Please use this identifier to cite or link to this item:
Title: Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity
Authors: Lin, H.H.
Han, L.Y. 
Zhang, H.L.
Zheng, C.J. 
Xie, B.
Chen, Y.Z. 
Keywords: Lipid metabolism
Lipid-modifying enzymes
Lipid-protein interactions
Support vector machine
Issue Date: Apr-2006
Citation: Lin, H.H., Han, L.Y., Zhang, H.L., Zheng, C.J., Xie, B., Chen, Y.Z. (2006-04). Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity. Journal of Lipid Research 47 (4) : 824-831. ScholarBank@NUS Repository.
Abstract: Lipid binding proteins play important roles in signaling, regulation, membrane trafficking, immune response, lipid metabolism, and transport. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting lipid binding proteins irrespective of sequence similarity. This work explores the use of support vector machines (SVMs) as such a method. SVM prediction systems are developed using 14,776 lipid binding and 133,441 nonlipid binding proteins and are evaluated by an independent set of 6,768 lipid binding and 64,761 nonlipid binding proteins. The computed prediction accuracy is 78.9, 79.5, 82.2, 79.5, 84.4, 76.6, 90.6, 79.0, and 89.9% for lipid degradation, lipid metabolism, lipid synthesis, lipid transport, lipid binding, lipopolysaccharide biosynthesis, lipoprotein, lipoyl, and all lipid binding proteins, respectively. The accuracy for the nonmember proteins of each class is 99.9, 99.2, 99.6, 99.8, 99.9, 99.8, 98.5, 99.9, and 97.0%, respectively. Comparable accuracies are obtained when homologous proteins are considered as one, or by using a different SVM kernel function. Our method predicts 86.8% of the 76 lipid binding proteins nonhomologous to any protein in the Swiss-Prot database and 89.0% of the 73 known lipid binding domains as lipid binding. These findings suggest the usefulness of SVMs for facilitating the prediction of lipid binding proteins. Our software can be accessed at the SVMProt server ( Copyright © 2006 by the American Society for Biochemistry and Molecular Biology, Inc.
Source Title: Journal of Lipid Research
ISSN: 00222275
DOI: 10.1194/jlr.M500530-JLR200
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jan 16, 2019


checked on Jan 1, 2019

Page view(s)

checked on Oct 26, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.