Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3

被引:60
作者
Graef, Joel [1 ]
Ehrt, Christiane [1 ]
Rarey, Matthias [1 ]
机构
[1] Univ Hamburg, ZBH Ctr Bioinformat, D-20146 Hamburg, Germany
关键词
SC-PDB; PREDICTION; DATABASE; IDENTIFICATION; DRUG; DRUGGABILITY; COMPILATION; CAVITIES; SEQUENCE; LIGANDS;
D O I
10.1021/acs.jcim.3c00336
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Binding site prediction on protein structures is a crucial step in early phase drug discovery whenever experimental or predicted structure models are involved. DoGSite belongs to the widely used tools for this task. It is a grid-based method that uses a Difference-of-Gaussian filter to detect cavities on the protein surface. We recently reimplemented the first version of this method, released in 2010, focusing on improved binding site detection in the presence of ligands and optimized parameters for more robust, reliable, and fast predictions and binding site descriptor calculations. Here, we introduce the new version, DoGSite3, compare it to its predecessor, and re-evaluate DoGSite on published data sets for a large-scale comparative performance evaluation.
引用
收藏
页码:1 / 10
页数:10
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