JianZhao Gao, Xue-Wen Tao, Jia Zhao, Yuan-Ming Feng, Yu-Dong Cai and Ning Zhang Pages 1 - 9 ( 9 )
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. 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. 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, and it suggested some important factors determining the lysine acetylation sites, which provided insights into the mechanism of lysine acetylation sites, providing guidance of experimental validation.
acetylation, post-translational modification, dagging, maximum relevance minimum redundancy, incremental feature selection
School of Mathematical Sciences and LPMC, Nankai University, Tianjin, Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin, Biomedical Research Center, CODBIO Company Ltd., Tianjin, Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin, School of Life Science, Shanghai University, Shanghai, Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin