title: PhosD: inferring kinase-substrate interactions based on protein domains.
abstract: BACKGROUND
  Identifying the kinase-substrate relationships is vital to understanding the phosphorylation events and various biological processes, especially signal transductions. Although large amount of phosphorylation sites have been detected, unfortunately, it is rarely known which kinases activate those sites. Despite distinct computational approaches have been proposed to predict the kinase-substrate interactions, the prediction accuracy still needs to be improved.
  
  
  RESULTS
  In this paper, we propose a novel probabilistic model named as PhosD to predict kinase-substrate relationships based on protein domains with the assumption that kinase-substrate interactions are accomplished with kinase-domain interactions. By further taking into account protein-protein interactions, our PhosD outperforms other popular approaches on several benchmark datasets with higher precision. In addition, some of our predicted kinase-substrate relationships are validated by signaling pathways, indicating the predictive power of our approach. Furthermore, we notice that given a kinase, the more substrates are known for the kinase the more accurate its predicted substrates will be, and the domains involved in kinase-substrate interactions are found to be more conserved across proteins phosphorylated by multiple kinases. These findings can help develop more efficient computational approaches in the future.
  
  
  METHODS
  The data and results are available at http://comp-sysbio.org/phosd.
  
  
  BACKGROUND
  xm_zhao@tongji.edu.cn.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.|School of Software.
authors.forename: Gui-Min
authors.initials: GM
authors.lastname: Qin
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an 710071, China.
authors.forename: Rui-Yi
authors.initials: RY
authors.lastname: Li
authors.affiliation: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Xing-Ming
authors.initials: XM
authors.lastname: Zhao
mesh_terms: D015233:Models, Statistical; D010766:Phosphorylation; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D011494:Protein Kinases / Q000378:metabolism*; D015398:Signal Transduction; D012984:Software
chemical_list: D011494:Protein Kinases