ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution.

ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution.
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DOI:
10.1021/acs.jctc.6b00319
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发表时间:
2016-09-13
影响因子:
5.5
通讯作者:
Doruker, Pemra
Doruker, Pemra
中科院分区:
化学1区
文献类型:
--
作者:
Kurkcuoglu, Zeynep;Bahar, Ivet;Doruker, Pemra

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构象空间的精确采样,特别是功能亚状态之间的转变一直是大型生物分子系统的分子动力学(MD)模拟中的一个挑战。我们开发了一种基于弹性网络模型 (ENM) 的计算方法 ClustENM,用于对各种尺寸和寡聚状态的生物分子的大构象变化进行采样。 ClustENM 是一种将 ENM 与能量最小化和聚类步骤相结合的迭代方法。这是一种无偏技术,仅需要初始结构作为输入,不需要有关目标构象的信息。为了测试 ClustENM 的性能,我们将其应用于六种生物分子系统:腺苷酸激酶 (AK)、钙调蛋白、p38 MAP 激酶、HIV-1 逆转录酶 (RT)、磷酸三糖异构酶 (TIM) 和 70S 核糖体复合物。在原子分辨率下确定的构象异构体的生成集合与实验数据(通过 X 射线和/或 NMR 解析的 979 个结构)显示出良好的一致性,并且包含 TIM、p38 和 RT 的独立 MD 模拟中涵盖的子空间。 ClustENM 是一种计算高效的工具,用于在原子细节上表征大型系统的构象空间,此外还生成可有利地用于模拟底物/配体结合事件的代表性构象异构体集合。
Accurate sampling of conformational space and, in particular, the transitions between functional substates has been a challenge in molecular dynamic (MD) simulations of large biomolecular systems. We developed an Elastic Network Model (ENM)-based computational method, ClustENM, for sampling large conformational changes of biomolecules with various sizes and oligomerization states. ClustENM is an iterative method that combines ENM with energy minimization and clustering steps. It is an unbiased technique, which requires only an initial structure as input, and no information about the target conformation. To test the performance of ClustENM, we applied it to six biomolecular systems: adenylate kinase (AK), calmodulin, p38 MAP kinase, HIV-1 reverse transcriptase (RT), triosephosphate isomerase (TIM), and the 70S ribosomal complex. The generated ensembles of conformers determined at atomic resolution show good agreement with experimental data (979 structures resolved by X-ray and/or NMR) and encompass the subspaces covered in independent MD simulations for TIM, p38, and RT. ClustENM emerges as a computationally efficient tool for characterizing the conformational space of large systems at atomic detail, in addition to generating a representative ensemble of conformers that can be advantageously used in simulating substrate/ligand-binding events.
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