Department of Chemistry, College of Science and Health, William Paterson University, 300 Pompton Road, Wayne, NJ 07470, USA.
Biomolecules. 2024 Sep 9;14(9):1136. doi: 10.3390/biom14091136.
NMR utilization in fragment-based drug discovery requires techniques to detect weakly binding fragments and to subsequently identify cooperatively binding fragments. Such cooperatively binding fragments can then be optimized or linked in order to develop viable drug candidates. Similarly, ligands or substrates that bind macromolecules (including enzymes) in competition with the endogenous ligand or substrate are valuable probes of macromolecular chemistry and function. The lengthy and costly process of identifying competitive or cooperative binding can be streamlined by coupling computational biochemistry and spectroscopy tools. The Clustering of Ligand Diffusion Coefficient Pairs (CoLD-CoP) method, previously developed by Snyder and co-workers, detects weakly binding ligands by analyzing pairs of diffusion spectra, obtained in the absence and the presence of a protein. We extended the CoLD-CoP method to analyze spectra pairs (each in the presence of a protein) with or without a critical ligand, to detect both competitive and cooperative binding.
NMR 在基于片段的药物发现中的应用需要能够检测弱结合片段的技术,并随后识别协同结合的片段。然后可以对这些协同结合的片段进行优化或连接,以开发可行的药物候选物。同样,与内源性配体或底物竞争结合大分子(包括酶)的配体或底物是研究大分子化学和功能的有价值的探针。通过将计算生物化学和光谱工具相结合,可以简化识别竞争或协同结合的漫长而昂贵的过程。Snyder 及其同事先前开发的配体扩散系数对(CoLD-CoP)方法通过分析在没有和存在蛋白质的情况下获得的扩散光谱对来检测弱结合配体。我们将 CoLD-CoP 方法扩展到分析具有或不具有关键配体的光谱对(每个光谱对都存在蛋白质),以检测竞争和协同结合。