The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4 (GC4), which focused on predicting the binding poses and affinity ranking for compounds targeting the [Formula: see text]-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 Å RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and DOCK 6 and found that using a reference ligand to guide the docking process is a better strategy for pose prediction and helped HYBRID to perform better here. We also conducted end-point free energy estimates on molecules dynamics based ensembles of protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R GC4 are: (i) the generation of the macrocyclic conformers is a key step for successful pose prediction, (ii) the protonation states of the BACE-1 binding site should be treated carefully, (iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and (iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.

中文翻译：

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D3R 大挑战 4：BACE-1 抑制剂的配体相似性和基于 MM-GBSA 的姿势预测和亲和力排名。


药物设计数据资源 (D3R) 大挑战提供了一个机会，可以在盲目预测挑战的背景下评估旨在帮助指导药物发现项目的工具和方法的准确性和局限性。在这里，我们报告了我们参与 D3R Grand Challenge 4 (GC4) 的结果，该挑战的重点是预测针对 [分子式：见文本]-淀粉样前体蛋白 (BACE-1) 的化合物的结合姿势和亲和力排名。我们使用 HYBRID（OpenEye 科学软件）的基于配体相似性的协议成功地识别了大多数配体的接近天然结合模式的姿势，且精度低于 2 Å RMSD。此外，我们将基于 HYBRID 的方法的性能与 AutoDock Vina 和 DOCK 6 的性能进行了比较，发现使用参考配体来指导对接过程是姿势预测的更好策略，并帮助 HYBRID 在这里表现得更好。我们还利用分子力学结合广义玻恩表面积法 (MM-GBSA) 对基于分子动力学的蛋白质-配体复合物整体进行了终点自由能估计。我们发现基于MM-GBSA评分的结合亲和力排序与实验值的相关性较差。最后，我们参与 D3R GC4 的主要经验教训是：（i）大环构象异构体的生成是成功位姿预测的关键步骤，（ii）应仔细对待 BACE-1 结合位点的质子化状态，（ iii) MM-GBSA 方法不能很好地区分不同的预测结合姿势，并且 (iv) MM-GBSA 方法在预测蛋白质-配体结合亲和力方面表现不佳。