Analysis and prediction of human acetylation using a cascade classifier based on support vector machine

Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in proteins accurately. Computational methods are popular because they a...

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Bibliographic Details
Published in:BMC bioinformatics 2019-06, Vol.20 (1), p.346-346, Article 346
Main Authors: Ning, Qiao, Yu, Miao, Ji, Jinchao, Ma, Zhiqiang, Zhao, Xiaowei
Format: Article
Language:English
Subjects:
Acetates
Acetylation
Acetylation sites
Amino acids
Analysis
Binding sites
Bioinformatics
Cascade classifier
Chromatography
Classifiers
Computation
Computer applications
Datasets
Experimental methods
Human
Lysine
Machine learning
Metabolism
Methods
Peptides
Post-translation
Post-translational modifications
Protein structure
Proteins
Secondary structure
Sequence features
Support vector machine
Support vector machines
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Summary:Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in proteins accurately. Computational methods are popular because they are more convenient and faster than experimental methods. In this study, we proposed a new computational method to predict acetylation sites in human by combining sequence features and structural features including physicochemical property (PCP), position specific score matrix (PSSM), auto covariation (AC), residue composition (RC), secondary structure (SS) and accessible surface area (ASA), which can well characterize the information of acetylated lysine sites. Besides, a two-step feature selection was applied, which combined mRMR and IFS. It finally trained a cascade classifier based on SVM, which successfully solved the imbalance between positive samples and negative samples and covered all negative sample information. The performance of this method is measured with a specificity of 72.19% and a sensibility of 76.71% on independent dataset which shows that a cascade SVM classifier outperforms single SVM classifier. In addition to the analysis of experimental results, we also made a systematic and comprehensive analysis of the acetylation data.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-2938-7