Bai Fang, Liao Sha, Gu Junfeng, Jiang Hualiang, Wang Xicheng, Li Honglin
†Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, Liaoning 116023, China.
‡Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.
J Chem Inf Model. 2015 Apr 27;55(4):833-47. doi: 10.1021/ci500647f. Epub 2015 Mar 17.
Metalloproteins, particularly zinc metalloproteins, are promising therapeutic targets, and recent efforts have focused on the identification of potent and selective inhibitors of these proteins. However, the ability of current drug discovery and design technologies, such as molecular docking and molecular dynamics simulations, to probe metal-ligand interactions remains limited because of their complicated coordination geometries and rough treatment in current force fields. Herein we introduce a robust, multiobjective optimization algorithm-driven metalloprotein-specific docking program named MpSDock, which runs on a scheme similar to consensus scoring consisting of a force-field-based scoring function and a knowledge-based scoring function. For this purpose, in this study, an effective knowledge-based zinc metalloprotein-specific scoring function based on the inverse Boltzmann law was designed and optimized using a dynamic sampling and iteration optimization strategy. This optimization strategy can dynamically sample and regenerate decoy poses used in each iteration step of refining the scoring function, thus dramatically improving both the effectiveness of the exploration of the binding conformational space and the sensitivity of the ranking of the native binding poses. To validate the zinc metalloprotein-specific scoring function and its special built-in docking program, denoted MpSDockZn, an extensive comparison was performed against six universal, popular docking programs: Glide XP mode, Glide SP mode, Gold, AutoDock, AutoDock4Zn, and EADock DSS. The zinc metalloprotein-specific knowledge-based scoring function exhibited prominent performance in accurately describing the geometries and interactions of the coordination bonds between the zinc ions and chelating agents of the ligands. In addition, MpSDockZn had a competitive ability to sample and identify native binding poses with a higher success rate than the other six docking programs.
金属蛋白，尤其是锌金属蛋白，是很有前景的治疗靶点，近期的研究工作主要集中在寻找这些蛋白的强效和选择性抑制剂。然而，当前的药物发现和设计技术，如分子对接和分子动力学模拟，在探测金属-配体相互作用方面的能力仍然有限，因为它们的配位几何结构复杂，且在当前力场中的处理较为粗糙。在此，我们介绍了一种强大的、由多目标优化算法驱动的金属蛋白特异性对接程序，名为MpSDock，它基于一种类似于共识评分的方案运行，该方案由基于力场的评分函数和基于知识的评分函数组成。为此，在本研究中，我们设计并优化了一种基于逆玻尔兹曼定律的有效的基于知识的锌金属蛋白特异性评分函数，并采用动态采样和迭代优化策略。这种优化策略可以在优化评分函数的每个迭代步骤中动态采样并重新生成诱饵构象，从而显著提高结合构象空间探索的有效性和天然结合构象排名的敏感性。为了验证锌金属蛋白特异性评分函数及其特殊的内置对接程序MpSDockZn，我们与六个通用的、流行的对接程序进行了广泛比较：Glide XP模式、Glide SP模式、Gold、AutoDock、AutoDock4Zn和EADock DSS。基于知识的锌金属蛋白特异性评分函数在准确描述锌离子与配体螯合剂之间配位键的几何结构和相互作用方面表现出突出性能。此外，MpSDockZn在采样和识别天然结合构象方面具有竞争能力，成功率高于其他六个对接程序。
