title: Analysis of protein phosphorylation site predictors with an independent dataset.
abstract: Protein phosphorylation plays a fundamental role in most of the cellular regulatory pathways. Experimental detection of protein phosphorylation sites is labour intensive and often limited by the availability and optimisation of enzymatic reactions. The in silico prediction of phosphorylation sites using protein's primary sequences may provide guidelines for further experimental consideration and interpretation of phosphoproteomic data. An array of such tools exists over the internet and provides the prediction for protein kinase families. We developed an independent dataset to compare the performances of these methods to provide scientists with a better understanding of which method to use for their research.
authors.affiliation: International Computer Science Institute, 1947 Center Street, Suite 600, Berkeley, CA 94704, USA. abdur@icsi.berkeley.edu
authors.forename: Abdur R
authors.initials: AR
authors.lastname: Sikder
authors.forename: Albert Y
authors.initials: AY
authors.lastname: Zomaya
mesh_terms: D000465:Algorithms; D020134:Catalytic Domain; D019295:Computational Biology / Q000379:methods*; D010750:Phosphoproteins / Q000737:chemistry* / Q000378:metabolism; D010766:Phosphorylation; D011494:Protein Kinases / Q000378:metabolism; D020543:Proteome / Q000378:metabolism
chemical_list: D010750:Phosphoproteins; D020543:Proteome; D011494:Protein Kinases