Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA.
Nucleic Acids Res. 2023 Apr 24;51(7):3341-3356. doi: 10.1093/nar/gkad122.
RNA 3D structures are critical for understanding their functions. However, only a limited number of RNA structures have been experimentally solved, so computational prediction methods are highly desirable. Nevertheless, accurate prediction of RNA 3D structures, especially those containing multiway junctions, remains a significant challenge, mainly due to the complicated non-canonical base pairing and stacking interactions in the junction loops and the possible long-range interactions between loop structures. Here we present RNAJP ('RNA Junction Prediction'), a nucleotide- and helix-level coarse-grained model for the prediction of RNA 3D structures, particularly junction structures, from a given 2D structure. Through global sampling of the 3D arrangements of the helices in junctions using molecular dynamics simulations and in explicit consideration of non-canonical base pairing and base stacking interactions as well as long-range loop-loop interactions, the model can provide significantly improved predictions for multibranched junction structures than existing methods. Moreover, integrated with additional restraints from experiments, such as junction topology and long-range interactions, the model may serve as a useful structure generator for various applications.
RNA 的 3D 结构对于理解其功能至关重要。然而，只有有限数量的 RNA 结构已经通过实验解决，因此计算预测方法是非常需要的。然而，准确预测 RNA 的 3D 结构，特别是那些含有多分支连接的结构，仍然是一个重大挑战，主要是由于连接环中的复杂非规范碱基配对和堆积相互作用以及可能的长程环结构之间的相互作用。这里我们提出了 RNAJP（“RNA 连接预测”），这是一种核苷酸和螺旋级别的粗粒度模型，用于从给定的 2D 结构预测 RNA 的 3D 结构，特别是连接结构。通过使用分子动力学模拟对连接中螺旋的 3D 排列进行全局采样，并明确考虑非规范碱基配对和碱基堆积相互作用以及长程环-环相互作用，该模型可以比现有方法提供对多分支连接结构的显著改进预测。此外，与实验的附加约束（如连接拓扑和长程相互作用）相结合，该模型可以作为各种应用的有用结构生成器。
