title: Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence.
abstract: Identifying kinase substrates and the specific phosphorylation sites they regulate is an important factor in understanding protein function regulation and signalling pathways. Computational prediction of kinase targets - assigning kinases to putative substrates, and selecting from protein sequence the sites that kinases can phosphorylate - requires the consideration of both the cellular context that kinases operate in, as well as their binding affinity. This consideration enables investigation of how phosphorylation influences a range of biological processes. We report here a novel probabilistic model for classifying kinase-specific phosphorylation sites from sequence across three model organisms: human, mouse and yeast. The model incorporates position-specific amino acid frequencies, and counts of co-occurring amino acids from kinase binding sites. We show how this model can be seamlessly integrated with protein interactions and cell-cycle abundance profiles. When evaluating the prediction accuracy of our method, PhosphoPICK, on an independent hold-out set of kinase-specific phosphorylation sites, it achieved an average specificity of 97%, with 32% sensitivity. We compared PhosphoPICK's ability, through cross-validation, to predict kinase-specific phosphorylation sites with alternative methods, and show that at high levels of specificity PhosphoPICK obtains greater sensitivity for most comparisons made. We investigated the relationship between kinase-specific phosphorylation sites and nuclear localisation signals. We show that kinases PKA, Akt1 and AurB have an over-representation of predicted binding sites at particular positions downstream from predicted nuclear localisation signals, demonstrating an important role for these kinases in regulating the nuclear import of proteins. PhosphoPICK is freely available as a web-service at http://bioinf.scmb.uq.edu.au/phosphopick.
authors: School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia 4072, Australia. Electronic address: r.patrick@uq.edu.au.
authors: Ralph
authors: R
authors: Patrick
authors: Polytech Nice-Sophia, Université Nice Sophia-Antipolis, Nice 06103, France.
authors: Coralie
authors: C
authors: Horin
authors: School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia 4072, Australia; Institute for Molecular Bioscience, The University of Queensland, St Lucia 4072, Australia; Australian Infectious Diseases Research Centre, The University of Queensland, St Lucia 4072, Australia.
authors: Bostjan
authors: B
authors: Kobe
authors: The University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, QLD 4102, Australia.
authors: Kim-Anh Lê
authors: KA
authors: Cao
authors: School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia 4072, Australia; Institute for Molecular Bioscience, The University of Queensland, St Lucia 4072, Australia.
authors: Mikael
authors: M
authors: Bodén
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D064107:Aurora Kinase B / Q000235:genetics* / Q000378:metabolism; D001499:Bayes Theorem; D001665:Binding Sites; D017868:Cyclic AMP-Dependent Protein Kinases / Q000235:genetics* / Q000378:metabolism; D030541:Databases, Genetic; D006801:Humans; D020407:Internet; D000069550:Machine Learning; D051379:Mice; D015233:Models, Statistical; D010750:Phosphoproteins / Q000235:genetics* / Q000378:metabolism; D010766:Phosphorylation; D011485:Protein Binding; D011494:Protein Kinases / Q000235:genetics* / Q000378:metabolism; D051057:Proto-Oncogene Proteins c-akt / Q000235:genetics* / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D012680:Sensitivity and Specificity; D015398:Signal Transduction
chemical_list: D010750:Phosphoproteins; D011494:Protein Kinases; D064107:Aurora Kinase B; D051057:Proto-Oncogene Proteins c-akt; D017868:Cyclic AMP-Dependent Protein Kinases
keywords: Bayesian networks; Kinases; Machine learning; Nuclear localisation; Phosphorylation