Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece.
Genetics and Computational Biology Group, Laboratory of Genetics, Department of Biotechnology, Agricultural University of Athens, Athens, Greece.
Adv Exp Med Biol. 2020;1194:41-58. doi: 10.1007/978-3-030-32622-7_4.
Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to either link them directly to diseases or drive the discovery of new medicines, such as antibody drug conjugates (ADC). Herein, we describe a new V domain antibody clustering method based on the comparison of the interaction sites between each antibody and its antigen. A more specific clustering analysis of the antibody's V domain was provided using deep learning and data mining techniques. The multidimensional information was extracted from the structural resolved antibodies when they were captured to interact with other proteins. The available 3D structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how antibody V domains recognize antigens as well as about which amino acids are involved in the recognition. As such, the antibody surface holds information about antigens' folding that reside with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, we propose a new simple philosophy to transform the conserved framework (fragment regions, complementarity-determining regions) of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity. Finally, an advanced three-level hybrid classification scheme has been set for clustering antibodies in subgroups, which can combine the information from the protein sequences, the three-dimensional structures, and specific "key patterns" of recognized interactions. The clusters provide multilevel information about antibodies and antibody-antigen complexes.
抗体 V 结构域聚类对于免疫学相关领域至关重要。尽管已经提出了几种抗体聚类方法，但仍未达成共识。许多尝试利用基因、蛋白质序列、3D 结构和 3D 表面的信息，努力阐明与功能直接相关的未知作用机制，并将其直接与疾病联系起来，或推动抗体药物偶联物 (ADC) 等新药的发现。在此，我们描述了一种新的基于每个抗体与其抗原之间相互作用位点比较的 V 结构域抗体聚类方法。使用深度学习和数据挖掘技术对抗体的 V 结构域进行了更具体的聚类分析。从与其他蛋白质相互作用时捕获的结构解析抗体中提取多维信息。蛋白质抗原-抗体 (Ag-Ab) 界面的可用 3D 结构包含有关抗体 V 结构域如何识别抗原以及哪些氨基酸参与识别的信息。因此，抗体表面包含有关抗原折叠的信息，这些信息存在于 Ab-Ag 界面残基中以及它们如何相互作用。为了深入了解这种相互作用的本质，我们提出了一种新的简单理念，即使用抗体-抗原相互作用的结构特征，将抗体 V 结构域的保守框架（片段区域、互补决定区）转化为二进制形式，以识别 V 结构域结合活性中的新抗体特征。最后，建立了一个先进的三级混合分类方案，用于将抗体分为亚群，该方案可以结合蛋白质序列、三维结构和识别相互作用的特定“关键模式”的信息。聚类提供了有关抗体和抗体-抗原复合物的多层次信息。
