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BioMed Research International
Volume 2016 (2016), Article ID 3281590, 12 pages
http://dx.doi.org/10.1155/2016/3281590
Research Article

RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest

1Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
2Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
3Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA

Received 2 October 2015; Revised 13 January 2016; Accepted 31 January 2016

Academic Editor: Zhirong Sun

Copyright © 2016 Hamid D. Ismail et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.