs were needed. Cost-effective bioinformatics tools are already frequently used to predict kinase-substrate interactions for linear consensus motifs, and new tools based on the structural data of these interactions improve the accuracy of these predictions and enable the identification of phosphorylation sites within nonlinear motifs. In this Review, we revisit kinase-substrate interactions and discuss the various approaches that can be used to identify them and analyze their binding structures for targeted drug development.