title: Prediction of PK-specific phosphorylation site based on information entropy.
abstract: Phosphorylation is a crucial way to control the activity of proteins in many eukaryotic organisms in vivo. Experimental methods to determine phosphorylation sites in substrates are usually restricted by the in vitro condition of enzymes and very intensive in time and labor. Although some in silico methods and web servers have been introduced for automatic detection of phosphorylation sites, sophisticated methods are still in urgent demand to further improve prediction performances. Protein primary sequences can help predict phosphorylation sites catalyzed by different protein kinase and most computational approaches use a short local peptide to make prediction. However, the useful information may be lost if only the conservative residues that are not close to the phosphorylation site are considered in prediction, which would hamper the prediction results. A novel prediction method named IEPP (Information-Entropy based Phosphorylation Prediction) is presented in this paper for automatic detection of potential phosphorylation sites. In prediction, the sites around the phosphorylation sites are selected or excluded by their entropy values. The algorithm was compared with other methods such as GSP and PPSP on the ABL, MAPK and PKA PK families. The superior prediction accuracies were obtained in various measurements such as sensitivity (Sn) and specificity (Sp). Furthermore, compared with some online prediction web servers on the new discovered phosphorylation sites, IEPP also yielded the best performance. IEPP is another useful computational resource for identification of PK-specific phosphorylation sites and it also has the advantages of simpleness, efficiency and convenience.
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China.
authors.forename: MingHui
authors.initials: M
authors.lastname: Wang
authors.forename: ChunHua
authors.initials: C
authors.lastname: Li
authors.forename: WeiZu
authors.initials: W
authors.lastname: Chen
authors.forename: CunXin
authors.initials: C
authors.lastname: Wang
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology; D019277:Entropy; D020407:Internet; D008969:Molecular Sequence Data; D010766:Phosphorylation; D011494:Protein Kinases / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D012680:Sensitivity and Specificity
chemical_list: D011494:Protein Kinases