Yusuf Hamied Department of Chemistry, University of Chemistry, Cambridge, UK.
Laboratoire CiTCoM, CNRS UMR 8038, Université de Paris, Paris, France.
Methods Mol Biol. 2021;2323:49-66. doi: 10.1007/978-1-0716-1499-0_5.
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.
计算能力的最新进展，例如 GPU 计算提供的巨大速度提升，使得在原子级细节上对 RNA 分子进行大规模计算研究成为可能。由于已知 RNA 分子可以采用具有可比能量但不同二维结构的多种构象，因此需要全原子模型来更好地描述 RNA 分子的结构集合。这一点很重要，因为不同的构象可以表现出不同的功能，它们的调节或失调与许多疾病有关。有问题的是，不同构象集合之间的能量势垒很高，导致集合间跃迁的时间尺度很长。计算势能景观框架旨在通过几何优化来克服这种遍历性破坏的问题。在这里，我们描述了 OPTIM 和 PATHSAMPLE 程序中用于能量景观探索的算法，以及它们在生物分子模拟中的应用。我们介绍了 RNA 7SK 的 5'-发夹的最近案例研究，以说明如何应用该方法来解释实验结果，并获得分子性质的详细描述。
