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Updated: Mar 6, 2026
A Facile Protocol to Generate Site-Specifically Acetylated Proteins in Escherichia Coli
Published on: December 9, 2017
Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method
JianZhao Gao1, Xue-Wen Tao2, Jia Zhao3
1School of Mathematical Sciences and LPMC, Nankai University, Tianjin. China.
Aim And Objective:
Lysine acetylation, as one type of post-translational modifications (PTM), plays key roles in cellular regulations and can be involved in a variety of human diseases. However, it is often high-cost and time-consuming to use traditional experimental approaches to identify the lysine acetylation sites. Therefore, effective computational methods should be developed to predict the acetylation sites. In this study, we developed a position-specific method for epsilon lysine acetylation site prediction.
Material And Methods:
Sequences of acetylated proteins were retrieved from the UniProt database. Various kinds of features such as position specific scoring matrix (PSSM), amino acid factors (AAF), and disorders were incorporated. A feature selection method based on mRMR (Maximum Relevance Minimum Redundancy) and IFS (Incremental Feature Selection) was employed.
Results:
Finally, 319 optimal features were selected from total 541 features. Using the 319 optimal features to encode peptides, a predictor was constructed based on dagging. As a result, an accuracy of 69.56% with MCC of 0.2792 was achieved. We analyzed the optimal features, which suggested some important factors determining the lysine acetylation sites.
Conclusion:
We developed a position-specific method for epsilon lysine acetylation site prediction. A set of optimal features was selected. Analysis of the optimal features provided insights into the mechanism of lysine acetylation sites, providing guidance of experimental validation.
