Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, USA.
BMC Bioinformatics. 2009 Dec 9;10:407. doi: 10.1186/1471-2105-10-407.
Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur.
We present a novel protein docking algorithm based on the use of 3D Zernike descriptors as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. Docking decoys are generated using geometric hashing, which are then ranked by a scoring function that incorporates a buried surface area and a novel geometric complementarity term based on normals associated with the 3D Zernike shape description. Our docking algorithm was tested on both bound and unbound cases in the ZDOCK benchmark 2.0 dataset. In 74% of the bound docking predictions, our method was able to find a near-native solution (interface C-alphaRMSD < or = 2.5 A) within the top 1000 ranks. For unbound docking, among the 60 complexes for which our algorithm returned at least one hit, 60% of the cases were ranked within the top 2000. Comparison with existing shape-based docking algorithms shows that our method has a better performance than the others in unbound docking while remaining competitive for bound docking cases.
We show for the first time that the 3D Zernike descriptors are adept in capturing shape complementarity at the protein-protein interface and useful for protein docking prediction. Rigorous benchmark studies show that our docking approach has a superior performance compared to existing methods.
蛋白质-蛋白质相互作用是许多生物过程的关键组成部分，介导多种功能。因此，了解蛋白质复合物的三级结构对于理解相互作用机制至关重要。然而，解决复合物结构的实验技术通常被发现很困难。为此，计算蛋白质-蛋白质对接方法可以提供一种有用的替代方法来解决这个问题。对接构象的预测依赖于能够有效捕获参与蛋白质形状特征的方法，同时考虑可能发生的构象变化。
我们提出了一种基于使用 3D Zernike 描述符作为分子形状的区域特征的新型蛋白质对接算法。使用这些描述符的主要动机除了紧凑表示局部表面形状特征外，还在于它们对变换的不变性。使用几何哈希生成对接诱饵，然后根据包含埋藏表面积和基于与 3D Zernike 形状描述相关联的法向量的新几何互补项的评分函数对其进行排名。我们的对接算法在 ZDOCK 基准 2.0 数据集的绑定和未绑定情况下进行了测试。在 74%的绑定对接预测中，我们的方法能够在前 1000 个排名中找到接近天然的解决方案（接口 C-alphaRMSD<或=2.5 A）。对于未绑定对接，在我们的算法返回至少一个命中的 60 个复合物中，有 60%的情况排在前 2000 名。与现有的基于形状的对接算法相比，我们的方法在未绑定对接方面的性能优于其他方法，而在绑定对接方面仍然具有竞争力。
我们首次表明，3D Zernike 描述符擅长捕获蛋白质-蛋白质界面的形状互补性，并且可用于蛋白质对接预测。严格的基准研究表明，与现有方法相比，我们的对接方法具有更好的性能。
