Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
BMC Bioinformatics. 2012 Jan 10;13:7. doi: 10.1186/1471-2105-13-7.
Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.
We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.
We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.
许多重要的细胞过程都是由蛋白质复合物来执行的。为了提供相互作用蛋白质的物理图像，过去已经开发了许多计算蛋白质-蛋白质预测方法。然而，在替代构象中，仍然很难在排名靠前的结构中识别出正确的对接复合物结构。
我们提出了一种新的蛋白质对接算法，该算法利用不完美的蛋白质-蛋白质结合界面预测来指导蛋白质对接。由于蛋白质结合位点预测的准确性因情况而异，因此开发一种方法的挑战在于，即使使用可能不是 100%准确的结合位点预测，也不会降低而是提高对接结果。该算法名为 PI-LZerD（使用基于局部 3D Zernike 描述符的预测接口进行对接算法），它基于我们之前开发的一种成对蛋白质对接预测算法 LZerD。PI-LZerD 从使用提供的蛋白质-蛋白质结合界面预测作为约束进行对接预测开始，然后进行第二轮对接，使用更新后的对接接口信息进一步改善对接构象。在绑定和未绑定情况下的基准测试结果表明，与不使用结合位点预测或使用结合位点预测作为后处理相比，PI-LZerD 始终能提高对接预测的准确性。
我们开发了 PI-LZerD，这是一种基于成对的对接算法，它使用不完美的蛋白质-蛋白质结合界面预测来提高对接的准确性。PI-LZerD 在一系列基准实验中，包括使用实际对接接口预测和未绑定对接案例，始终表现出比其他方法更好的预测准确性。
