| Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity. | |
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MedLine Citation:
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PMID: 16443826 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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 (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi). |
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Authors:
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H H Lin; L Y Han; H L Zhang; C J Zheng; B Xie; Y Z Chen |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2006-01-27 |
Journal Detail:
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Title: Journal of lipid research Volume: 47 ISSN: 0022-2275 ISO Abbreviation: J. Lipid Res. Publication Date: 2006 Apr |
Date Detail:
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Created Date: 2006-03-24 Completed Date: 2006-10-05 Revised Date: 2006-11-15 |
Medline Journal Info:
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Nlm Unique ID: 0376606 Medline TA: J Lipid Res Country: United States |
Other Details:
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Languages: eng Pagination: 824-31 Citation Subset: IM |
Affiliation:
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Bioinformatics and Drug Design Group, Department of Computational Science, National University of Singapore, Singapore 117543. |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Amino Acid Sequence Animals Base Sequence Databases, Protein Humans Lipids* Molecular Sequence Data Proteins* / classification, genetics, metabolism Sequence Alignment Sequence Analysis, Protein* Sequence Homology, Amino Acid |
| Chemical | |
Reg. No./Substance:
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0/Lipids; 0/Proteins |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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