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首页> 外文期刊>Journal of Medicinal Chemistry >Comparative Evaluation of 11 Scoring Functions for Molecular Docking
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Comparative Evaluation of 11 Scoring Functions for Molecular Docking

机译：分子对接11种评分功能的比较评估

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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, 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 ≤2.0 A 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.
机译：已经对100种蛋白质-配体复合物测试了11种流行的评分功能，以评估其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功能（LigScore，PLP，PMF和LUDI），在SYBYL的CScore模块中实现的四个评分功能（F-Score，D-Score和ChemScore），实现的评分功能在AutoDock程序中，以及两个独立的评分功能（DrugScore和X-Score）。这些评分功能未在特定对接程序的上下文中进行测试。相反，构象采样和评分分为两个连续的步骤。首先，通过使用AutoDock程序进行详尽的构象采样，以生成每个配体分子对接构象的集合。需要这种构象集合以尽可能覆盖整个构象空间，而不是专注于最小的能量最小值。然后，应用每个评分函数对该构象集合进行评分，以查看它是否可以从所有其他诱饵中识别出实验观察到的构象。在所有被测试的评分功能中，其中六个，即PLP，F-Score，LigScore，DrugScore，LUDI和X-Score，其成功率高于AutoDock评分功能。如果使用均方根偏差≤2.0A作为标准，则这六个评分函数的成功率范围为66％至76％。将这六个评分功能中的任何两个或三个组合到一个共识评分方案中，可以进一步将成功率提高到近80％甚至更高。但是，当用于复制100种蛋白质-配体复合物的实验确定的结合亲和力时，只有X-Score，PLP，DrugScore和G-Score能够给出超过0.50的相关系数。通过11种评分功能的全部功能，可以构造出描述性的，漏斗形的能量表面来进行蛋白-配体复合，从而进一步检查了这些功能。结果表明，在此方面，X-Score和DrugScore的性能优于其他。

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