Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method

Author(s): JianZhao Gao, Xue-Wen Tao, Jia Zhao, Yuan-Ming Feng*, Yu-Dong Cai*, Ning Zhang*.

Journal Name:Combinatorial Chemistry & High Throughput Screening

Volume 20 , Issue 7 , 2017

Abstract:

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.

Keywords: Acetylation, post-translational modification, dagging, maximum relevance minimum redundancy, incremental feature selection, epsilon lysine acetylation site.

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Article Details

VOLUME: 20
ISSUE: 7
Year: 2017
Page: [629 - 637]
Pages: 9
DOI: 10.2174/1386207320666170314093216
Price: $58