title: Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method.
abstract: 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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Mathematical Sciences and LPMC, Nankai University, Tianjin. China.
authors.forename: JianZhao
authors.initials: J
authors.lastname: Gao
authors.affiliation: Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin. China.
authors.forename: Xue-Wen
authors.initials: XW
authors.lastname: Tao
authors.affiliation: Biomedical Research Center, CODBIO Company Ltd., Tianjin. China.
authors.forename: Jia
authors.initials: J
authors.lastname: Zhao
authors.affiliation: Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin. China.
authors.forename: Yuan-Ming
authors.initials: YM
authors.lastname: Feng
authors.affiliation: School of Life Science, Shanghai University, Shanghai. China.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.affiliation: Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin. China.
authors.forename: Ning
authors.initials: N
authors.lastname: Zhang
mesh_terms: D000107:Acetylation; D019295:Computational Biology; D030562:Databases, Protein; D006801:Humans; D008239:Lysine / Q000737:chemistry / Q000378:metabolism*; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D011506:Proteins; D008239:Lysine
keywords: Acetylation; dagging; epsilon lysine acetylation site; incremental
  feature selection; maximum relevance minimum redundancy; post-translational modification