Predicting the accuracy of protein-ligand docking on homology models.
Predicting the accuracy of protein-ligand docking on homology models.
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DOI：
10.1002/jcc.21601
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发表时间：
2011-01-15
影响因子：
3
通讯作者：
Bonati, Laura
中科院分区：
文献类型：
--
作者：
Bordogna, Annalisa;Pandini, Alessandro;Bonati, Laura
配体-蛋白质对接越来越多地用于药物发现。通过使用理论模型，特别是通过同源性建模技术得到的模型，已经克服了由靶蛋白结构的可用性降低所施加的初始限制。虽然这极大地扩展了对接模拟的使用，但它也引入了对通用和鲁棒标准的需求，以估计给定模型质量的对接结果的可靠性。为此，一个大规模的实验进行了不同的设置，包括实验结构和同源性模型的一组代表性的配体-蛋白质复合物。使用不同进化距离的模板和不同的目标-模板比对和建模策略对广泛的模型质量进行采样。通过选择最常用的模型质量指标对所获得的模型进行评分。使用AutoDock（最常见的对接程序之一）生成结合几何形状。这项研究的一个重要结果是，确实定量和强大的相关性之间存在的对接结果的准确性和模型的质量，特别是在结合位点。此外，国家的最先进的指数模型质量评估已经是一个有效的工具，先验预测的准确性对接实验的背景下，具有保守的结构特征的蛋白质组。
Ligand–protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large-scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand–protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target–template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state-of-the-art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics.
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影响因子：
7.4
作者：
Hillisch A;Pineda LF;Hilgenfeld R
通讯作者：
Hilgenfeld R
影响因子：
7.3
作者：
Cozzini P;Kellogg GE;Spyrakis F;Abraham DJ;Costantino G;Emerson A;Fanelli F;Gohlke H;Kuhn LA;Morris GM;Orozco M;Pertinhez TA;Rizzi M;Sotriffer CA
通讯作者：
Sotriffer CA
影响因子：
--
作者：
Kairys, V.;Gilson, M. K.;Fernandes, M. X.
通讯作者：
Fernandes, M. X.
影响因子：
2.9
作者：
Battey, James N. D.;Kopp, Jurgen;Schwede, Torsten
通讯作者：
Schwede, Torsten
影响因子：
2.2
作者：
Ferrara, Philippe;Jacoby, Edgar
通讯作者：
Jacoby, Edgar