Please use this identifier to cite or link to this item: 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
Source: 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
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