Improved prediction of lysine acetylation by support vector machines.
Reversible acetylation on lysine residues, a crucial post-translational modification (PTM) for both histone and non-histone proteins, governs many central cellular processes. Due to limited data and lack of a clear acetylation consensus sequence, little research has focused on prediction of lysine acetylation sites. Incorporating almost all currently available lysine acetylation information, and using the support vector machine (SVM) method along with coding schema for protein sequence coupling patterns, we propose here a novel lysine acetylation prediction algorithm: LysAcet. When compared with other methods or existing tools, LysAcet is the best predictor of lysine acetylation, with K-fold (5- and 10-) and jackknife cross-validation accuracies of 75.89%, 76.73%, and 77.16%, respectively. LysAcet's superior predictive accuracy is attributed primarily to the use of sequence coupling patterns, which describe the relative position of two amino acids. LysAcet contributes to the limited PTM prediction research on lysine epsilon-acetylation, and may serve as a complementary in-silicon approach for exploring acetylation on proteomes. An online web server is freely available at http://www.biosino.org/LysAcet/.
Li
Songling
S
Bio-X Life Science Research Center and School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
Li
Hong
H
Li
Mingfa
M
Shyr
Yu
Y
Xie
Lu
L
Li
Yixue
Y
D000107:Acetylation; D000465:Algorithms; D001185:Artificial Intelligence; D008239:Lysine / Q000378:metabolism*; D008954:Models, Biological; D011506:Proteins / Q000378:metabolism*
D011506:Proteins; D008239:Lysine