College of Science, China Agricultural University, Beijing 100083, China.
J Theor Biol. 2010 May 7;264(1):130-5. doi: 10.1016/j.jtbi.2010.01.013. Epub 2010 Jan 18.
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/.
赖氨酸乙酰化是一种基本可逆且高度调控的翻译后修饰，可调节多种蛋白质特性。乙酰化位点的实验鉴定既费力又昂贵。因此，人们对开发从氨基酸序列可靠预测乙酰化位点的计算方法有很大的兴趣。在本文中，我们使用支持向量机分类器的集成来完成这项工作。实验确定的乙酰化赖氨酸位点从 Swiss-Prot 数据库和科学文献中提取。实验结果表明，支持向量机分类器的集成在预测乙酰化赖氨酸位点的问题上优于单个支持向量机分类器和其他计算方法，如 PAIL 和 LysAcet。该方法已在 EnsemblePail 中实现，这是一个用于赖氨酸乙酰化位点预测的网络服务器，可在 http://www.aporc.org/EnsemblePail/ 上获得。
