Noncoding RNAs are increasingly found to play a wide variety of roles in living organisms. Yet, their functional mechanisms are poorly understood because their structures are difficult to determine experimentally. As a result, developing more effective computational techniques to predict RNA structures becomes increasingly an urgent task. One key challenge in RNA structure prediction is the lack of an accurate free energy function to guide RNA folding and discriminate native and near-native structures from decoy conformations. In this study, we developed an all-atom distance-dependent knowledge-based energy function for RNA that is based on a reference state (distance-scaled finite ideal-gas reference state, DFIRE) proven successful for protein structure discrimination. Using four separate benchmarks including RNA puzzles, we found that this DFIRE-based RNA statistical energy function is able to discriminate native and near-native structures against decoys with performance comparable with or better than several existing scoring functions compared. The energy function is expected to be useful for improving the detection of RNA near-native structures.

中文翻译：

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基于距离缩放的有限理想气体参考状态的基于全原子知识的 RNA 结构鉴别潜力。

人们越来越多地发现非编码 RNA 在生物体中扮演着各种各样的角色。然而，它们的功能机制知之甚少，因为它们的结构难以通过实验确定。因此，开发更有效的计算技术来预测 RNA 结构变得越来越紧迫。RNA 结构预测的一个关键挑战是缺乏准确的自由能函数来指导 RNA 折叠并将天然和近天然结构与诱饵构象区分开来。在这项研究中，我们为 RNA 开发了一个全原子距离相关的基于知识的能量函数，该函数基于一个参考状态（距离缩放的有限理想气体参考状态，DFIRE），被证明可以成功地识别蛋白质结构。使用四个独立的基准，包括 RNA 谜题，我们发现，这种基于 DFIRE 的 RNA 统计能量函数能够区分原生和近原生结构与诱饵的性能，其性能与几个现有的评分函数相当或更好。预计能量函数可用于改进 RNA 近天然结构的检测。