Detecting Succinylation sites from protein sequences using ensemble support vector machine

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 at https://github.com/ningq669/PSuccE .

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

Fig. 1

The flow chart of PSuccE

Fig. 2

The information entropy value of positions around the central residue

Fig. 3

Two sample logos of the compositional biases around succinylation sites compared to non-succinylation sites. Statistically significant symbols are plotted using the size of the symbol that is proportional to the difference between the two samples. Residues are separated in two groups: (1) enriched in the positive samples; and (2) depleted in the positive samples. Color of the symbols was classified according to the polarity of the residue side chain

Fig. 4

ROC curves (AUC) of predictions based on 10-fold cross validation