Department of Biochemistry, Chemistry and Pharmacy, Institute of Organic Chemistry and Chemical Biology, LiFF/ZAFES, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany.
J Comput Chem. 2010 Jul 15;31(9):1919-28. doi: 10.1002/jcc.21479.
We present a computational approach to protein-protein docking based on surface shape complementarity ("ProBinder"). Within this docking approach, we implemented a new surface decomposition method that considers local shape features on the protein surface. This new surface shape decomposition results in a deterministic representation of curvature features on the protein surface, such as "knobs," "holes," and "flats" together with their point normals. For the actual docking procedure, we used geometric hashing, which allows for the rapid, translation-, and rotation-free comparison of point coordinates. Candidate solutions were scored based on knowledge-based potentials and steric criteria. The potentials included electrostatic complementarity, desolvation energy, amino acid contact preferences, and a van-der-Waals potential. We applied ProBinder to a diverse test set of 68 bound and 30 unbound test cases compiled from the Dockground database. Sixty-four percent of the protein-protein test complexes were ranked with an root mean square deviation (RMSD) < 5 A to the target solution among the top 10 predictions for the bound data set. In 82% of the unbound samples, docking poses were ranked within the top ten solutions with an RMSD < 10 A to the target solution.
我们提出了一种基于表面形状互补性的蛋白质-蛋白质对接的计算方法（ProBinder）。在这个对接方法中，我们实现了一种新的表面分解方法，该方法考虑了蛋白质表面上的局部形状特征。这种新的表面形状分解导致了蛋白质表面上曲率特征的确定性表示，例如“凸块”、“凹坑”和“平面”以及它们的点法向量。对于实际的对接过程，我们使用了几何哈希，它允许快速、平移和无旋转地比较点坐标。候选解决方案基于基于知识的势能和空间位阻标准进行评分。势能包括静电互补性、去溶剂化能、氨基酸接触偏好和范德华势能。我们将 ProBinder 应用于从 Dockground 数据库中编译的 68 个结合和 30 个未结合的测试案例的多样化测试集。对于结合数据集，在排名前 10 的预测中，有 64%的蛋白质-蛋白质测试复合物的根均方偏差（RMSD）<5 A 与目标解决方案匹配。在 82%的未结合样本中，对接构象在与目标解决方案的 RMSD<10 A 范围内排名前 10 位。
