Abstract：
The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF1% and EF5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF1% and EF5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF1% and EF5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.
摘要：
最近大量GPCR晶体结构的可用性提供了前所未有的机会，可以使用已建立的基准测试数据集来评估其在虚拟筛选协议中的性能。在这项研究中,我们评估了MM/GBSA在基于共识评分的虚拟筛选富集中的能力以及9个经典评分函数，使用由24个GPCR晶体结构和254,646种活性物质和诱饵组成的GPCR-Bench数据集。虽然共识评分的表现总体上是适度的，与经典评分函数的组合相比，包含MM/GBSA的组合表现相对较好。MM/GBSA和性能良好的评分函数的组合提供了最高比例的改进，在所有目标中，所有组合的32％和19％分别在EF1％和EF5％水平上观察到改善。MM/GBSA和表现不佳的评分函数的组合仍然优于经典的评分函数，在EF1%和EF5%的水平下，所有组合的26%和17%都有改善。相比之下,只有14-22%和6-11%的经典评分函数组合分别在EF1%和EF5%产生改善.通过将共识评分中的评分功能数量增加到三个来提高绩效的努力大多无效。我们还观察到，共识评分对于具有最初低富集因子的个体评分函数表现更好，潜在的暗示他们的好处在这种情况下更相关。总的来说,这项研究证明了MM/GBSA在GPCR-Bench数据集的共识评分中的首次实现,并且与GPCR共识评分中的经典评分函数相比,可以为MM/GBSA的性能提供有价值的基准.
