Detecting Succinylation sites from protein sequences using ensemble support vector machine

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. H...

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Veröffentlicht in:BMC bioinformatics Jg. 19; H. 1; S. 237 - 9
Hauptverfasser: Ning, Qiao, Zhao, Xiaosa, Bao, Lingling, Ma, Zhiqiang, Zhao, Xiaowei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 25.06.2018
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Accuracy
Algorithms
Amino acid composition
Amino acid sequencing
Amino acids
Bioinformatics
Biomedical and Life Sciences
Chromatography
Composition
Computational Biology - methods
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer applications
Correlation coefficient
Correlation coefficients
Datasets
Ensemble learning algorithm
Genetic vectors
Grey pseudo amino acid composition
Information gain
Learning algorithms
Life Sciences
Lysine
Machine learning
Microarrays
Multiple features
Physicochemical properties
Post-translation
Post-translational modifications
Predict succinylation sites
Predictions
Protein structure
Proteins
Research Article
Results and data
Support Vector Machine - standards
Support vector machines
SVM
System theory
Ensemble learning algorithm
SVM
Information gain
Predict succinylation sites
Grey pseudo amino acid composition
Multiple features
ISSN:1471-2105, 1471-2105
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Zusammenfassung: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 .
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2249-4