Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, U.K.
Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States.
J Chem Inf Model. 2021 Feb 22;61(2):743-755. doi: 10.1021/acs.jcim.0c01144. Epub 2021 Feb 5.
The geometry of a molecule plays a significant role in determining its physical and chemical properties. Despite its importance, there are relatively few studies on ring puckering and conformations, often focused on small cycloalkanes, 5- and 6-membered carbohydrate rings, and specific macrocycle families. We lack a general understanding of the puckering preferences of medium-sized rings and macrocycles. To address this, we provide an extensive conformational analysis of a diverse set of rings. We used Cremer-Pople puckering coordinates to study the trends of the ring conformation across a set of 140 000 diverse small molecules, including small rings, macrocycles, and cyclic peptides. By standardizing using key atoms, we show that the ring conformations can be classified into relatively few conformational clusters, based on their canonical forms. The number of such canonical clusters increases slowly with ring size. Ring puckering motions, especially pseudo-rotations, are generally restricted and differ between clusters. More importantly, we propose models to map puckering preferences to torsion space, which allows us to understand the inter-related changes in torsion angles during pseudo-rotation and other puckering motions. Beyond ring puckers, our models also explain the change in substituent orientation upon puckering. We also present a novel knowledge-based sampling method using the puckering preferences and coupled substituent motion to generate ring conformations efficiently. In summary, this work provides an improved understanding of general ring puckering preferences, which will in turn accelerate the identification of low-energy ring conformations for applications from polymeric materials to drug binding.
分子的几何形状在决定其物理和化学性质方面起着重要作用。尽管它很重要,但关于环扭转和构象的研究相对较少,通常集中在小环烷烃、5-和 6 元碳水化合物环以及特定的大环家族上。我们对中等大小的环和大环的扭转偏好缺乏全面的了解。为了解决这个问题,我们对一组多样化的环进行了广泛的构象分析。我们使用 Cremer-Pople 扭转坐标研究了一组 140000 个不同小分子中环构象的趋势,包括小环、大环和环肽。通过使用关键原子进行标准化,我们表明,根据它们的典型形式,环构象可以分为相对较少的构象簇。这样的典型簇的数量随着环的大小缓慢增加。环扭转运动,特别是拟旋转,通常受到限制,并且在簇之间有所不同。更重要的是,我们提出了将扭转空间映射到扭转空间的模型,这使我们能够理解在拟旋转和其他扭转运动过程中扭转角的相关变化。除了环扭转外,我们的模型还解释了扭转时取代基取向的变化。我们还提出了一种新颖的基于知识的采样方法,使用扭转偏好和耦合取代基运动来有效地生成环构象。总之,这项工作提供了对一般环扭转偏好的更好理解,这反过来又将加速识别用于从聚合材料到药物结合的低能环构象。