Improved Prediction of Lysine Acetylation by Support Vector Machines

Title: Improved Prediction of Lysine Acetylation by Support Vector Machines

Volume: 16 Issue: 8

Author(s): Songling Li, Hong Li, Mingfa Li, Yu Shyr, Lu Xie and Yixue Li

Affiliation:

Keywords: Reversible lysine acetylation, support vector machine, protein coupling pattern

Abstract: 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. LysAcets 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 η-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/.

Cite this article as:

Li Songling, Li Hong, Li Mingfa, Shyr Yu, Xie Lu and Li Yixue, Improved Prediction of Lysine Acetylation by Support Vector Machines, Protein & Peptide Letters 2009; 16 (8) . https://dx.doi.org/10.2174/092986609788923338

DOI https://dx.doi.org/10.2174/092986609788923338	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305