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用于稳健且准确的计算机辅助配体筛选的多靶点筛选方法

Multiple target screening method for robust and accurate in silico ligand screening.

作者信息

Fukunishi Yoshifumi, Mikami Yoshiaki, Kubota Satoru, Nakamura Haruki

机构信息

Biological Information Research Center (BIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6, Aomi, Tokyo 135-0064, Japan.

出版信息

J Mol Graph Model. 2006 Sep;25(1):61-70. doi: 10.1016/j.jmgm.2005.11.006. Epub 2005 Dec 22.

Abstract

We developed a new in silico multiple target screening (MTS) method, based on a multi-receptor versus multi-ligand docking affinity matrixes, and examined its robustness against changes in the scoring system. According to this method, compounds in a database are docked to multiple proteins. The compounds among these proteins that are likely bind to the target protein are selected as the members of the candidate-hit compound group. Then, the compounds in the group are sorted into descending order using the docking score: the first (n-th) compound is expected to be the most (n-th) probable hit compound. This method was applied to the analysis of a set of 142 receptors and 142 compounds using a receptor-ligand docking program, Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura, Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening, J. Mol. Graphics Modelling, 24 (2005) 34-45], and the results demonstrated that this method achieves a high hit ratio compared to uniform sampling. We prepared two new scores: the DeltaG score, designed to reproduce the protein-ligand binding free energy, and the hit-optimized score, designed to maximize the hit ratio of in silico screening. Using the Sievgene docking score, DeltaG score and hit-optimized score, the MTS method is more robust than the multiple active-site correction scoring method [G.P.A. Vigers, J.P. Rizzi, Multiple active site corrections for docking and virtual screening, J. Med. Chem., 47 (2004) 80-89].

摘要

我们基于多受体与多配体对接亲和力矩阵开发了一种新的计算机辅助多靶点筛选(MTS)方法,并研究了其对评分系统变化的稳健性。根据该方法,数据库中的化合物与多种蛋白质进行对接。这些蛋白质中可能与目标蛋白质结合的化合物被选为候选命中化合物组的成员。然后,使用对接分数将该组中的化合物按降序排列:第一个(第n个)化合物预计是最有可能(第n个)命中的化合物。使用受体-配体对接程序Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura,受体口袋之间和化合物之间的相似性:计算机辅助配体筛选的分析与应用,《分子图形与建模杂志》,24 (2005) 34 - 45] 将该方法应用于一组142种受体和142种化合物的分析,结果表明与均匀采样相比,该方法具有较高的命中率。我们准备了两个新的分数:旨在重现蛋白质-配体结合自由能的DeltaG分数,以及旨在最大化计算机辅助筛选命中率的命中优化分数。使用Sievgene对接分数、DeltaG分数和命中优化分数,MTS方法比多活性位点校正评分方法 [G.P.A. Vigers, J.P. Rizzi,对接和虚拟筛选的多活性位点校正,《药物化学杂志》,47 (2004) 80 - 89] 更稳健。

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