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在 D3R 大分子对接挑战赛 4 中使用 Convex-PL、AutoDock Vina 和 RDKit 对接刚性大环。

Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4.

机构信息

Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000, Grenoble, France.

Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700.

出版信息

J Comput Aided Mol Des. 2020 Feb;34(2):191-200. doi: 10.1007/s10822-019-00263-3. Epub 2019 Nov 29.

Abstract

The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.

摘要

D3R 大分子对接挑战赛 4 为我们提供了一个绝佳的机会,让我们可以在多样化的高质量实验数据上测试大环对接方案。我们参加了构象预测和亲和力预测两个项目。总的来说,我们的目标是使用一个自动化的基于结构的对接管道,该管道围绕我们团队开发的一组工具构建。这一实践再次证明了正确的局部配体几何形状对对接整体成功的重要性。从构象预测阶段的第二部分开始,我们开发了一个稳定的大环构象采样管道。这使得我们的构象预测平均精度达到了亚埃。在亲和力预测项目中,我们得到了平均成绩。然而,当我们使用最佳预测器提交的对接构象时,我们可以提高这些成绩。我们的对接工具包括 Convex-PL 评分函数,可在 https://team.inria.fr/nano-d/software/ 上获取。

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