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基于洛伦兹结构势研究腺苷酸激酶构象转变的原子机制。

The Atomistic Mechanism of Conformational Transition of Adenylate Kinase Investigated by Lorentzian Structure-Based Potential.

作者信息

Lee Juyong, Joo Keehyoung, Brooks Bernard R, Lee Jooyoung

机构信息

School of Computational Sciences, Korea Institute for Advanced Study , Dongdaemun-gu, Seoul 130-722, Korea.

Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health , Bethesda, Maryland 20852, United States.

出版信息

J Chem Theory Comput. 2015 Jul 14;11(7):3211-24. doi: 10.1021/acs.jctc.5b00268. Epub 2015 Jun 24.

Abstract

We present a new all-atom structure-based method to study protein conformational transitions using Lorentzian attractive interactions based on native structures. The variability of each native contact is estimated based on evolutionary information using a machine learning method. To test the validity of this approach, we have investigated the conformational transition of adenylate kinase (ADK). The intrinsic boundedness of the Lorentzian attractive interactions facilitated frequent conformational transitions, and consequently we were able to observe more than 1000 structural interconversions between the open and closed states of ADK out of a total of 6 μs MD simulations. ADK has three domains: the nucleoside monophosphate (NMP) binding domain, the LID-domain, and the CORE domain, which catalyze the interconversion between ATP and ADP. We identified two transition states: a more frequent LID-closed-NMP-open (TS1) state and a less frequent LID-open-NMP-closed (TS2) state. The transition was found to be symmetric in both directions via TS1. We also obtained an off-pathway metastable state that was previously observed with physics-based all-atom simulations but not with coarse-grained models. In the metastable state, the LID domain was slightly twisted and formed contacts with the NMP domain. Our model correctly identified a total of 14 out of the top 16 residues with highest fluctuation by NMR experiment, thus showing excellent agreement with experimental NMR relaxation data and overwhelmingly better results than existing models.

摘要

我们提出了一种基于全原子结构的新方法,用于利用基于天然结构的洛伦兹吸引相互作用来研究蛋白质构象转变。使用机器学习方法根据进化信息估计每个天然接触的变异性。为了测试该方法的有效性,我们研究了腺苷酸激酶(ADK)的构象转变。洛伦兹吸引相互作用的内在局限性促进了频繁的构象转变,因此在总共6微秒的分子动力学模拟中,我们能够观察到ADK的开放态和封闭态之间超过1000次的结构相互转换。ADK有三个结构域:核苷单磷酸(NMP)结合结构域、LID结构域和CORE结构域,它们催化ATP和ADP之间的相互转换。我们确定了两个过渡态:一个更频繁出现的LID-封闭-NMP-开放(TS1)态和一个较不频繁出现的LID-开放-NMP-封闭(TS2)态。发现通过TS1,转变在两个方向上都是对称的。我们还获得了一个非途径亚稳态,该亚稳态以前在基于物理的全原子模拟中观察到,但在粗粒度模型中未观察到。在亚稳态中,LID结构域略有扭曲并与NMP结构域形成接触。我们的模型通过核磁共振实验正确地识别出了波动最大的前16个残基中的总共14个,从而与实验核磁共振弛豫数据显示出极好的一致性,并且比现有模型的结果要好得多。

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