Auld Douglas S, Jimenez Marta, Yue Kimberley, Busby Scott, Chen Yu-Chi, Bowes Scott, Wendel Greg, Smith Thomas, Zhang Ji-Hu
1 Novartis Institutes for Biomedical Research, Center for Proteomic Chemistry, Cambridge, MA, USA.
2 National Center for Advancing Translational Sciences, Bethesda, MD, USA.
J Biomol Screen. 2016 Dec;21(10):1075-1089. doi: 10.1177/1087057116667255. Epub 2016 Sep 27.
One of the central questions in the characterization of enzyme inhibitors is determining the mode of inhibition (MOI). Classically, this is done with a number of low-throughput methods in which inhibition models are fitted to the data. The ability to rapidly characterize the MOI for inhibitors arising from high-throughput screening in which hundreds to thousands of primary inhibitors may need to be characterized would greatly help in lead selection efforts. Here we describe a novel method for determining the MOI of a compound without the need for curve fitting of the enzyme inhibition data. We provide experimental data to demonstrate the utility of this new high-throughput MOI classification method based on nonparametric analysis of the activity derived from a small matrix of substrate and inhibitor concentrations (e.g., from a 4 × 4 matrix). Lists of inhibitors from four different enzyme assays are studied, and the results are compared with the previously described IC-shift method for MOI classification. The MOI results from this method are in good agreement with the known MOI and compare favorably with those from the IC-shift method. In addition, we discuss some advantages and limitations of the method and provide recommendations for utilization of this MOI classification method.
在酶抑制剂特性描述中，核心问题之一是确定抑制模式（MOI）。传统上，这是通过一些低通量方法来完成的，在这些方法中，抑制模型被拟合到数据上。对于高通量筛选产生的抑制剂，能够快速表征其MOI（其中可能需要对数百至数千种初级抑制剂进行表征）将极大地有助于先导化合物的筛选工作。在此，我们描述了一种无需对酶抑制数据进行曲线拟合即可确定化合物MOI的新方法。我们提供实验数据来证明这种基于对来自底物和抑制剂浓度小矩阵（例如，4×4矩阵）的活性进行非参数分析的新高通量MOI分类方法的实用性。研究了来自四种不同酶测定的抑制剂列表，并将结果与先前描述的用于MOI分类的IC位移方法进行比较。该方法得到的MOI结果与已知的MOI高度一致，并且与IC位移方法得到的结果相比更具优势。此外，我们讨论了该方法的一些优点和局限性，并为该MOI分类方法的应用提供了建议。
