Prediction of Membrane Protein Types in a Hybrid Space
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- 作者：Peilin Jia ; Ziliang Qian ; Kaiyan Feng ; Wencong Lu ; Yixue Li ; Yudong Cai
- 刊名：Journal of Proteome Research
- 出版年：2008
- 出版时间：March 2008
- 年：2008
- 卷：7
- 期：3
- 页码：1131 - 1137
- 全文大小：383K
- 年卷期：v.7,no.3(March 2008)
- ISSN：1535-3907
文摘
Prediction of the types of membrane proteins is of great importance both for genome-wide annotation and for experimental researchers to understand proteins’ functions. We describe a new strategy for the prediction of the types of membrane proteins using the Nearest Neighbor Algorithm. We introduced a bipartite feature space consisting of two kinds of disjoint vectors, proteins’ domain profile and proteins’ physiochemical characters. Jackknife cross validation test shows that a combination of both features greatly improves the prediction accuracy. Furthermore, the contribution of the physiochemical features to the classification of membrane proteins has also been explored using the feature selection method called “mRMR” (Minimum Redundancy, Maximum Relevance) (IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27 (8), 1226− 1238). A more compact set of features that are mostly contributive to membrane protein classification are obtained. The analyses highlighted both hydrophobicity and polarity as the most important features. The predictor with 56 most contributive features achieves an acceptable prediction accuracy of 87.02%. Online prediction service is available freely on our Web site http://pcal.biosino.org/TransmembraneProteinClassification.html.