Recent breakthroughs in G protein-coupled receptor (GPCR) crystallography and the subsequent increase in number of solved GPCR structures has allowed for the unprecedented opportunity to utilize their experimental structures for structure-based drug discovery applications. As virtual screening represents one of the primary computational methods used for the discovery of novel leads, the GPCR-Bench dataset was created to facilitate comparison among various virtual screening protocols. In this study, we have benchmarked the performance of Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) in improving virtual screening enrichment in comparison to docking with Glide, using the entire GPCR-Bench dataset of 24 GPCR targets and 254,646 actives and decoys. Reranking the top 10% of the docked dataset using MM/PBSA resulted in improvements for six targets at EF_1% and nine targets at EF_5%, with the gains in enrichment being more pronounced at the EF_1% level. We additionally assessed the utility of rescoring the top ten poses from docking and the ability of short MD simulations to refine the binding poses prior to MM/PBSA calculations. There was no clear trend of the benefit observed in both cases, suggesting that utilizing a single energy minimized structure for MM/PBSA calculations may be the most computationally efficient approach in virtual screening. Overall, the performance of MM/PBSA rescoring in improving virtual screening enrichment obtained from docking of the GPCR-Bench dataset was found to be relatively modest and target-specific, highlighting the need for validation of MM/PBSA-based protocols prior to prospective use.

G 蛋白偶联受体 (GPCR) 晶体学的最新突破以及随后解决的 GPCR 结构数量的增加为利用其实验结构进行基于结构的药物发现应用提供了前所未有的机会。由于虚拟筛选是用于发现新线索的主要计算方法之一，因此创建了 GPCR-Bench 数据集以促进各种虚拟筛选协议之间的比较。在本研究中，我们使用包含 24 个 GPCR 靶标和 254,646 个活性成分的整个 GPCR-Bench 数据集，对分子力学/泊松-玻尔兹曼表面积 (MM/PBSA) 与与 Glide 对接相比在改进虚拟筛选富集方面的性能进行了基准测试。诱饵。_1％ ，并在EF 9个项目标_5％ ，与富集的收益被更明显的EF _1％等级。我们还评估了从对接中重新评分前十个姿势的效用以及短 MD 模拟在 MM/PBSA 计算之前改进结合姿势的能力。在这两种情况下都没有观察到明显的好处趋势，这表明利用单个能量最小化结构进行 MM/PBSA 计算可能是虚拟筛选中计算效率最高的方法。总体而言，发现 MM/PBSA 重新评分在改善从 GPCR-Bench 数据集对接获得的虚拟筛选富集方面的性能相对适中且具有目标特异性，突出了在前瞻性使用之前验证基于 MM/PBSA 的协议的必要性.