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Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
Published on: March 24, 2017
Antibody-specified B-cell epitope prediction in line with the principle of context-awareness
Liang Zhao1, Limsoon Wong, Jinyan Li
1School of Computer Engineering, Bioinformatics Research Center, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798. S080011@e.ntu.edu.sg
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.
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.
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