B-cell epitope prediction through a graph model
- BioMed Central Ltd
- Publication Type:
- Journal Article
- BMC Bioinformatics, 2012, 13 (suppl 17), pp. 1 - 12
- Issue Date:
|dc.identifier.citation||BMC Bioinformatics, 2012, 13 (suppl 17), pp. 1 - 12||en_US|
|dc.description.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.||en_US|
|dc.publisher||BioMed Central Ltd||en_US|
|dc.title||B-cell epitope prediction through a graph model||en_US|
|utslib.for||0801 Artificial Intelligence and Image Processing||en_US|
|utslib.for||06 Biological Sciences||en_US|
|utslib.for||08 Information And Computing Sciences||en_US|
|utslib.for||01 Mathematical Sciences||en_US|
|pubs.organisational-group||/University of Technology Sydney|
|pubs.organisational-group||/University of Technology Sydney/Faculty of Engineering and Information Technology|
|pubs.organisational-group||/University of Technology Sydney/Strength - AAI - Advanced Analytics Research Centre|
|pubs.organisational-group||/University of Technology Sydney/Strength - CHT - Health Technologies|
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