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Computational studies of molecular pre-organization through macrocyclization: Conformational distribution analysis of closely related non-macrocyclic and macrocyclic analogs
Bioorganic & Medicinal Chemistry ( IF 3.5 ) Pub Date : 2021-09-11 , DOI: 10.1016/j.bmc.2021.116399
Gustav Olanders 1 , Peter Brandt 2 , Christian Sköld 1 , Anders Karlén 1
Affiliation  

Macrocycles form an important compound class in medicinal chemistry due to their interesting structural and biological properties. To help design macrocycles, it is important to understand how the conformational preferences are affected upon macrocyclization of a lead compound. To address this, we collected a unique data set of protein–ligand complexes containing “non-macrocyclic” (“linear”) ligands matched with macrocyclic analogs binding to the same protein in a similar pose. Out of the 39 co-crystallized ligands considered, 10 were linear and 29 were macrocyclic. To enable a more general analysis, 128 additional ligands from the publications associated with these protein data bank entries were added to the data set. Using in total 167 collected ligands, we investigated if the conformers in the macrocyclic conformational ensembles were more similar to the bioactive conformation in comparison to the conformers of their linear counterparts. Unexpectedly, in most cases the macrocycle conformational ensemble distributions were not very different from those of the linear compounds. Thus, care should be taken when designing macrocycles with the aim to focus their conformational preference towards the bioactive conformation. We also set out to investigate potential conformational flexibility differences between the two compound classes, computational energy window settings and evaluate a literature metric for approximating the conformational focusing on the bioactive conformation.



中文翻译:

通过大环化进行分子预组织的计算研究:密切相关的非大环和大环类似物的构象分布分析

由于其有趣的结构和生物学特性,大环化合物在药物化学中形成了重要的化合物类别。为了帮助设计大环,重要的是要了解构象偏好如何影响先导化合物的大环化。为了解决这个问题,我们收集了一组独特的蛋白质-配体复合物数据集,其中含有“非大环”(“线性”)配体,与以相似姿势结合相同蛋白质的大环类似物相匹配。在考虑的 39 个共结晶配体中,10 个是线性的,29 个是大环的。为了进行更一般的分析,将与这些蛋白质数据库条目相关的出版物中的 128 个附加配体添加到数据集中。总共使用 167 个收集的配体,我们研究了与其线性对应物的构象异构体相比,大环构象集合中的构象异构体是否更类似于生物活性构象。出乎意料的是,在大多数情况下,大环构象集合分布与线性化合物的分布没有太大区别。因此,在设计大环化合物时应小心,旨在将它们的构象偏好集中在生物活性构象上。我们还着手研究这两种化合物类别之间潜在的构象灵活性差异、计算能量窗口设置,并评估一种文献度量,以近似聚焦于生物活性构象的构象。在大多数情况下,大环构象集合分布与线性化合物的分布没有太大区别。因此,在设计大环化合物时应小心,旨在将它们的构象偏好集中在生物活性构象上。我们还着手研究这两种化合物类别之间潜在的构象灵活性差异、计算能量窗口设置,并评估一种文献度量,以近似聚焦于生物活性构象的构象。在大多数情况下,大环构象集合分布与线性化合物的分布没有太大区别。因此,在设计大环化合物时应小心,旨在将它们的构象偏好集中在生物活性构象上。我们还着手研究这两种化合物类别之间潜在的构象灵活性差异、计算能量窗口设置,并评估一种文献度量,以近似聚焦于生物活性构象的构象。

更新日期:2021-09-30
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