Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson-Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, AutoDock4, to that of MM-GBSA in predicting the binding affinities of a set of [Formula: see text]-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: (i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, (ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, (iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding affinity predictions.

中文翻译：

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在 D3R Grand Challenge 4 中使用 AutoDock-GPU 和 MM-GBSA 评分对 BACE-1 抑制剂的亲和力排名进行比较。


分子对接已成功用于计算机辅助分子设计项目，用于识别蛋白质结合位点内的配体姿势。然而，依靠对接分数对不同配体的实验亲和力进行排名可能还不够。据信，使用分子力学结合泊松-玻尔兹曼表面积（MM-PBSA）或广义玻恩表面积（MM-GBSA）计算的结合分数可以更准确地预测结合亲和力。从这个角度来看，我们决定参加药物设计数据资源 (D3R) Grand Challenge 4 (GC4) 的第 2 阶段，以比较快速评分函数 AutoDock4 与 MM-GBSA 在预测结合亲和力方面的性能一组[分子式：见文字]-淀粉样蛋白裂解酶 1 (BACE-1) 配体。我们的结果表明，使用 MM-GBSA 重新评分对接姿势并没有改善与实验亲和力的相关性。我们进一步对结果进行了回顾性分析，发现我们的 MM-GBSA 协议对蛋白质-配体系统中的细节敏感：（i）中性配体比带电配体更适合 MM-GBSA 计算，（ii）预测结合亲和力取决于 BACE-1 受体的初始构象，(iii) 正确质子化 BACE-1 的天冬氨酰二联体可导致更准确的结合亲和力预测。