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配体结合型谷氨酰胺结合蛋白具有多个具有不同结合亲和力的亚稳态结合位点。

Ligand-bound glutamine binding protein assumes multiple metastable binding sites with different binding affinities.

作者信息

Zhang Lu, Wu Shaowen, Feng Yitao, Wang Dan, Jia Xilin, Liu Zhijun, Liu Jianwei, Wang Wenning

机构信息

State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, China.

Department of Chemistry, Institutes of Biomedical Sciences, Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, China.

出版信息

Commun Biol. 2020 Aug 3;3(1):419. doi: 10.1038/s42003-020-01149-z.

Abstract

Protein dynamics plays key roles in ligand binding. However, the microscopic description of conformational dynamics-coupled ligand binding remains a challenge. In this study, we integrate molecular dynamics simulations, Markov state model (MSM) analysis and experimental methods to characterize the conformational dynamics of ligand-bound glutamine binding protein (GlnBP). We show that ligand-bound GlnBP has high conformational flexibility and additional metastable binding sites, presenting a more complex energy landscape than the scenario in the absence of ligand. The diverse conformations of GlnBP demonstrate different binding affinities and entail complex transition kinetics, implicating a concerted ligand binding mechanism. Single molecule fluorescence resonance energy transfer measurements and mutagenesis experiments are performed to validate our MSM-derived structure ensemble as well as the binding mechanism. Collectively, our study provides deeper insights into the protein dynamics-coupled ligand binding, revealing an intricate regulatory network underlying the apparent binding affinity.

摘要

蛋白质动力学在配体结合中起着关键作用。然而,对构象动力学耦合配体结合的微观描述仍然是一个挑战。在本研究中,我们整合了分子动力学模拟、马尔可夫状态模型(MSM)分析和实验方法,以表征配体结合型谷氨酰胺结合蛋白(GlnBP)的构象动力学。我们表明,配体结合型GlnBP具有高构象灵活性和额外的亚稳结合位点,呈现出比无配体情况下更为复杂的能量景观。GlnBP的多种构象表现出不同的结合亲和力,并具有复杂的转变动力学,这暗示了一种协同配体结合机制。进行了单分子荧光共振能量转移测量和诱变实验,以验证我们从MSM获得的结构集合以及结合机制。总的来说,我们的研究为蛋白质动力学耦合配体结合提供了更深入的见解,揭示了表观结合亲和力背后复杂的调控网络。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56cc/7400645/8da3f969ecff/42003_2020_1149_Fig1_HTML.jpg

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