Improved prediction of lysine acetylation by support vector machines

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

Research output: Contribution to journalArticlepeer-review

83 Citations (Scopus)


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

Original languageEnglish
Pages (from-to)977-983
Number of pages7
JournalProtein and Peptide Letters
Issue number8
Publication statusPublished - 2009 Aug

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry


Dive into the research topics of 'Improved prediction of lysine acetylation by support vector machines'. Together they form a unique fingerprint.

Cite this