Analyzing the Topology of Active Sites: On the Prediction of Pockets and Subpockets
Analyzing the Topology of Active Sites: On the Prediction of Pockets and Subpockets
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DOI：
10.1021/ci100241y
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发表时间：
2010-11-01
影响因子：
5.6
通讯作者：
Rarey, Matthias
中科院分区：
文献类型：
--
作者：
Volkamer, Andrea;Griewel, Axel;Rarey, Matthias
蛋白质活性位点的自动预测对于大规模蛋白质功能预测、分类和可药用性估计是必不可少的。在这项工作中，我们提出了DoGSite，一个新的基于结构的方法来预测蛋白质中的活性位点的高斯差分（DoG）的方法，起源于图像处理的基础上。与现有的方法相比，DoGSite将预测的口袋分割成子口袋，揭示了活性位点拓扑结构的精细描述。DoGSite正确预测了超过92%的PDBBind和scPDB数据集的结合口袋，与可用的最佳性能方法一致。在63%的PDBBind数据集中，检测到的口袋可以细分为更小的子口袋。在87%的预测中，共结晶配体恰好包含在一个子口袋中。此外，我们引入了一个更精确的预测性能的措施，考虑到成对的配体和口袋覆盖。在90%的情况下，DoGSite预测含有至少一半配体的口袋。在70%的情况下，另外超过四分之一的相应口袋本身被共结晶配体覆盖。考虑到子口袋产生的覆盖面增加，产生83%的成功率为后一项措施。
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.