Mapping of antibody epitopes based on docking and homology modeling
Mapping of antibody epitopes based on docking and homology modeling
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DOI：
10.1002/prot.26420
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发表时间：
2022-09-30
影响因子：
2.9
通讯作者：
Kozakov, Dima
中科院分区：
文献类型：
--
作者：
Desta, Israel T.;Kotelnikov, Sergei;Kozakov, Dima
抗体是由免疫系统产生的关键蛋白质，通过特异性结合称为表位的表面区域来靶向称为抗原的病原体蛋白质。给定抗原和抗体的序列，表位的知识对于基于抗体的治疗剂的发现和开发至关重要。在这项工作中，我们提出了一个计算协议，使用基于模板的建模和对接来预测表位残基。该协议分三个主要步骤实施。首先，使用基于模板的建模方法来构建抗体结构。我们测试了几个选项，包括使用AlphaFold 2生成模型。其次，使用基于快速傅立叶变换（FFT）的对接程序PIPER将每个抗体模型对接到抗原。注意根据输入数据最佳地选择对接能量参数。特别是，相对于X射线结构，模型化抗体的货车德瓦尔斯能量项减少。最后，产生抗原表面残基的排序。排序依赖于对接结果，即，残留物在对接姿势的界面中出现的频率，并且还依赖于所讨论的对接姿势的能量可重复性。该方法被称为PIPER-Map，已在广泛使用的抗体-抗原对接基准上进行了测试。结果表明，PIPER-Map改进了现有的表位预测方法。一个有趣的观察是，从单独的抗体序列开始的表位预测准确性与从未结合的（即，单独结晶的）抗体结构。
Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody-antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.