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通过 NMR 引导对接的高置信度蛋白-配体复合物建模可实现早期命中优化。

High-Confidence Protein-Ligand Complex Modeling by NMR-Guided Docking Enables Early Hit Optimization.

机构信息

Global Discovery Chemistry, Novartis Institutes for BioMedical Research , 5300 Chiron Way, Emeryville, California 94608, United States.

Global Discovery Chemistry, Novartis Institutes for BioMedical Research , Novartis Campus, 4056 Basel, Switzerland.

出版信息

J Am Chem Soc. 2017 Dec 13;139(49):17824-17833. doi: 10.1021/jacs.7b07171. Epub 2017 Nov 30.

Abstract

Structure-based drug design is an integral part of modern day drug discovery and requires detailed structural characterization of protein-ligand interactions, which is most commonly performed by X-ray crystallography. However, the success rate of generating these costructures is often variable, in particular when working with dynamic proteins or weakly binding ligands. As a result, structural information is not routinely obtained in these scenarios, and ligand optimization is challenging or not pursued at all, representing a substantial limitation in chemical scaffolds and diversity. To overcome this impediment, we have developed a robust NMR restraint guided docking protocol to generate high-quality models of protein-ligand complexes. By combining the use of highly methyl-labeled protein with experimentally determined intermolecular distances, a comprehensive set of protein-ligand distances is generated which then drives the docking process and enables the determination of the correct ligand conformation in the bound state. For the first time, the utility and performance of such a method is fully demonstrated by employing the generated models for the successful, prospective optimization of crystallographically intractable fragment hits into more potent binders.

摘要

基于结构的药物设计是现代药物发现的一个组成部分,需要对蛋白质-配体相互作用进行详细的结构特征描述,这通常通过 X 射线晶体学来完成。然而,生成这些共结构的成功率往往是可变的,特别是在处理动态蛋白质或弱结合配体时。因此,在这些情况下通常无法获得结构信息,配体优化具有挑战性,或者根本不进行,这代表了化学支架和多样性的重大限制。为了克服这一障碍,我们开发了一种强大的 NMR 约束引导对接方案,以生成高质量的蛋白质-配体复合物模型。通过将高度甲基化的蛋白质与实验确定的分子间距离相结合,生成了一套全面的蛋白质-配体距离,然后驱动对接过程,并能够确定结合状态下正确的配体构象。首次通过使用生成的模型成功地对晶体学上难以处理的片段进行前瞻性优化,将其转化为更有效的结合物,充分证明了这种方法的实用性和性能。

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