Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Australia.
Bioinformatics. 2019 May 1;35(9):1591-1593. doi: 10.1093/bioinformatics/bty822.
Methods for antibody structure prediction rely on sequence homology to experimentally determined structures. Resulting models may be accurate but are often stereochemically strained, limiting their usefulness in modeling and design workflows. We present the AbPredict 2 web-server, which instead of using sequence homology, conducts a Monte Carlo-based search for low-energy combinations of backbone conformations to yield accurate and unstrained antibody structures.
We introduce several important improvements over the previous AbPredict implementation: (i) backbones and sidechains are now modeled using ideal bond lengths and angles, substantially reducing stereochemical strain, (ii) sampling of the rigid-body orientation at the light-heavy chain interface is improved, increasing model accuracy and (iii) runtime is reduced 20-fold without compromising accuracy, enabling the implementation of AbPredict 2 as a fully automated web-server (http://abpredict.weizmann.ac.il). Accurate and unstrained antibody model structures may in some cases obviate the need for experimental structures in antibody optimization workflows.
抗体结构预测方法依赖于与实验确定结构的序列同源性。由此产生的模型可能是准确的,但往往在立体化学上存在应变,限制了它们在建模和设计工作流程中的用途。我们介绍了 AbPredict 2 网络服务器,它不是使用序列同源性,而是进行基于蒙特卡罗的搜索,以产生低能量的骨架构象组合,从而得到准确且无应变的抗体结构。
我们在以前的 AbPredict 实现中引入了几个重要的改进:(i)现在使用理想的键长和角度来模拟骨干和侧链,大大减少了立体化学应变,(ii)改进了轻链-重链界面处的刚体取向的采样,提高了模型的准确性,(iii)在不影响准确性的情况下将运行时间缩短了 20 倍,从而能够将 AbPredict 2 实现为一个全自动的网络服务器(http://abpredict.weizmann.ac.il)。在某些情况下,准确且无应变的抗体模型结构可能会避免在抗体优化工作流程中需要实验结构。