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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/27814

Title: Estimation and extraction of predictive linear epitopes by mathematical morphology approaches
Authors: Hao-Tan Chang;Chih-Hong Liu;Dah-Tsyr Chang;Tun-Wen Pai
Contributors: NTOU:Department of Computer Science and Engineering
國立臺灣海洋大學:資訊工程學系
Keywords: linear epitope;antigenicity;mathematical morphology
Date: 2008-01
Issue Date: 2011-10-21T02:34:06Z
Publisher: The Sixth Asia- Pacific Bioinformatics Conference
Abstract: Abstract:B-cell epitope prediction facilitates the design and synthesis of short peptides for various immunological applications. Several algorithms have been developed to predict B-cell linear epitopes (LEs) from primary sequences of antigens, providing important information for immunobiological experiments and antibody design. This paper describes two robust methods, LE prediction with/without local peak extraction (LEP-LP and LEP-NLP), based on antigenicity scale and mathematical morphology for the prediction of B-cell LEs. Previous studies revealed that LEs could occur in regions with low-to-moderate but not globally high antigenicity scales. Hence, we developed a method adopting mathematical morphology to extract local peaks from a linear combination of the propensity scales of physico-chemical characteristics at each antigen residue. Comparison among LEP-LP/LEP-NLP, BepiPred and BEPITOPE revealed that our algorithms performed better in retrieving epitopes with low-to-moderate antigenicity and achieved comparable performance according to receiver operation characteristics (ROC) curve analysis. Of the identified LEs, over 30% were unable to be predicted by BepiPred and BEPITOPE employing an average threshold of antigenicity index or default settings. Our LEP-LP method provides a bioinformatics approach for predicting B-cell LEs with low- to-moderate antigenicity.
URI: http://ntour.ntou.edu.tw/handle/987654321/27814
Appears in Collections:[資訊工程學系] 演講及研討會

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