Shen Yang, Brenke Ryan, Kozakov Dima, Comeau Stephen R, Beglov Dmitri, Vajda Sandor
BioMolecular Engineering Research Center, Boston University, Boston, Massachusetts 02215, USA.
Proteins. 2007 Dec 1;69(4):734-42. doi: 10.1002/prot.21754.
Our approach to protein-protein docking includes three main steps. First we run PIPER, a new rigid body docking program. PIPER is based on the Fast Fourier Transform (FFT) correlation approach that has been extended to use pairwise interactions potentials, thereby substantially increasing the number of near-native structures generated. The interaction potential is also new, based on the DARS (Decoys As the Reference State) principle. In the second step, the 1000 best energy conformations are clustered, and the 30 largest clusters are retained for refinement. Third, the conformations are refined by a new medium-range optimization method SDU (Semi-Definite programming based Underestimation). SDU has been developed to locate global minima within regions of the conformational space in which the energy function is funnel-like. The method constructs a convex quadratic underestimator function based on a set of local energy minima, and uses this function to guide future sampling. The combined method performed reliably without the direct use of biological information in most CAPRI problems that did not require homology modeling, providing acceptable predictions for targets 21, and medium quality predictions for targets 25 and 26.
我们的蛋白质-蛋白质对接方法包括三个主要步骤。首先,我们运行PIPER,一个新的刚体对接程序。PIPER基于快速傅里叶变换(FFT)相关方法,该方法已扩展为使用成对相互作用势,从而大幅增加了生成的近天然结构的数量。相互作用势也是新的,基于DARS(以诱饵作为参考状态)原理。在第二步中,对1000个最佳能量构象进行聚类,并保留30个最大的聚类用于优化。第三步,通过一种新的中程优化方法SDU(基于半定规划的低估法)对构象进行优化。SDU的开发目的是在能量函数呈漏斗状的构象空间区域内找到全局最小值。该方法基于一组局部能量最小值构建一个凸二次低估函数,并使用此函数指导未来的采样。在大多数不需要同源建模的CAPRI问题中,该组合方法在不直接使用生物学信息的情况下可靠运行,为目标21提供了可接受的预测,为目标25和26提供了中等质量的预测。