title: Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information.
abstract: BACKGROUND
  Most of the existing in silico phosphorylation site prediction systems use machine learning approach that requires preparing a good set of classification data in order to build the classification knowledge. Furthermore, phosphorylation is catalyzed by kinase enzymes and hence the kinase information of the phosphorylated sites has been used as major classification data in most of the existing systems. Since the number of kinase annotations in protein sequences is far less than that of the proteins being sequenced to date, the prediction systems that use the information found from the small clique of kinase annotated proteins can not be considered as completely perfect for predicting outside the clique. Hence the systems are certainly not generalized. In this paper, a novel generalized prediction system, PPRED (Phosphorylation PREDictor) is proposed that ignores the kinase information and only uses the evolutionary information of proteins for classifying phosphorylation sites.
  
  
  RESULTS
  Experimental results based on cross validations and an independent benchmark reveal the significance of using the evolutionary information alone to classify phosphorylation sites from protein sequences. The prediction performance of the proposed system is better than those of the existing prediction systems that also do not incorporate kinase information. The system is also comparable to systems that incorporate kinase information in predicting such sites.
  
  
  CONCLUSIONS
  The approach presented in this paper provides an efficient way to identify phosphorylation sites in a given protein primary sequence that would be a valuable information for the molecular biologists working on protein phosphorylation sites and for bioinformaticians developing generalized prediction systems for the post translational modifications like phosphorylation or glycosylation. PPRED is publicly available at the URL http://www.cse.univdhaka.edu/~ashis/ppred/index.php.
authors.affiliation: Department of Computer Science and Engineering, University of Dhaka, Dhaka - 1000, Bangladesh. ashis.csedu@gmail.com
authors.forename: Ashis Kumer
authors.initials: AK
authors.lastname: Biswas
authors.forename: Nasimul
authors.initials: N
authors.lastname: Noman
authors.forename: Abdur Rahman
authors.initials: AR
authors.lastname: Sikder
mesh_terms: D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001665:Binding Sites; D030562:Databases, Protein; D010766:Phosphorylation; D011494:Protein Kinases / Q000737:chemistry / Q000378:metabolism; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins; D011494:Protein Kinases