Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA.
Department of Engineering Science and Mechanics, Penn State University, State College, PA, USA.
Methods Mol Biol. 2023;2709:51-64. doi: 10.1007/978-1-0716-3417-2_3.
Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.
精确的 RNA 三级结构预测可以帮助设计 RNA 纳米颗粒。然而，大多数现有的 RNA 三级结构预测方法仅限于具有相对简单二级结构的小型 RNA。大型 RNA 分子通常具有复杂的二级结构，包括多分支环和假结，允许 RNA 具有高度灵活的几何形状和多个稳定状态。各种实验和生物信息学分析通常可以提供 RNA 中原子（或残基）之间的距离信息，这些信息可用于指导 RNA 三级结构的预测。在本章中，我们将介绍一个平台 iFoldNMR，该平台可以将 NMR 中的非交换亚氨基质子共振数据作为 RNA 三维结构预测的约束条件。我们还介绍了一种算法 DVASS，它可以优化距离约束条件，以更好地预测 RNA 三维结构。
