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Title: Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties
Authors: Cui, J.
Han, L.Y. 
Li, H. 
Ung, C.Y. 
Tang, Z.Q.
Zheng, C.J. 
Cao, Z.W.
Chen, Y.Z. 
Keywords: Allergen
Statistical learning method
Support vector machine
Issue Date: Jan-2007
Citation: Cui, J., Han, L.Y., Li, H., Ung, C.Y., Tang, Z.Q., Zheng, C.J., Cao, Z.W., Chen, Y.Z. (2007-01). Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties. Molecular Immunology 44 (4) : 514-520. ScholarBank@NUS Repository.
Abstract: Background: Computational methods have been developed for predicting allergen proteins from sequence segments that show identity, homology, or motif match to a known allergen. These methods achieve good prediction accuracies, but are less effective for novel proteins with no similarity to any known allergen. Methods: This work tests the feasibility of using a statistical learning method, support vector machines, as such a method. The prediction system is trained and tested by using 1005 allergen proteins from the Allergome database and 22,469 non-allergen proteins from 7871 Pfam families. Results: Testing results by an independent set of 229 allergen and 6717 non-allergen proteins from 7871 Pfam families show that 93.0% and 99.9% of these are correctly predicted, which are comparable to the best results of other methods. Of the 18 novel allergen proteins non-homologous to any other proteins in the Swissprot database, 88.9% is correctly predicted. A further screening of 168,128 proteins in the Swissprot database finds that 2.9% of the proteins are predicted as allergen proteins, which is consistent with the estimated numbers from motif-based methods. Conclusions: Our study suggests that SVM is a potentially useful method for predicting allergen proteins and it has certain capability for predicting novel allergen proteins. Our software can be accessed at © 2006 Elsevier Ltd. All rights reserved.
Source Title: Molecular Immunology
ISSN: 01615890
DOI: 10.1016/j.molimm.2006.02.010
Appears in Collections:Staff Publications

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