Research & Development, AbbVie, 1 North Waukegan Road, North Chicago, IL, 60064, USA.
Biochemistry Department, University of Missouri, 117 Schweitzer Hall, Columbia, MO, 65211, USA.
J Biomol NMR. 2019 Dec;73(12):675-685. doi: 10.1007/s10858-019-00279-9. Epub 2019 Sep 20.
Protein-based NMR spectroscopy has proven to be a very robust method for finding fragment leads to protein targets. However, one limitation of protein-based NMR is that the data acquisition and analysis can be time consuming. In order to streamline the scoring of protein-based NMR fragment screening data and the determination of ligand affinities using 2D NMR experiments we have developed a data analysis workflow based on principal component analysis (PCA) within the TREND NMR Pro software package. We illustrate this using four different proteins and sets of ligands which interact with these proteins over a range of affinities. Also, these PCA-based methods can be successfully applied even to systems where ligand binding to target proteins is in intermediate or even slow exchange on the NMR time scale. Finally, these methods will work for scoring of fragment binding data using protein spectra that are either highly overlapped or lower in resolution.
基于蛋白质的 NMR 光谱学已被证明是一种非常有效的方法，可以找到针对蛋白质靶标的片段先导物。然而，基于蛋白质的 NMR 的一个限制是数据采集和分析可能很耗时。为了简化基于蛋白质的 NMR 片段筛选数据的评分以及使用 2D NMR 实验确定配体亲和力，我们在 TREND NMR Pro 软件包中基于主成分分析 (PCA) 开发了一种数据分析工作流程。我们使用四个不同的蛋白质和与这些蛋白质相互作用的配体组来说明这一点，这些配体在一系列亲和力范围内与这些蛋白质相互作用。此外，即使在配体与靶蛋白结合处于 NMR 时间尺度上的中间或甚至缓慢交换的情况下，这些基于 PCA 的方法也可以成功应用。最后，这些方法可用于使用分辨率较高或较低的蛋白质谱对片段结合数据进行评分。
