Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, CA 92093-0365, USA.
J Mol Graph Model. 2011 Feb;29(5):773-6. doi: 10.1016/j.jmgm.2010.10.007. Epub 2010 Nov 3.
Researchers engaged in computer-aided drug design often wish to measure the volume of a ligand-binding pocket in order to predict pharmacology. We have recently developed a simple algorithm, called POVME (POcket Volume MEasurer), for this purpose. POVME is Python implemented, fast, and freely available. To demonstrate its utility, we use the new algorithm to study three members of the matrix-metalloproteinase family of proteins. Despite the structural similarity of these proteins, differences in binding-pocket dynamics are easily identified.
从事计算机辅助药物设计的研究人员通常希望测量配体结合口袋的体积，以预测药理学。为此，我们最近开发了一种简单的算法，称为 POVME（口袋体积测量器）。POVME 是用 Python 实现的，速度快，并且可以免费使用。为了证明其效用，我们使用新算法研究了基质金属蛋白酶家族的三个蛋白质成员。尽管这些蛋白质具有结构相似性，但结合口袋动力学的差异很容易识别。
