Research Group for Computational Molecular Design, Bundesstr. 43, 20146 Hamburg, Germany.
J Chem Inf Model. 2010 Nov 22;50(11):2041-52. doi: 10.1021/ci100241y. Epub 2010 Oct 14.
Automated prediction of protein active sites is essential for large-scale protein function prediction, classification, and druggability estimates. In this work, we present DoGSite, a new structure-based method to predict active sites in proteins based on a Difference of Gaussian (DoG) approach which originates from image processing. In contrast to existing methods, DoGSite splits predicted pockets into subpockets, revealing a refined description of the topology of active sites. DoGSite correctly predicts binding pockets for over 92% of the PDBBind and the scPDB data set, being in line with the best-performing methods available. In 63% of the PDBBind data set the detected pockets can be subdivided into smaller subpockets. The cocrystallized ligand is contained in exactly one subpocket in 87% of the predictions. Furthermore, we introduce a more precise prediction performance measure by taking the pairwise ligand and pocket coverage into account. In 90% of the cases DoGSite predicts a pocket that contains at least half of the ligand. In 70% of the cases additionally more than a quarter of the respective pocket itself is covered by the cocrystallized ligand. Consideration of subpockets produces an increase in coverage yielding a success rate of 83% for the latter measure.
自动预测蛋白质的活性位点对于大规模的蛋白质功能预测、分类和药物可及性估计至关重要。在这项工作中，我们提出了一种新的基于结构的方法 DoGSite，用于基于高斯差分（DoG）方法预测蛋白质中的活性位点，该方法源自图像处理。与现有方法不同，DoGSite 将预测的口袋分为亚口袋，从而更精细地描述了活性位点的拓扑结构。DoGSite 正确预测了 PDBBind 和 scPDB 数据集超过 92%的结合口袋，与现有的表现最佳的方法相当。在 PDBBind 数据集中的 63%的口袋可以进一步细分为更小的亚口袋。在 87%的预测中，共结晶配体恰好位于一个亚口袋中。此外，我们通过考虑配体和口袋的成对覆盖率，引入了一种更精确的预测性能度量。在 90%的情况下，DoGSite 预测的口袋至少包含一半的配体。在 70%的情况下，配体本身的四分之一以上被共结晶配体覆盖。考虑亚口袋会增加覆盖率，后者的成功率达到 83%。
