Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Eleventh International Conference on Bioinformatics (InCoB2012): Bioinformatics

Open Access Proceedings

B-cell epitope prediction through a graph model

Liang Zhao1, Limsoon Wong2, Lanyuan Lu3, Steven CH Hoi1* and Jinyan Li4*

Author affiliations

1 Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore

2 School of Computing, National University of Singapore, Singapore

3 School of Biological Science, Nanyang Technological University, Singapore

4 Advanced Analytics Institute, School of Software, Faculty of Engineering and IT, University of Technology Sydney, PO Box 123, NSW 2007, Australia

For all author emails, please log on.

Citation and License

BMC Bioinformatics 2012, 13(Suppl 17):S20  doi:10.1186/1471-2105-13-S17-S20

Published: 13 December 2012

Abstract

Background

Prediction of B-cell epitopes from antigens is useful to understand the immune basis of antibody-antigen recognition, and is helpful in vaccine design and drug development. Tremendous efforts have been devoted to this long-studied problem, however, existing methods have at least two common limitations. One is that they only favor prediction of those epitopes with protrusive conformations, but show poor performance in dealing with planar epitopes. The other limit is that they predict all of the antigenic residues of an antigen as belonging to one single epitope even when multiple non-overlapping epitopes of an antigen exist.

Results

In this paper, we propose to divide an antigen surface graph into subgraphs by using a Markov Clustering algorithm, and then we construct a classifier to distinguish these subgraphs as epitope or non-epitope subgraphs. This classifier is then taken to predict epitopes for a test antigen. On a big data set comprising 92 antigen-antibody PDB complexes, our method significantly outperforms the state-of-the-art epitope prediction methods, achieving 24.7% higher averaged f-score than the best existing models. In particular, our method can successfully identify those epitopes with a non-planarity which is too small to be addressed by the other models. Our method can also detect multiple epitopes whenever they exist.

Conclusions

Various protrusive and planar patches at the surface of antigens can be distinguishable by using graphical models combined with unsupervised clustering and supervised learning ideas. The difficult problem of identifying multiple epitopes from an antigen can be made easied by using our subgraph approach. The outstanding residue combinations found in the supervised learning will be useful for us to form new hypothesis in future studies.