RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.

RNA是一种独特的生物大分子，既可以记录遗传信息，又可以在多种分子过程中发挥生物学功能，包括转录、剪接、翻译，甚至调节蛋白质功能。 RNA 采用特定的三维构象来实现其功能。使用 X 射线晶体学实验测定高分辨率 RNA 结构既费力又需要专业知识，因此阻碍了我们对 RNA 结构生物学的理解。 RNA结构的计算模型是生物信息学诞生的一个里程碑。尽管计算建模在过去十年中得到了很大的改进，并出现了许多成功案例，但这种计算建模的准确性不仅取决于长度，而且还根据结构的复杂程度而变化。为了提高可信度，各种实验数据被集成到计算模型中。在这篇综述中，我们总结了可以整合到RNA结构建模中的实验以及基于这些实验数据的计算方法。我们还演示了计算模型如何帮助实验确定 RNA 结构。我们重点介绍计算建模的最新进展，它可以使用高通量实验数据提供可靠的结构模型。