title: Prioritizing functional phosphorylation sites based on multiple feature integration.
abstract: Protein phosphorylation is an important type of post-translational modification that is involved in a variety of biological activities. Most phosphorylation events occur on serine, threonine and tyrosine residues in eukaryotes. In recent years, many phosphorylation sites have been identified as a result of advances in mass-spectrometric techniques. However, a large percentage of phosphorylation sites may be non-functional. Systematically prioritizing functional sites from a large number of phosphorylation sites will be increasingly important for the study of their biological roles. This study focused on exploring the intrinsic features of functional phosphorylation sites to predict whether a phosphosite is likely to be functional. We found significant differences in the distribution of evolutionary conservation, kinase association, disorder score, and secondary structure between known functional and background phosphorylation datasets. We built four different types of classifiers based on the most representative features and found that their performances were similar. We also prioritized 213,837 human phosphorylation sites from a variety of phosphorylation databases, which will be helpful for subsequent functional studies. All predicted results are available for query and download on our website (Predict Functional Phosphosites, PFP, http://pfp.biosino.org/).
authors.affiliation: Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.|University of Chinese Academy of Sciences, Beijing, P. R. China.
authors.forename: Qingyu
authors.initials: Q
authors.lastname: Xiao
authors.affiliation: Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.|University of Chinese Academy of Sciences, Beijing, P. R. China.
authors.forename: Benpeng
authors.initials: B
authors.lastname: Miao
authors.affiliation: University of Chinese Academy of Sciences, Beijing, P. R. China.|Key Lab of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.
authors.forename: Jie
authors.initials: J
authors.lastname: Bi
authors.affiliation: Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.|Shanghai Center for Bioinformation Technology, Shanghai Industrial Technology Institute, Shanghai, P. R. China.
authors.forename: Yixue
authors.initials: Y
authors.lastname: Li
mesh_terms: D001665:Binding Sites; D006801:Humans; D008962:Models, Theoretical; D010766:Phosphorylation; D011506:Proteins / Q000378:metabolism*
chemical_list: D011506:Proteins