B-cell epitope prediction through a graph model

BMC Bioinformatics

Proceedings

Jinyan Li¹  Steven CH Hoi²  Liang Zhao²  Lanyuan Lu³  Limsoon Wong⁴ 

[1] Advanced Analytics Institute, School of Software, Faculty of Engineering and IT, University of Technology Sydney, PO Box 123, 2007, NSW, Australia;Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore;School of Biological Science, Nanyang Technological University, Singapore;School of Computing, National University of Singapore, Singapore;

关键词: Protein Data Bank;    Accessible Surface Area;    Boundary Edge;    Residue Type;    Residue Pair;   

DOI  :  10.1186/1471-2105-13-S17-S20

来源: Springer

【 摘 要 】

BackgroundPrediction 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.ResultsIn 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.ConclusionsVarious 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.

【 授权许可】

Unknown   
© Zhao et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.