iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier.
Protein phosphorylation is a posttranslational modification (PTM or PTLM), where a phosphoryl group is added to the residue(s) of a protein molecule. The most commonly phosphorylated amino acids occur at serine (S), threonine (T), and tyrosine (Y). Protein phosphorylation plays a significant role in a wide range of cellular processes; meanwhile its dysregulation is also involved with many diseases. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of S, T, or Y, which ones can be phosphorylated, and which ones cannot? To address this problem, we have developed a predictor called iPhos-PseEn by fusing four different pseudo component approaches (amino acids' disorder scores, nearest neighbor scores, occurrence frequencies, and position weights) into an ensemble classifier via a voting system. Rigorous cross-validations indicated that the proposed predictor remarkably outperformed its existing counterparts. For the convenience of most experimental scientists, a user-friendly web-server for iPhos-PseEn has been established at http://www.jci-bioinfo.cn/iPhos-PseEn, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.|Department of Computer Science and Bond Life Science Center, University of Missouri, Columbia, MO, USA.
Wang-Ren
WR
Qiu
Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.|Gordon Life Science Institute, Boston, MA, USA.
Xuan
X
Xiao
Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.
Zhao-Chun
ZC
Xu
Gordon Life Science Institute, Boston, MA, USA.|Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia.|Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Kuo-Chen
KC
Chou
D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008239:Lysine / Q000378:metabolism; D010766:Phosphorylation; D054730:Protein Interaction Domains and Motifs; D011494:Protein Kinases / Q000378:metabolism*; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012984:Software
D011506:Proteins; D011494:Protein Kinases; D008239:Lysine
ensemble classifier; protein phosphorylation; pseudo components; random forests