Center for Bioinformatics, Saarland University , 66041 Saarbruecken, Germany.
J Chem Inf Model. 2015 Sep 28;55(9):1944-52. doi: 10.1021/acs.jcim.5b00045. Epub 2015 Sep 11.
Detecting appropriate ligand binding pockets on protein surfaces has several important applications in the drug discovery process. In pocket sets identified by two software packages, PASS and Fpocket, we found a sizable number of protein-ligand complexes where more than one pocket overlaps with the ligand. In such cases, it would be desirable if a merged set of contacting pockets would represent the small molecule. Thus, we tested three clustering approaches to merge the given pockets, a classical clustering method and two methods based on algorithms from graph theory. We found that hierarchical clustering, as well as an approach based on the concept of maximum flow, could be favorably used for clustering pockets predicted either by PASS or by Fpocket.
在药物发现过程中，检测蛋白质表面上合适的配体结合口袋具有几个重要的应用。在 PASS 和 Fpocket 两个软件包识别的口袋集中，我们发现了大量的蛋白质-配体复合物，其中多个口袋与配体重叠。在这种情况下，如果一组合并的接触口袋可以代表小分子，那就非常理想了。因此，我们测试了三种聚类方法来合并给定的口袋，一种经典的聚类方法和两种基于图论算法的方法。我们发现，层次聚类以及基于最大流概念的方法可以很好地用于聚类由 PASS 或 Fpocket 预测的口袋。
