Center for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylor's, 47500, Subang Jaya, Selangor, Malaysia.
School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylor's, 47500, Subang Jaya, Selangor, Malaysia.
J Comput Aided Mol Des. 2020 Nov;34(11):1133-1145. doi: 10.1007/s10822-020-00339-5. Epub 2020 Aug 27.
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 and nine targets at EF, with the gains in enrichment being more pronounced at the EF 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.
近年来，G 蛋白偶联受体（GPCR）晶体学的突破以及随后解决的 GPCR 结构数量的增加，使得利用其实验结构进行基于结构的药物发现应用成为可能。由于虚拟筛选代表了用于发现新型先导物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方案之间的比较。在这项研究中，我们使用整个 GPCR-Bench 数据集（包含 24 个 GPCR 靶标和 254,646 个活性和非活性化合物），比较了分子力学/泊松-玻尔兹曼表面面积（MM/PBSA）与 Glide 对接在提高虚拟筛选富集方面的性能。使用 MM/PBSA 对对接数据集的前 10%进行重新排序，在 EF 和 EF 时，对六个靶标和九个靶标都有改善，EF 水平的富集增益更为明显。我们还评估了重新评分对接的前十个构象以及短 MD 模拟在进行 MM/PBSA 计算之前优化结合构象的能力。在这两种情况下，都没有观察到明显的受益趋势，这表明在虚拟筛选中，使用单个能量最小化结构进行 MM/PBSA 计算可能是最具计算效率的方法。总的来说，从 GPCR-Bench 数据集的对接中使用 MM/PBSA 重新评分提高虚拟筛选富集的性能相对较小且具有靶标特异性，这突出了在前瞻性使用之前验证基于 MM/PBSA 的方案的必要性。
