Lysine acetylation sites prediction using an ensemble of support vector machine classifiers.
Lysine acetylation is an essentially reversible and high regulated post-translational modification which regulates diverse protein properties. Experimental identification of acetylation sites is laborious and expensive. Hence, there is significant interest in the development of computational methods for reliable prediction of acetylation sites from amino acid sequences. In this paper we use an ensemble of support vector machine classifiers to perform this work. The experimentally determined acetylation lysine sites are extracted from Swiss-Prot database and scientific literatures. Experiment results show that an ensemble of support vector machine classifiers outperforms single support vector machine classifier and other computational methods such as PAIL and LysAcet on the problem of predicting acetylation lysine sites. The resulting method has been implemented in EnsemblePail, a web server for lysine acetylation sites prediction available at http://www.aporc.org/EnsemblePail/.
Xu
Yan
Y
College of Science, China Agricultural University, Beijing 100083, China.
Wang
Xiao-Bo
XB
Ding
Jun
J
Wu
Ling-Yun
LY
Deng
Nai-Yang
NY
D000107:Acetylation; D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005188:False Negative Reactions; D005189:False Positive Reactions; D020407:Internet; D008239:Lysine / Q000378:metabolism*; D011499:Protein Processing, Post-Translational; D012372:ROC Curve; D014584:User-Computer Interface
D008239:Lysine