Spoendlin Fabian C, Abanades Brennan, Raybould Matthew I J, Wong Wing Ki, Georges Guy, Deane Charlotte M
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.
Large Molecule Research, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany.
Front Mol Biosci. 2023 Sep 18;10:1237621. doi: 10.3389/fmolb.2023.1237621. eCollection 2023.
The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).
抗体的功能与其所结合的表位有着内在联系。基于序列同一性的克隆聚类方法通常用于对能结合相同表位的抗体进行分组。然而，这类方法忽略了一个事实，即序列高度多样的抗体可能表现出相似的结合位点几何结构并结合共同的表位。在先前的一项研究中，我们描述了SPACE1，一种通过结构对抗体进行聚类以预测其表位的方法。该方法受到基于模板建模的不准确性和不完整性的限制。此外，它仅在一个病毒类别的结构域一致性水平上进行了基准测试。在此，我们展示SPACE2，它使用最新的基于机器学习的结构预测技术并结合一种新颖的聚类协议，并在具有表位水平分辨率的结合数据上对其进行基准测试。在六组不同的抗原特异性抗体上，我们证明SPACE2能准确地对结合共同表位的抗体进行聚类，并且比克隆聚类和SPACE1实现了更高的数据覆盖度。此外，我们表明，与SPACE1所发现的相比，SPACE2所识别的功能一致的结构簇在序列、遗传谱系和物种起源上更加多样。这些结果重申，结构数据提高了我们识别结合相同表位的抗体的能力，为基于序列的方法增添了信息，尤其是在来自不同来源的抗体数据集中。SPACE2可在GitHub（https://github.com/oxpig/SPACE2）上公开获取。
