title: Predicting protein post-translational modifications using meta-analysis of proteome scale data sets.
abstract: Protein post-translational modifications are an important biological regulatory mechanism, and the rate of their discovery using high throughput techniques is rapidly increasingly. To make use of this wealth of sequence data, we introduce a new general strategy designed to predict a variety of post-translational modifications in several organisms. We used the motif-x program to determine phosphorylation motifs in yeast, fly, mouse, and man and lysine acetylation motifs in man. These motifs were then scanned against proteomic sequence data using a newly developed tool called scan-x to globally predict other potential modification sites within these organisms. 10-fold cross-validation was used to determine the sensitivity and minimum specificity for each set of predictions, all of which showed improvement over other available tools for phosphoprediction. New motif discovery is a byproduct of this approach, and the phosphorylation motif analyses provide strong evidence of evolutionary conservation of both known and novel kinase motifs.
authors.affiliation: Department of Genetics, Harvard Medical School, Boston, MA 02115, USA. dschwartz@genetics.med.harvard.edu
authors.forename: Daniel
authors.initials: D
authors.lastname: Schwartz
authors.forename: Michael F
authors.initials: MF
authors.lastname: Chou
authors.forename: George M
authors.initials: GM
authors.lastname: Church
mesh_terms: D000107:Acetylation; D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D000818:Animals; D030562:Databases, Protein; D004330:Drosophila; D006801:Humans; D051379:Mice; D008969:Molecular Sequence Data; D010766:Phosphorylation; D011499:Protein Processing, Post-Translational; D020543:Proteome / Q000737:chemistry / Q000378:metabolism*; D012372:ROC Curve; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae
chemical_list: D020543:Proteome