Wu Kevin E, Zou James Y, Chang Howard
Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA.
Brief Bioinform. 2023 Jul 20;24(4). doi: 10.1093/bib/bbad210.
The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules' secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions.
RNA分子的三维结构在广泛的细胞过程中起着关键作用，这些过程涵盖了从核糖开关到表观遗传调控等多种功能。这些RNA结构极具动态性，实际上可以恰当地描述为一组结构，其分布会根据不同的细胞条件而变化。因此，即使计算蛋白质折叠已经取得了巨大进展，RNA结构的计算预测仍然带来了独特的挑战。在这篇综述中，我们重点关注为预测RNA分子的二级结构以及更复杂的三级结构而开发的各种基于机器学习的方法。我们调查了常用的建模策略，以及其中有多少是受热力学原理启发或纳入了热力学原理。我们讨论了各种设计决策所带来的缺点，并提出了未来的方向，这些方向可以在这些方法的基础上进一步发展，以实现更稳健、准确的RNA结构预测。
