Institute of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck , University of Innsbruck , 6020 Innsbruck , Austria.
Medicinal Chemistry , Boehringer Ingelheim Pharma GmbH & Co. KG , 88397 Biberach , Germany.
J Chem Inf Model. 2018 May 29;58(5):982-992. doi: 10.1021/acs.jcim.8b00097. Epub 2018 Apr 20.
Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensemble. By applying an energetic cutoff, followed by geometric clustering, we demonstrate the striking robustness and efficiency of the approach in identifying highly populated conformational states of cyclic peptides. The resulting structural and thermodynamic information is benchmarked against interproton distances from NMR experiments and conformational states identified by X-ray crystallography. Using three different model systems of varying size and flexibility, we show that the method reliably reproduces experimentally determined structural ensembles and is capable of identifying key conformational states that include the bioactive conformation. Thus, the described approach is a robust method to generate conformations of peptidic macrocycles and holds promise for structure-based drug design.
大环化合物作为高度特异性的药物候选物备受关注，但它们的大量可旋转键和构象限制给标准构象生成器带来了挑战。在这里，我们提出了一种基于分子动力学的方法，该方法绕过了构象采样中的当前限制，并广泛分析了溶液中肽大环的自由能景观。我们进行加速分子动力学模拟以捕获多样化的构象系综。通过应用能量截止值，然后进行几何聚类，我们证明了该方法在识别环状肽的高占据构象态方面具有惊人的稳健性和效率。所得的结构和热力学信息与 NMR 实验中的质子间距离和 X 射线晶体学确定的构象态进行了基准测试。使用三个不同大小和灵活性的模型系统，我们表明该方法可靠地再现了实验确定的结构系综，并且能够识别包括生物活性构象的关键构象态。因此，所描述的方法是生成肽大环构象的稳健方法，并有望用于基于结构的药物设计。
