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11种分子对接评分函数的比较评估

Comparative evaluation of 11 scoring functions for molecular docking.

作者信息

Wang Renxiao, Lu Yipin, Wang Shaomeng

机构信息

Department of Internal Medicine and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor 48109-0934, USA.

出版信息

J Med Chem. 2003 Jun 5;46(12):2287-303. doi: 10.1021/jm0203783.

Abstract

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 < or =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.

摘要

在100个蛋白质 - 配体复合物上测试了11种常用的评分函数,以评估它们重现实验测定结构和结合亲和力的能力。它们包括Cerius2中LigFit模块实现的4种评分函数(LigScore、PLP、PMF和LUDI),SYBYL中CScore模块实现的4种评分函数(F - Score、G - Score、D - Score和ChemScore),AutoDock程序中实现的评分函数,以及两种独立的评分函数(DrugScore和X - Score)。这些评分函数并非在特定对接程序的背景下进行测试。相反,构象采样和评分被分为两个连续的步骤。首先,使用AutoDock程序进行详尽的构象采样,为每个配体分子生成一组对接构象。这个构象集需要尽可能覆盖整个构象空间,而不是专注于少数能量极小值。然后,应用每个评分函数对这个构象集进行评分,以查看它是否能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,其中6个,即PLP、F - Score、LigScore、DrugScore、LUDI和X - Score,产生的成功率高于AutoDock评分函数。如果使用均方根偏差≤2.0 Å作为标准,这6种评分函数的成功率在66%至76%之间。将这6种评分函数中的任意两种或三种组合成一种共识评分方案,可进一步将成功率提高到近80%甚至更高。然而,当应用于重现100个蛋白质 - 配体复合物的实验测定结合亲和力时,只有X - Score、PLP、DrugScore和G - Score能够给出超过0.50的相关系数。通过它们构建蛋白质 - 配体络合的描述性、漏斗形能量表面的能力,对所有11种评分函数进行了进一步检查。结果表明,在这方面X - Score和DrugScore比其他函数表现更好。

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