Detecting Succinylation sites from protein sequences using ensemble support vector machine

BMC Bioinformatics

Xiaowei Zhao¹  Xiaosa Zhao²  Lingling Bao²  Zhiqiang Ma²  Qiao Ning² 

[1] Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University;School of Information Science and Technology, Northeast Normal University;

关键词: Predict succinylation sites;    Multiple features;    Grey pseudo amino acid composition;    Information gain;    SVM;    Ensemble learning algorithm;   

DOI  :  10.1186/s12859-018-2249-4

来源: DOAJ

【 摘 要 】

Abstract Background Lysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. However, traditional methods, experimental approaches, are labor-intensive and time-consuming. Computational prediction methods have been proposed recent years, and they are popular because of their convenience and high speed. In this study, we developed a new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acid composition, with a feature selection scheme (information gain). And then, it was trained using SVM (Support Vector Machine) and an ensemble learning algorithm. Results The performance of this method was measured with an accuracy of 89.14% and a MCC (Matthew Correlation Coefficient) of 0.79 using 10-fold cross validation on training dataset and an accuracy of 84.5% and a MCC of 0.2 on independent dataset. Conclusions The conclusions made from this study can help to understand more of the succinylation mechanism. These results suggest that our method was very promising for predicting succinylation sites. The source code and data of this paper are freely available athttps://github.com/ningq669/PSuccE.

【 授权许可】

Unknown