title: RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.
abstract: Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.
authors.affiliation: Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA.
authors.forename: Hamid D
authors.initials: HD
authors.lastname: Ismail
authors.affiliation: Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA.
authors.forename: Ahoi
authors.initials: A
authors.lastname: Jones
authors.affiliation: Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA.
authors.forename: Jung H
authors.initials: JH
authors.lastname: Kim
authors.affiliation: Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA.
authors.forename: Robert H
authors.initials: RH
authors.lastname: Newman
authors.affiliation: Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA.
authors.forename: Dukka B
authors.initials: DB
authors.lastname: Kc
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003663:Decision Trees; D015233:Models, Statistical; D010766:Phosphorylation; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins