School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, People's Republic of China.
J Mol Model. 2012 May;18(5):2199-208. doi: 10.1007/s00894-011-1231-0. Epub 2011 Sep 27.
Computational models of protein-protein docking that incorporate backbone flexibility can predict perturbations of the backbone and side chains during docking and produce protein interaction models with atomic accuracy. Most previous models usually predefine flexible regions by visually comparing the bound and unbound structures. In this paper, we propose a general method to automatically identify the flexible hinges for domain assembly and the flexible loops for loop refinement, in addition to predicting the corresponding movements of the identified active residues. We conduct experiments to evaluate performance of our approach on two test sets. Comparison of results on test set I between algorithms with and without prediction of flexible regions demonstrate the superior recovery of energy funnels in many target interactions using the new loop refinement model. In addition, our decoys are superior for each target. Indeed, the total number of satisfactory models is almost double that of other programs. The results on test set II docking tests produced by our domain assembly method also show encouraging results. Of the three targets examined, one exhibits energy funnel and the best models of the other two targets all meet the conditions of acceptable accuracy. Results demonstrate that the automatic prediction of flexible backbone regions can greatly improve the performance of protein-protein docking models.
将蛋白质-蛋白质对接的计算模型与骨架柔性结合起来，可以预测对接过程中骨架和侧链的变化，并生成具有原子精度的蛋白质相互作用模型。以前的大多数模型通常通过视觉比较结合态和非结合态结构来预先定义柔性区域。在本文中，我们提出了一种通用方法，用于自动识别结构域组装的柔性铰链和环区细化的柔性环，以及预测已识别的活性残基的相应运动。我们在两个测试集上进行了实验，以评估我们的方法的性能。在算法中加入和不加入柔性区域预测的测试集 I 上的结果比较表明，新的环细化模型在许多目标相互作用中恢复了更多的能量漏斗。此外，我们的假受体在每个靶标上都表现出色。实际上，满意模型的总数几乎是其他程序的两倍。我们的结构域组装方法在测试集 II 对接测试中的结果也显示出了令人鼓舞的结果。在所研究的三个靶标中，一个靶标表现出能量漏斗，另外两个靶标的最佳模型都满足可接受精度的条件。结果表明，自动预测柔性骨架区域可以大大提高蛋白质-蛋白质对接模型的性能。
