Comparative evaluation of 11 scoring functions for molecular docking
Comparative evaluation of 11 scoring functions for molecular docking
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DOI：
10.1021/jm0203783
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发表时间：
2003-06-05
影响因子：
7.3
通讯作者：
Wang, SM
中科院分区：
文献类型：
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作者：
Wang, RX;Lu, YP;Wang, SM
在100个蛋白质-配体复合体上测试了11个流行的评分函数，以评估它们复制实验确定的结构和结合亲和力的能力。它们包括在Cerius2的LigFit模块中实现的四个评分函数(LigScore、PLP、PMF和Ludi)，在SYBYL的CScore模块中实现的四个评分函数(F-SCORE、G-SCORE、D-SCORE和ChemScore)，在AutoDock程序中实现的评分函数，以及两个独立的评分函数(DrugScore和X-Score)。这些计分功能没有在特定对接计划的环境中进行测试。相反，构象采样和评分被分成两个连续的步骤。首先，使用AutoDock程序进行详尽的构象采样，以生成每个配体分子的对接构象系综。这种构象系综需要尽可能地覆盖整个构象空间，而不是集中在几个能量最小值上。然后，每个评分函数被应用于对这个构象集合进行评分，以查看它是否能够从所有其他诱饵中识别出实验观察到的构象。在所有测试的评分函数中，PLP、F-SCORE、LigScore、DrugScore、Ludi和X-Score这六个评分函数的成功率高于AutoDock评分函数。如果以均方根偏差小于或等于2.0埃为标准，这六种评分函数的成功率从66%到76%不等。将这六个评分函数中的任意两个或三个组合成一个共识评分方案，进一步将成功率提高到近80%甚至更高。然而，当应用于再现实验确定的100个蛋白质-配体复合体的结合亲和力时，只有X-Score、PLP、DrugScore和G-Score能够给出0.50以上的相关系数。所有11个计分函数都进一步通过它们构建蛋白质-配体络合的描述性漏斗状能量表面的能力进行了检验。结果表明，X-Score和DrugScore在这方面的表现优于其他两种。
Eleven popular scoring functions have been tested on 100 protein-ligand complexes to evaluate their abilities to reproduce experimentally determined structures and binding affinities. They include four scoring functions implemented in the LigFit module in Cerius2 (LigScore, PLP, PMF, and LUDI), four scoring functions implemented in the CScore module in SYBYL (F-Score, G-Score, D-Score, and ChemScore), the scoring function implemented in the AutoDock program, and two stand-alone scoring functions (DrugScore and X-Score). These scoring functions are not tested in the context of a particular docking program. Instead, conformational sampling and scoring are separated into two consecutive steps. First, an exhaustive conformational sampling is performed by using the AutoDock program to generate an ensemble of docked conformations for each ligand molecule. This conformational ensemble is required to cover the entire conformational space as much as possible rather than to focus on a few energy minima. Then, each scoring function is applied to score this conformational ensemble to see if it can identify the experimentally observed conformation from all of the other decoys. Among all of the scoring functions under test, six of them, i.e., PLP, F-Score, LigScore, DrugScore, LUDI, and X-Score, yield success rates higher than the AutoDock scoring function. The success rates of these six scoring functions range from 66% to 76% if using root-mean-square deviation less than or equal to2.0 Angstrom as the criterion. Combining any two or three of these six scoring functions into a consensus scoring scheme further improves the success rate to nearly 80% or even higher. However, when applied to reproduce the experimentally determined binding affinities of the 100 protein-ligand complexes, only X-Score, PLP, DrugScore, and G-Score are able to give correlation coefficients over 0.50. All of the 11 scoring functions are further inspected by their abilities to construct a descriptive, funnel-shaped energy surface for protein-ligand complexation. The results indicate that X-Score and DrugScore perform better than the other ones at this aspect.