This article is part of the supplement: Selected articles from the Computational Structural Bioinformatics Workshop 2009
Mining for the antibody-antigen interacting associations that predict the B cell epitopes
1 Bioinformatics Research Center, & School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798
2 Bioinformatics Research Center, & School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798
BMC Structural Biology 2010, 10(Suppl 1):S6 doi:10.1186/1472-6807-10-S1-S6Published: 17 May 2010
Predicting B-cell epitopes is very important for designing vaccines and drugs to fight against the infectious agents. However, due to the high complexity of this problem, previous prediction methods that focus on linear and conformational epitope prediction are both unsatisfactory. In addition, antigen interacting with antibody is context dependent and the coarse binary classification of antigen residues into epitope and non-epitope without the corresponding antibody may not reveal the biological reality. Therefore, we take a novel way to identify epitopes by using associations between antibodies and antigens.
Given a pair of antibody-antigen sequences, the epitope residues can be identified by two types of associations: paratope-epitope interacting biclique and cooccurrent pattern of interacting residue pairs. As the association itself does not include the neighborhood information on the primary sequence, residues' cooperativity and relative composition are then used to enhance our method. Evaluation carried out on a benchmark data set shows that the proposed method produces very good performance in terms of accuracy. After compared with other two structure-based B-cell epitope prediction methods, results show that the proposed method is competitive to, sometimes even better than, the structure-based methods which have much smaller applicability scope.
The proposed method leads to a new way of identifying B-cell epitopes. Besides, this antibody-specified epitope prediction can provide more precise and helpful information for wet-lab experiments.