Molecular dynamics and quantum mechanics of RNA: conformational and chemical change we can believe in.
Molecular dynamics and quantum mechanics of RNA: conformational and chemical change we can believe in.
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DOI：
10.1021/ar900093g
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发表时间：
2010-01-19
影响因子：
18.3
通讯作者：
Walter, Nils G.
中科院分区：
文献类型：
--
作者：
Ditzler, Mark A.;Otyepka, Michal;Sponer, Jiri;Walter, Nils G.
结构和动力学对RNA在生物学中的重要功能都至关重要。许多技术可以阐明RNA的结构动力学，但基于实验数据的计算方法可以说是提供最详细的承诺。在这个帐户中，我们强调的领域，其中分子动力学（MD）和量子力学（QM）技术应用于RNA，特别是在互补的实验研究。我们已经扩大了原子分辨率的晶体结构的RNA在功能相关的状态，通过应用显式溶剂MD模拟探索其动力学和构象变化的亚微秒时间尺度上。MD依赖于简化的原子，成对加性相互作用势（力场）。由于有限的采样，由于有限的可访问的仿真时间尺度和近似的力场，高质量的启动结构是必需的。尽管他们的不完美，我们发现，目前可用的力场使MD提供有意义的和预测的信息，RNA动力学在晶体学定义的能量最小值。力场的性能可以通过对小模型系统的精确QM计算来估计。这种计算与康奈尔等人的琥珀力场相当吻合，特别是对于堆积和氢键相互作用。任何力场的最终验证都是通过模拟复杂的核酸结构来完成的。Cornell等人的AMBER力场的性能通常与实验数据很好地对应并增加了实验数据，但一个值得注意的例外可能是双螺旋柄的帽环。此外，对于包含二价阳离子来说，成对加性力场的性能显然不令人满意，因为它们的相互作用导致了被力场忽视的主要极化和电荷转移效应。极化的不确定性也限制了其他贡献的描述准确性，尽管程度较轻，例如与单价离子的相互作用，阴离子糖-磷酸骨架的构象灵活性，氢键和溶剂的溶质极化。尽管存在局限性，MD模拟仍然是分析现有实验结构的结构动力学的有效工具。仔细分析MD模拟可以识别实验RNA结构的问题方面，揭示被实验约束掩盖的结构特征，揭示功能上显著的随机波动，评估碱基电离的结构作用，并预测溶剂行为的结构和潜在功能上重要的细节，包括紧密结合的水分子的存在。此外，结合经典的MD模拟与QM计算的混合QM/MM方法有助于在评估的催化RNA（核酶）的化学机制的可扩展性。相比之下，从序列信息可靠地预测结构超出了MD工具的适用性。因此，计算研究在理解RNA功能方面的最终效用要求其结果既不盲目接受也不断然拒绝，而是在所有可用实验数据的背景下进行考虑，并非常小心地通过可用的起始结构、力场近似和采样限制来评估限制。本帐户中给出的例子展示了如何明智地使用基本MD模拟已经作为一个强大的工具，以帮助评估结构动力学在RNA生物功能中的作用。
Structure and dynamics are both critical to RNA’s vital functions in biology. Numerous techniques can elucidate the structural dynamics of RNA, but computational approaches based on experimental data arguably hold the promise of providing the most detail. In this Account, we highlight areas wherein molecular dynamics (MD) and quantum mechanical (QM) techniques are applied to RNA, particularly in relation to complementary experimental studies. We have expanded on atomic-resolution crystal structures of RNAs in functionally relevant states by applying explicit solvent MD simulations to explore their dynamics and conformational changes on the submicrosecond time scale. MD relies on simplified atomistic, pairwise additive interaction potentials (force fields). Because of limited sampling, due to the finite accessible simulation time scale and the approximated force field, high-quality starting structures are required. Despite their imperfection, we find that currently available force fields empower MD to provide meaningful and predictive information on RNA dynamics around a crystallographically defined energy minimum. The performance of force fields can be estimated by precise QM calculations on small model systems. Such calculations agree reasonably well with the Cornell et al. AMBER force field, particularly for stacking and hydrogen-bonding interactions. A final verification of any force field is accomplished by simulations of complex nucleic acid structures. The performance of the Cornell et al. AMBER force field generally corresponds well with and augments experimental data, but one notable exception could be the capping loops of double-helical stems. In addition, the performance of pairwise additive force fields is obviously unsatisfactory for inclusion of divalent cations, because their interactions lead to major polarization and charge-transfer effects neglected by the force field. Neglect of polarization also limits, albeit to a lesser extent, the description accuracy of other contributions, such as interactions with monovalent ions, conformational flexibility of the anionic sugar−phosphate backbone, hydrogen bonding, and solute polarization by solvent. Still, despite limitations, MD simulations are a valid tool for analyzing the structural dynamics of existing experimental structures. Careful analysis of MD simulations can identify problematic aspects of an experimental RNA structure, unveil structural characteristics masked by experimental constraints, reveal functionally significant stochastic fluctuations, evaluate the structural role of base ionization, and predict structurally and potentially functionally important details of the solvent behavior, including the presence of tightly bound water molecules. Moreover, combining classical MD simulations with QM calculations in hybrid QM/MM approaches helps in the assessment of the plausibility of chemical mechanisms of catalytic RNAs (ribozymes). In contrast, the reliable prediction of structure from sequence information is beyond the applicability of MD tools. The ultimate utility of computational studies in understanding RNA function thus requires that the results are neither blindly accepted nor flatly rejected, but rather considered in the context of all available experimental data, with great care given to assessing limitations through the available starting structures, force field approximations, and sampling limitations. The examples given in this Account showcase how the judicious use of basic MD simulations has already served as a powerful tool to help evaluate the role of structural dynamics in biological function of RNA.
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