Weisel Martin, Proschak Ewgenij, Schneider Gisbert
Johann Wolfgang Goethe-Universität, Beilstein Endowed Chair for Cheminformatics, Institut für Organische Chemie und Chemische Biologie, Frankfurt am Main, Germany.
Chem Cent J. 2007 Mar 13;1:7. doi: 10.1186/1752-153X-1-7.
Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding.
We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITE(cs), PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITE(cs) and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites.
The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections.
识别和评估蛋白质表面结合口袋及封闭腔是基于蛋白质结构的药物设计的初始步骤。表征活性位点的形状以及周围残基的分布对于诸如自动配体对接或原位建模等各种应用起着重要作用。比较相关蛋白质结合位点几何形状的形状相似性可进一步深入了解配体结合机制。
我们提出了PocketPicker，一种基于网格的自动技术，用于预测蛋白质结合口袋，该技术根据潜在结合位点的埋藏程度来确定其形状。该方法应用于一组具有代表性的蛋白质-配体复合物及其相应的无配体蛋白质结构，以评估结合位点预测的质量。将口袋检测程序的性能与现有方法CAST、LIGSITE、LIGSITE(cs)、PASS和SURFNET的结果进行了比较。PocketPicker的成功率与LIGSITE(cs)相当，且优于其他工具。我们引入了一种描述符，将描绘检测到的结合位点的网格点排列转化为相关向量。通过研究醛糖还原酶中的诱导契合现象，我们表明这种形状描述符适用于对相似结合位点几何形状的比较分析。这种新方法利用了从潜在结合位点埋藏度计算中获得的信息。
PocketPicker的口袋预测程序是识别潜在蛋白质结合口袋的有用工具。它能方便地呈现结合位点的形状，包括对其可及性的直观描述。用于结合位点几何形状自动分类的形状描述符可作为补充详细手动检查的附加工具。
