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Journal of Biomedicine and Biotechnology
Volume 2011 (2011), Article ID 432830, 12 pages
Research Article

Prediction of B-cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification

1Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan
2Center of Excellence for Marine Bioenvironment and Biotechnology, National Taiwan Ocean University, Keelung 20224, Taiwan
3Graduate Institute of Molecular Systems Biomedicine, China Medical University, Taichung 40402, Taiwan
4Graduate Institute of Clinical Medical Science, China Medical University, Taichung 40402, Taiwan
5Graduate Institute of Basic Medical Science & Ph.D. Program for Aging, China Medical University, Taichung 40402, Taiwan

Received 3 June 2011; Accepted 28 June 2011

Academic Editor: Yongqun O. He

Copyright © 2011 Hsin-Wei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).