Predicting Kinase Substrates using Conservation of Local Motif Density

Short linear motifs (SLM) play critical roles in cell signaling and are associated with important biochemical events such as phosphorylation, glycosylation, and other post translational modifications. The primary aim of this thesis is to develop a new computational method (“ConDens”) to predict kinase substrates by assessing the evolution of phosphorylation SLM’s in a novel manner. This method could predict yeast Cdc28 kinase substrates that were not confidently detected by several other prediction methods published in literature and was demonstrated to be generalizable to other kinases. Genome-wide predictions experiments with this method also revealed potentially interesting novel substrates of Mec1, Prk1, PKA, and CKII.MAS