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https://doi.org/10.1109/TCBB.2011.49
Title: | Antibody-specified B-cell epitope prediction in line with the principle of context-awareness | Authors: | Zhao, L. Wong, L. Li, J. |
Keywords: | antibody antigen. context dependence Epitope prediction |
Issue Date: | 2011 | Citation: | Zhao, L., Wong, L., Li, J. (2011). Antibody-specified B-cell epitope prediction in line with the principle of context-awareness. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 (6) : 1483-1494. ScholarBank@NUS Repository. https://doi.org/10.1109/TCBB.2011.49 | Abstract: | Context-awareness is a characteristic in the recognition between antigens and antibodies, highlighting the reconfiguration of epitope residues when an antigen interacts with a different antibody. A coarse binary classification of antigen regions into epitopes, or nonepitopes without specifying antibodies may not accurately reflect this biological reality. Therefore, we study an antibody-specified epitope prediction problem in line with this principle. This problem is new and challenging as we pinpoint a subset of the antigenic residues from an antigen when it binds to a specific antibody. We introduce two kinds of associations of the contextual awareness: 1) residues-residues pairing preference, and 2) the dependence between sets of contact residue pairs. Preference plays a bridging role to link interacting paratope and epitope residues while dependence is used to extend the association from one-dimension to two-dimension. The paratope/epitope residues' relative composition, cooperativity ratios, and Markov properties are also utilized to enhance our method. A nonredundant data set containing 80 antibody-antigen complexes is compiled and used in the evaluation. The results show that our method yields a good performance on antibody-specified epitope prediction. On the traditional antibody-ignored epitope prediction problem, a simplified version of our method can produce a competitive, sometimes much better, performance in comparison with three structure-based predictors. © 2011 IEEE. | Source Title: | IEEE/ACM Transactions on Computational Biology and Bioinformatics | URI: | http://scholarbank.nus.edu.sg/handle/10635/39674 | ISSN: | 15455963 | DOI: | 10.1109/TCBB.2011.49 |
Appears in Collections: | Staff Publications |
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