Kerpedjiev Peter, Höner Zu Siederdissen Christian, Hofacker Ivo L
Institute for Theoretical Chemistry, A-1090 Vienna, Austria.
Institute for Theoretical Chemistry, A-1090 Vienna, Austria Bioinformatics Group, Department of Computer Science, Universität Leipzig, D-04107 Leipzig, Germany Interdisciplinary Center for Bioinformatics, Universität Leipzig, D-04107 Leipzig, Germany.
RNA. 2015 Jun;21(6):1110-21. doi: 10.1261/rna.047522.114. Epub 2015 Apr 22.
A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a fine-grain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures.
RNA结构的三维模型能够提供关于其功能和调控的信息，而仅靠序列或二级结构是无法做到这一点的。当前的模型存在准确性低和运行时间长的问题，并且要么忽略了稳定三级结构的长程相互作用，要么假定已经了解这些相互作用。与全原子模型相比，我们基于螺旋的粗粒度三级结构模型仅具有少数几个自由度，同时在给定二级结构的情况下仍保留对三级结构进行采样的能力。它在全原子三级结构模型的精度与二级结构表示的简单性和有效性之间取得了平衡。它为探索RNA结构内螺旋和环的全局排列提供了一个简化工具。我们给出了一个仅依赖于茎和环位置的新型能量函数的示例。我们表明，将我们的模型与该能量函数相结合所产生的预测结果与当前最先进的工具一样好，甚至更好。我们提出，鉴于需要探索的构象空间范围广泛，与耦合了细粒度能量函数的全原子模型相比，粗粒度方法能够以更少的迭代次数探索更多的构象。最后，我们强调提供预测结构集合这一总体主题，这是我们的工具所擅长的，而不是提供少数几个能量最低的结构。
