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An ensemble method for prediction of conformational B-cell epitopes from antigen sequences

Paper ID Volume ID Publish Year Pages File Format Full-Text
15076 1371 2014 8 PDF Available
Title
An ensemble method for prediction of conformational B-cell epitopes from antigen sequences
Abstract

•A support vector machine-based ensemble method is proposed to predict conformational B-cell epitopes.•Epitopes are more accessible than non-epitopes, and preferred in beta-turn.•The flexibility and polarity of epitopes are higher than non-epitopes.•In bound dataset, Asn, Glu, Gly, Lys, Ser, and Thr are preferred in epitope regions.•In unbound dataset, Glu and Lys are preferred in epitope sites.

Epitopes are immunogenic regions in antigen protein. Prediction of B-cell epitopes is critical for immunological applications. B-cell epitopes are categorized into linear and conformational. The majority of B-cell epitopes are conformational. Several machine learning methods have been proposed to identify conformational B-cell epitopes. However, the quality of these methods is not ideal. One question is whether or not the prediction of conformational B-cell epitopes can be improved by using ensemble methods. In this paper, we propose an ensemble method, which combined 12 support vector machine-based predictors, to predict the conformational B-cell epitopes, using an unbound dataset. AdaBoost and resampling methods are used to deal with an imbalanced labeled dataset. The proposed method achieves AUC of 0.642–0.672 on training dataset with 5-fold cross validation and AUC of 0.579–0.604 on test dataset. We also find some interesting results with the bound and unbound datasets. Epitopes are more accessible than non-epitopes, in bound and unbound datasets. Epitopes are also preferred in beta-turn, in bound and unbound datasets. The flexibility and polarity of epitopes are higher than non-epitopes. In a bound dataset, Asn (N), Glu (E), Gly (G), Lys (K), Ser (S), and Thr (T) are preferred in epitope regions, while Ala (A), Leu (L) and Val (V) are preferred in non-epitope regions. In the unbound dataset, Glu (E) and Lys (K) are preferred in epitope sites, while Leu (L) and Val (V) are preferred in non-epitiopes sites.

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Keywords
Bound dataset; Unbound dataset; Support vector machine; Beta-turn; Flexibility
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An ensemble method for prediction of conformational B-cell epitopes from antigen sequences
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 49, April 2014, Pages 51–58
Authors
, , , , ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us