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基于结构的 KRAS 抑制剂设计中的受体柔性建模。

Modeling receptor flexibility in the structure-based design of KRAS inhibitors.

机构信息

Department of Molecular Engineering, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320, USA.

Amgen Asia R&D Center, 13th Floor, Building No. 2, 4560 Jinke Road, Zhangjiang, Shanghai, 201210, China.

出版信息

J Comput Aided Mol Des. 2022 Aug;36(8):591-604. doi: 10.1007/s10822-022-00467-0. Epub 2022 Aug 5.

Abstract

KRAS has long been referred to as an 'undruggable' target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRAS has revealed that occupancy of an allosteric binding site located between the α3-helix and switch-II loop of KRAS-sometimes referred to as the 'switch-II pocket'-holds great potential in the design of direct inhibitors of KRAS. In studying diverse switch-II pocket binders during the development of sotorasib (AMG 510), the first FDA-approved inhibitor of KRAS, we found the dramatic conformational flexibility of the switch-II pocket posing significant challenges toward the structure-based design of inhibitors. Here, we present our computational approaches for dealing with receptor flexibility in the prediction of ligand binding pose and binding affinity. For binding pose prediction, we modified the covalent docking program CovDock to allow for protein conformational mobility. This new docking approach, termed as FlexCovDock, improves success rates from 55 to 89% for binding pose prediction on a dataset of 10 cross-docking cases and has been prospectively validated across diverse ligand chemotypes. For binding affinity prediction, we found standard free energy perturbation (FEP) methods could not adequately handle the significant conformational change of the switch-II loop. We developed a new computational strategy to accelerate conformational transitions through the use of targeted protein mutations. Using this methodology, the mean unsigned error (MUE) of binding affinity prediction were reduced from 1.44 to 0.89 kcal/mol on a set of 14 compounds. These approaches were of significant use in facilitating the structure-based design of KRAS inhibitors and are anticipated to be of further use in the design of covalent (and noncovalent) inhibitors of other conformationally labile protein targets.

摘要

KRAS 长期以来被称为“不可成药”的靶点,因为它与同源配体(GDP 和 GTP)具有高亲和力,并且缺乏易于利用的变构结合口袋。最近,KRAS 共价抑制剂的开发取得了进展,揭示了占据 KRAS 的 α3-螺旋和开关 II 环之间的变构结合位点(有时称为“开关 II 口袋”)在设计 KRAS 的直接抑制剂方面具有巨大潜力。在研究 sotorasib(AMG 510,首个获 FDA 批准的 KRAS 抑制剂)开发过程中的各种开关 II 口袋结合物时,我们发现开关 II 口袋的剧烈构象灵活性对基于结构的抑制剂设计构成了重大挑战。在这里,我们介绍了我们在预测配体结合构象和结合亲和力时处理受体灵活性的计算方法。对于结合构象预测,我们修改了共价对接程序 CovDock 以允许蛋白质构象移动。这种新的对接方法称为 FlexCovDock,将 10 个交叉对接案例数据集的结合构象预测成功率从 55%提高到 89%,并在各种配体化学型中进行了前瞻性验证。对于结合亲和力预测,我们发现标准自由能微扰 (FEP) 方法不能充分处理开关 II 环的显著构象变化。我们开发了一种新的计算策略,通过使用靶向蛋白突变来加速构象转变。使用这种方法,在一组 14 种化合物上,结合亲和力预测的平均未修正误差 (MUE) 从 1.44 降低到 0.89 kcal/mol。这些方法在促进 KRAS 抑制剂的基于结构的设计方面非常有用,预计在设计其他构象不稳定的蛋白质靶标(包括共价(和非共价)抑制剂)方面也将进一步发挥作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cc1/9512760/047f905cf557/10822_2022_467_Fig1_HTML.jpg

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