期刊
COMPUTATIONAL BIOLOGY AND CHEMISTRY
卷 46, 期 -, 页码 16-22出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compbiolchem.2013.05.001
关键词
Halophile; Pearson VII function kernel; Support vector machine; Amino acid composition; Hypersaline adaptation
资金
- Cultivation Project of Huaqiao University for the China National Funds for Distinguished Young Scientists [JB-GJ1006]
- Program for New Century Excellent Talents in Universities of Fujian Province [07176C02]
Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins. (c) 2013 The Authors. Published by Elsevier Ltd. All rights reserved.
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