School of Computer Engineering, Bioinformatics Research Center, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Nov-Dec;8(6):1483-94. doi: 10.1109/TCBB.2011.49.
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.
上下文感知是抗原和抗体识别的一个特征，突出了抗原与不同抗体相互作用时表位残基的重新配置。将抗原区域粗略地分为抗原表位或非表位，而不指定抗体，可能无法准确反映这种生物学现实。因此，我们根据这一原则研究了一个抗体特异性表位预测问题。这个问题是新的和具有挑战性的，因为当抗原与特定抗体结合时，我们要从抗原中确定抗原的一个子集。我们引入了两种上下文感知的关联：1）残基-残基配对偏好，2）接触残基对集之间的依赖关系。偏好起着桥梁作用，将相互作用的抗体互补位和表位残基连接起来，而依赖关系则用于将关联从一维扩展到二维。抗体互补位/表位残基的相对组成、协同作用比和马尔可夫特性也被用来增强我们的方法。我们编译了一个包含 80 个抗体-抗原复合物的非冗余数据集，并在评估中使用。结果表明，我们的方法在抗体特异性表位预测方面表现良好。在传统的抗体忽略的表位预测问题上，我们的方法的简化版本与三个基于结构的预测器相比，可以产生有竞争力的，有时甚至更好的性能。
