Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA.
Department of Chemistry, University of California, Irvine, CA, 92697, USA.
J Comput Aided Mol Des. 2020 Feb;34(2):163-177. doi: 10.1007/s10822-019-00249-1. Epub 2019 Nov 28.
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.
药物设计数据资源（D3R）大挑战提供了一个机会，可以在盲目的预测性挑战的背景下，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 大挑战 4（GC4）的结果，该挑战主要集中在预测针对β-淀粉样前体蛋白（BACE-1）的化合物的结合构象和亲和力排序。我们使用 HYBRID（OpenEye Scientific Software）的基于配体相似性的方案成功地确定了大多数配体的接近天然结合模式的构象，其均方根偏差（RMSD）精度小于 2Å。此外，我们比较了我们基于 HYBRID 的方法与 AutoDock Vina 和 DOCK 6 的性能，发现使用参考配体来指导对接过程是一种更好的构象预测策略，这有助于 HYBRID 在此处表现更好。我们还使用基于分子力学的广义 Born 表面积方法（MM-GBSA）对基于分子动力学的蛋白质-配体复合物的集合进行了无终点自由能估计。我们发现，基于 MM-GBSA 分数的结合亲和力排序与实验值相关性较差。最后，我们从参与 D3R GC4 中得出的主要经验教训是：（i）大环构象的生成是成功预测构象的关键步骤，（ii）BACE-1 结合位点的质子化状态应谨慎处理，（iii）MM-GBSA 方法不能很好地区分不同的预测结合构象，以及（iv）MM-GBSA 方法在此处不能很好地预测蛋白质-配体结合亲和力。
