RNA Modeling with the Computational Energy Landscape Framework
RNA Modeling with the Computational Energy Landscape Framework
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DOI：
10.1007/978-1-0716-1499-0_5
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发表时间：
2021-01-01
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影响因子：
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通讯作者：
Pasquali, Samuela
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文献类型：
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作者：
Roder, Konstantin;Pasquali, Samuela
计算能力的最新进展，例如GPU计算提供的巨大加速，允许在原子级细节上对RNA分子进行大规模计算研究。由于RNA分子具有能量相当的多种构象，但二维结构不同，因此需要全原子模型来更好地描述RNA分子的结构系综。这一点很重要，因为不同的构象可以表现出不同的功能，它们的调节或错误调节与许多疾病有关。问题是，不同构象系综之间的能垒很高，导致分子间跃迁的时间尺度很长。通过几何优化设计了计算势能景观框架来克服这种破坏遍历性的问题。在这里，我们描述的算法中使用的能源景观探索与OPTIM和PATHSAMPLE程序，以及它们是如何用于生物分子模拟。我们提出了一个最近的案例研究的RNA 7SK的5 '-发夹，以说明该方法如何可以应用于解释实验结果，并获得详细的描述的分子特性。
The recent advances in computational abilities, such as the enormous speed-ups provided by GPU computing, allow for large scale computational studies of RNA molecules at an atomic level of detail. As RNA molecules are known to adopt multiple conformations with comparable energies, but different two-dimensional structures, all-atom models are necessary to better describe the structural ensembles for RNA molecules. This point is important because different conformations can exhibit different functions, and their regulation or mis-regulation is linked to a number of diseases. Problematically, the energy barriers between different conformational ensembles are high, resulting in long time scales for interensemble transitions. The computational potential energy landscape framework was designed to overcome this problem of broken ergodicity by use of geometry optimization. Here, we describe the algorithms used in the energy landscape explorations with the OPTIM and PATHSAMPLE programs, and how they are used in biomolecular simulations. We present a recent case study of the 5'-hairpin of RNA 7SK to illustrate how the method can be applied to interpret experimental results, and to obtain a detailed description of molecular properties.