Campus Units

Genetics, Development and Cell Biology, Bioinformatics and Computational Biology, Computer Science

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2008

Journal or Book Title

Journal of Molecular Recognition

Volume

21

Issue

4

First Page

243

Last Page

255

DOI

10.1002/jmr.893

Abstract

The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B-cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross-validation on a homology-reduced data set of 701 linear B-cell epitopes, extracted from Bcipep database, and 701 non-epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B-cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC = 0.758) outperforms 11 SVM-based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC = 0.7), a recently proposed method for predicting linear B-cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B-cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B-cell epitope prediction methods drawn on the basis of experiments using data sets of unique B-cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology-reduced data sets in comparing B-cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homologyreduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.

Comments

This is the peer reviewed version of the following article: EL-Manzalawy, Y., Dobbs, D. and Honavar, V. (2008), Predicting linear B-cell epitopes using string kernels. J. Mol. Recognit., 21: 243–255 , which has been published in final form at doi: 10.1002/jmr.893. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Copyright Owner

John Wiley & Sons, Ltd.

Language

en

File Format

application/pdf

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