Mercier Kelly A, Powers Robert
Department of Chemistry, University of Nebraska Lincoln, 722 Hamilton Hall, Lincoln, NE 68522-0304, USA.
J Biomol NMR. 2005 Mar;31(3):243-58. doi: 10.1007/s10858-005-0948-4.
High-throughput screening (HTS) using NMR spectroscopy has become a common component of the drug discovery effort and is widely used throughout the pharmaceutical industry. NMR provides additional information about the nature of small molecule-protein interactions compared to traditional HTS methods. In order to achieve comparable efficiency, small molecules are often screened as mixtures in NMR-based assays. Nevertheless, an analysis of the efficiency of mixtures and a corresponding determination of the optimum mixture size (OMS) that minimizes the amount of material and instrumentation time required for an NMR screen has been lacking. A model for calculating OMS based on the application of the hypergeometric distribution function to determine the probability of a "hit" for various mixture sizes and hit rates is presented. An alternative method for the deconvolution of large screening mixtures is also discussed. These methods have been applied in a high-throughput NMR screening assay using a small, directed library.
使用核磁共振光谱的高通量筛选(HTS)已成为药物发现工作的常见组成部分,并在整个制药行业中广泛使用。与传统的高通量筛选方法相比,核磁共振能提供有关小分子与蛋白质相互作用性质的额外信息。为了实现可比的效率,在基于核磁共振的分析中,小分子通常作为混合物进行筛选。然而,目前缺乏对混合物效率的分析以及对最佳混合物大小(OMS)的相应确定,而最佳混合物大小能将核磁共振筛选所需的材料量和仪器时间减至最少。本文提出了一个基于超几何分布函数应用来计算最佳混合物大小的模型,以确定各种混合物大小和命中率下“命中”的概率。还讨论了一种用于大型筛选混合物去卷积的替代方法。这些方法已应用于使用小型定向文库的高通量核磁共振筛选分析中。