Li Songling, Li Hong, Li Mingfa, Shyr Yu, Xie Lu, Li Yixue
Bio-X Life Science Research Center and School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
Protein Pept Lett. 2009;16(8):977-83. doi: 10.2174/092986609788923338.
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/.
赖氨酸残基上的可逆乙酰化是组蛋白和非组蛋白至关重要的翻译后修饰（PTM），它调控着许多核心细胞过程。由于数据有限且缺乏明确的乙酰化共有序列，很少有研究聚焦于赖氨酸乙酰化位点的预测。整合几乎所有当前可用的赖氨酸乙酰化信息，并使用支持向量机（SVM）方法以及蛋白质序列耦合模式的编码模式，我们在此提出一种新型的赖氨酸乙酰化预测算法：LysAcet。与其他方法或现有工具相比，LysAcet是赖氨酸乙酰化的最佳预测器，其K折（5折和10折）和留一法交叉验证准确率分别为75.89%、76.73%和77.16%。LysAcet卓越的预测准确率主要归因于对序列耦合模式的使用，该模式描述了两个氨基酸的相对位置。LysAcet为赖氨酸ε-乙酰化有限的PTM预测研究做出了贡献，并可作为探索蛋白质组乙酰化的一种补充性的硅基方法。可通过http://www.biosino.org/LysAcet/免费获取在线网络服务器。
