Improved prediction of lysine acetylation by support vector machines

Songng Li, Hong Li, Mingfa Li, Yu Shyr, Lu Xie, Yixue Li

研究成果: Article

76 引文 斯高帕斯(Scopus)

摘要

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 othermethods 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 ε-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/.

原文English
頁(從 - 到)977-983
頁數7
期刊Protein and Peptide Letters
16
發行號8
DOIs
出版狀態Published - 2009 八月 1

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry

指紋 深入研究「Improved prediction of lysine acetylation by support vector machines」主題。共同形成了獨特的指紋。

  • 引用此