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

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.

journal_name

Comb Chem High Throughput Screen

journal_title

Combinatorial chemistry & high throughput screening

authors

Gao J,Tao XW,Zhao J,Feng YM,Cai YD,Zhang N

doi

10.2174/1386207320666170314093216

subject

Has Abstract

pub_date

2017-01-01 00:00:00

pages

629-637

issue

7

eissn

1386-2073

issn

1875-5402

pii

CCHTS-EPUB-82257

journal_volume

20

pub_type

杂志文章

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