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一种多起点蒙特卡洛对接方法。

A multiple-start Monte Carlo docking method.

作者信息

Hart T N, Read R J

机构信息

Department of Medical Microbiology and Infectious Diseases, University of Alberta, Edmonton, Canada.

出版信息

Proteins. 1992 Jul;13(3):206-22. doi: 10.1002/prot.340130304.

DOI:10.1002/prot.340130304
PMID:1603810
Abstract

We present a method to search for possible binding modes of molecular fragments at a specific site of a potential drug target of known structure. Our method is based on a Monte Carlo (MC) algorithm applied to the translational and rotational degrees of freedom of the probe fragment. Starting from a randomly generated initial configuration, favorable binding modes are generated using a two-step process. An MC run is first performed in which the energy in the Metropolis algorithm is substituted by a score function that measures the average distance of the probe to the target surface. This has the effect of making buried probes move toward the target surface and also allows enhanced sampling of deep pockets. In a second MC run, a pairwise atom potential function is used, and the temperature parameter is slowly lowered during the run (Simulated Annealing). We repeat this procedure starting from a large number of different randomly generated initial configurations in order to find all energetically favorable docking modes in a specified region around the target. We test this method using two inhibitor-receptor systems: Streptomyces griseus proteinase B in complex with the third domain of the ovomucoid inhibitor from turkey, and dihydrofolate reductase from E. coli in complex with methotrexate. The method could consistently reproduce the complex found in the crystal structure searching from random initial positions in cubes ranging from 25 A to 50 A about the binding site. In the case of SGPB, we were also successful in docking to the native structure. In addition, we were successful in docking small probes in a search that included the entire protein surface.

摘要

我们提出了一种方法,用于在已知结构的潜在药物靶点的特定位点搜索分子片段可能的结合模式。我们的方法基于一种蒙特卡罗(MC)算法,该算法应用于探针片段的平移和旋转自由度。从随机生成的初始构型开始,通过两步过程生成有利的结合模式。首先进行一次MC运行,其中在 metropolis 算法中的能量被一个评分函数所取代,该评分函数测量探针到靶点表面的平均距离。这使得埋藏的探针向靶点表面移动,并且还允许对深口袋进行增强采样。在第二次MC运行中,使用成对原子势函数,并且在运行过程中缓慢降低温度参数(模拟退火)。我们从大量不同的随机生成的初始构型开始重复这个过程,以便在靶点周围的指定区域找到所有能量上有利的对接模式。我们使用两个抑制剂-受体系统测试了这种方法:灰色链霉菌蛋白酶B与来自火鸡的卵类粘蛋白抑制剂的第三个结构域形成的复合物,以及大肠杆菌二氢叶酸还原酶与甲氨蝶呤形成的复合物。该方法能够从围绕结合位点的25 Å至50 Å的立方体中的随机初始位置一致地重现晶体结构中发现的复合物。在SGPB的情况下,我们还成功地对接至天然结构。此外,我们在包括整个蛋白质表面的搜索中成功对接了小探针。

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