Zhou Yuanzhe, Chen Shi-Jie
Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211, USA.
Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA.
RNA Nanomed. 2024 Dec;1(1):1-15. doi: 10.59566/isrnn.2024.0101001.
RNA molecules have emerged as promising therapeutic targets due to their diverse functional and regulatory roles within cells. Computational modeling in RNA-targeted drug discovery presents a significant opportunity to expedite the discovery of novel small molecule compounds. However, this field encounters unique challenges compared to protein-targeted drug design, primarily due to limited experimental data availability and current models' inability to adequately address RNA's conformational flexibility during ligand recognition. Despite these challenges, several studies have successfully identified active RNA-targeting compounds using structure-based approaches or quantitative structure-activity relationship (QSAR) models. This review offers an overview of recent advancements in modeling RNA-small molecule interactions, emphasizing practical applications of computational methods in RNA-targeted drug discovery. Additionally, we survey existing databases that catalog nucleic acid-small molecule interactions. As interest in RNA-small molecule interactions grows and curated databases expand, the field anticipates rapid development. Novel computational models are poised to enhance the identification of potent and selective small-molecule modulators for therapeutic needs.
由于RNA分子在细胞内具有多样的功能和调控作用，它们已成为很有前景的治疗靶点。在以RNA为靶点的药物发现中，计算建模为加速新型小分子化合物的发现提供了重要契机。然而，与以蛋白质为靶点的药物设计相比，该领域面临着独特的挑战，主要原因是实验数据有限，以及当前模型无法在配体识别过程中充分解决RNA的构象灵活性问题。尽管存在这些挑战，一些研究已通过基于结构的方法或定量构效关系（QSAR）模型成功鉴定出了具有活性的RNA靶向化合物。本综述概述了RNA-小分子相互作用建模的最新进展，重点介绍了计算方法在以RNA为靶点的药物发现中的实际应用。此外，我们还调查了现有的编目核酸-小分子相互作用的数据库。随着对RNA-小分子相互作用的兴趣增加以及经过整理的数据库不断扩大，该领域有望快速发展。新型计算模型有望加强对用于治疗需求的强效和选择性小分子调节剂的识别。
