Prediction of phosphorylation sites using SVMs.
BACKGROUND
Phosphorylation is involved in diverse signal transduction pathways. By predicting phosphorylation sites and their kinases from primary protein sequences, we can obtain much valuable information that can form the basis for further research. Using support vector machines, we attempted to predict phosphorylation sites and the type of kinase that acts at each site.


RESULTS
Our prediction system was limited to phosphorylation sites catalyzed by four protein kinase families and four protein kinase groups. The accuracy of the predictions ranged from 83 to 95% at the kinase family level, and 76-91% at the kinase group level. The prediction system used-PredPhospho-can be applied to the functional study of proteins, and can help predict the changes in phosphorylation sites caused by amino acid variations at intra- and interspecies levels.
Kim
Jong Hun
JH
National Genome Research Institute, 5 Nokbun-Dong, Eunpyung-Gu, Seoul, 122-701 Korea. jh7521@ngri.re.kr
Lee
Juyoung
J
Oh
Bermseok
B
Kimm
Kuchan
K
Koh
Insong
I
D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D010766:Phosphorylation; D010770:Phosphotransferases / Q000737:chemistry*; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
D011506:Proteins; D010770:Phosphotransferases