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Ensemble completeness in conformer sampling: the case of small macrocycles
Journal of Cheminformatics ( IF 7.1 ) Pub Date : 2021-07-29 , DOI: 10.1186/s13321-021-00524-0
Lea Seep 1 , Anne Bonin 1 , Katharina Meier 1 , Holger Diedam 2 , Andreas H Göller 1
Affiliation  

In this study we compare the three algorithms for the generation of conformer ensembles Biovia BEST, Schrödinger Prime macrocycle sampling (PMM) and Conformator (CONF) form the University of Hamburg, with ensembles derived for exhaustive molecular dynamics simulations applied to a dataset of 7 small macrocycles in two charge states and three solvents. Ensemble completeness is a prerequisite to allow for the selection of relevant diverse conformers for many applications in computational chemistry. We apply conformation maps using principal component analysis based on ring torsions. Our major finding critical for all applications of conformer ensembles in any computational study is that maps derived from MD with explicit solvent are significantly distinct between macrocycles, charge states and solvents, whereas the maps for post-optimized conformers using implicit solvent models from all generator algorithms are very similar independent of the solvent. We apply three metrics for the quantification of the relative covered ensemble space, namely cluster overlap, variance statistics, and a novel metric, Mahalanobis distance, showing that post-optimized MD ensembles cover a significantly larger conformational space than the generator ensembles, with the ranking PMM > BEST >> CONF. Furthermore, we find that the distributions of 3D polar surface areas are very similar for all macrocycles independent of charge state and solvent, except for the smaller and more strained compound 7, and that there is also no obvious correlation between 3D PSA and intramolecular hydrogen bond count distributions.

中文翻译:


构象异构体采样中的集成完整性:小大环的情况



在本研究中,我们比较了来自汉堡大学的 Biovia BEST、Schrödinger Prime 大环采样 (PMM) 和 Conformator (CONF) 的构象异构体集成的三种生成算法,并导出了用于详尽分子动力学模拟的集成,应用于 7 个小数据集两种电荷状态和三种溶剂的大环化合物。整体完整性是为计算化学中的许多应用选择相关的不同构象异构体的先决条件。我们使用基于环扭转的主成分分析来应用构象图。我们的主要发现对于任何计算研究中构象异构体系综的所有应用都至关重要,即使用显式溶剂从 MD 导出的图在大环、电荷态和溶剂之间显着不同,而使用来自所有生成器算法的隐式溶剂模型的优化后构象异构体的图与溶剂无关,非常相似。我们应用了三个度量来量化相对覆盖的集成空间,即簇重叠、方差统计和一种新的度量,马哈拉诺比斯距离,表明优化后的 MD 集成覆盖了比生成器集成更大的构象空间,并且排名PMM > 最佳>> 配置。此外,我们发现,除了较小且应变较大的化合物 7 之外,所有大环的 3D 极性表面积的分布都非常相似,与电荷状态和溶剂无关,并且 3D PSA 和分子内氢键之间也没有明显的相关性计数分布。
更新日期:2021-07-29
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