期刊
PROTEIN AND PEPTIDE LETTERS
卷 19, 期 5, 页码 492-500出版社
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/092986612800191080
关键词
Protein-protein interactions; principal component analysis (PCA); support vector machine (SVM); protein sequences; prediction
资金
- National Natural Science Foundation of China [60803102, 61070084]
- NSFC [60496321]
Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.
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