Kowalsman Noga, Eisenstein Miriam
Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.
Bioinformatics. 2007 Feb 15;23(4):421-6. doi: 10.1093/bioinformatics/btl524. Epub 2006 Oct 12.
The limited success rate of protein-protein docking procedures is generally attributed to structure differences between the bound and unbound states of the molecules. Herein we analyze a large dataset of protein-protein docking results and identify additional parameters that affect the performance of docking procedures.
We find that the distinction between nearly correct models (NCMs) and decoys depends on the size of the interface to be predicted thus setting a limit to the prediction ability of docking procedures, particularly those in which the geometric complementarity descriptor is dominant. The geometric complementarity score in grid-based docking carries a large statistical error which further reduces the distinction between NCMs and decoys. We propose a method for correcting the statistical error and show that the distinction is improved when the docking models are ranked by statistically equivalent scores.
MolFit can be downloaded from our website http://www.weizmann.ac.il/Chemical_Research_Support/molfit.
Supplementary data are available at Bioinformatics online.
蛋白质-蛋白质对接程序的成功率有限，通常归因于分子结合态与非结合态之间的结构差异。在此，我们分析了一个大型蛋白质-蛋白质对接结果数据集，并确定了影响对接程序性能的其他参数。
我们发现，近乎正确模型（NCM）与诱饵之间的区分取决于待预测界面的大小，从而为对接程序的预测能力设定了限制，尤其是那些几何互补性描述符占主导的对接程序。基于网格对接中的几何互补性得分存在较大统计误差，这进一步缩小了NCM与诱饵之间的区分。我们提出了一种校正统计误差的方法，并表明当对接模型按统计等效得分排序时，区分得到了改善。
MolFit可从我们的网站http://www.weizmann.ac.il/Chemical_Research_Support/molfit下载。
补充数据可在《生物信息学》在线获取。
