Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101426
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dc.contributor.authorZhao, Liangen
dc.contributor.authorWong, Limsoonen
dc.contributor.authorLu, Lanyuanen
dc.contributor.authorHoi, Steven Chu Hongen
dc.contributor.authorLi, Jinyanen
dc.date.accessioned2013-07-10T02:34:36Zen
dc.date.accessioned2019-12-06T20:38:35Z-
dc.date.available2013-07-10T02:34:36Zen
dc.date.available2019-12-06T20:38:35Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationZhao, L., Wong, L., Lu, L., Hoi, S. C. H., & Li, J. (2012). B-cell epitope prediction through a graph model. BMC Bioinformatics, 13.en
dc.identifier.issn1471-2105en
dc.identifier.urihttps://hdl.handle.net/10356/101426-
dc.description.abstractBackground: 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.en
dc.language.isoenen
dc.relation.ispartofseriesBMC bioinformaticsen
dc.rights© 2012 The Authors. This paper was published in BMC Bioinformatics and is made available as an electronic reprint (preprint) with permission of the authors. The paper can be found at the following official open URL: [http://www.biomedcentral.com/1471-2105/13/S17/S20]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en
dc.titleB-cell epitope prediction through a graph modelen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.schoolSchool of Biological Sciencesen
dc.contributor.researchBioinformatics Research Centreen
dc.identifier.openurlhttp://www.biomedcentral.com/1471-2105/13/S17/S20en
dc.description.versionPublished versionen
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