Zhuo Chen, Zeng Chengwei, Liu Haoquan, Wang Huiwen, Peng Yunhui, Zhao Yunjie
Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Life (Basel). 2025 Jan 15;15(1):104. doi: 10.3390/life15010104.
The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA-ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA-ligand complexes by examining RNA's sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA-ligand interaction prediction tools.
RNA的多样性和复杂性包括序列、二级结构和三级结构特征。这些元素对于RNA特异性识别其他分子至关重要。随着生物技术的进步,RNA-配体结构使研究人员能够利用实验数据揭示复杂相互作用的机制。然而,通过实验确定这些复合物的结构在技术上具有挑战性,并且常常产生低分辨率的数据。最近出现了许多机器学习计算方法来学习多尺度水平的RNA特征以预测相互作用。预测相互作用仍然是一个未被探索的领域。因此,研究RNA-配体相互作用对于理解生物过程至关重要。在这篇综述中,我们通过研究RNA的序列、二级结构和三级结构来分析RNA-配体复合物的相互作用特征。我们的目标是阐明RNA如何特异性识别配体。此外,我们系统地讨论了预测相互作用的计算方法的进展,并指导未来的研究方向。我们旨在激发创建更可靠的RNA-配体相互作用预测工具。