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如何提高 AutoDock4.2 的对接精度:使用不同静电势的案例研究。

How to improve docking accuracy of AutoDock4.2: a case study using different electrostatic potentials.

机构信息

Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (MOE), School of Pharmacy, Shandong University, Jinan, Shandong 250012, China.

出版信息

J Chem Inf Model. 2013 Jan 28;53(1):188-200. doi: 10.1021/ci300417y. Epub 2013 Jan 2.

Abstract

Molecular docking, which is the indispensable emphasis in predicting binding conformations and energies of ligands to receptors, constructs the high-throughput virtual screening available. So far, increasingly numerous molecular docking programs have been released, and among them, AutoDock 4.2 is a widely used docking program with exceptional accuracy. It has heretofore been substantiated that the calculation of partial charge is very fundamental for the accurate conformation search and binding energy estimation. However, no systematic comparison of the significances of electrostatic potentials on docking accuracy of AutoDock 4.2 has been determined. In this paper, nine different charge-assigning methods, including AM1-BCC, Del-Re, formal, Gasteiger-Hückel, Gasteiger-Marsili, Hückel, Merck molecular force field (MMFF), and Pullman, as well as the ab initio Hartree-Fock charge, were sufficiently explored for their molecular docking performance by using AutoDock4.2. The results clearly demonstrated that the empirical Gasteiger-Hückel charge is the most applicable in virtual screening for large database; meanwhile, the semiempirical AM1-BCC charge is practicable in lead compound optimization as well as accurate virtual screening for small databases.

摘要

分子对接是预测配体与受体结合构象和能量的不可或缺的重点,构建了高通量虚拟筛选。到目前为止,已经发布了越来越多的分子对接程序,其中 AutoDock 4.2 是一个广泛使用的对接程序,具有很高的准确性。到目前为止,已经证明计算部分电荷对于准确的构象搜索和结合能估计非常重要。然而,尚未确定 AutoDock 4.2 对接准确性中静电势的重要性的系统比较。在本文中,充分探讨了包括 AM1-BCC、Del-Re、formal、Gasteiger-Hückel、Gasteiger-Marsili、Hückel、Merck 分子力场(MMFF)和 Pullman 在内的 9 种不同的电荷分配方法,以及从头算 Hartree-Fock 电荷,使用 AutoDock4.2 对其分子对接性能进行了充分的研究。结果清楚地表明,经验的 Gasteiger-Hückel 电荷在大型数据库的虚拟筛选中最适用;同时,半经验的 AM1-BCC 电荷在先导化合物优化以及小型数据库的准确虚拟筛选中也是可行的。

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