Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China.
J Chem Inf Model. 2024 Oct 14;64(19):7386-7397. doi: 10.1021/acs.jcim.4c01324. Epub 2024 Sep 12.
The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence and secondary structure features and neglect small molecule characteristics, and resulting in poor performance on unknown small molecule testing. To overcome this limitation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule binding preferences by learning RNA and small molecule features to capture their interaction information. ZHMol-RLinter also combines sequence and secondary structural features with structural geometric and physicochemical environment information to capture the specificity of RNA spatial conformations in recognizing small molecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on the published RL98 testing set, representing a significant improvement over existing methods. Additionally, ZHMol-RLinter achieved a success rate of 77.1% on the unknown small molecule UNK96 testing set, showing substantial improvement over the existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule binding preferences, even for challenging unknown small molecule testing. Predicting RNA-small molecule binding preferences can help in the understanding of RNA-small molecule interactions and promote the design of RNA-related drugs for biological and medical applications.
RNA 和小分子之间的相互作用在各种生物功能中至关重要。鉴定靶向 RNA 的分子对于抑制剂设计和 RNA 相关研究至关重要。然而，传统方法侧重于学习 RNA 序列和二级结构特征，而忽略了小分子的特征，导致在未知小分子测试中的性能不佳。为了克服这一限制，我们开发了一种基于双层堆叠的机器学习模型，称为 ZHMol-RLinter。这种方法通过学习 RNA 和小分子的特征来更有效地预测 RNA-小分子结合偏好，以捕获它们的相互作用信息。ZHMol-RLinter 还结合了序列和二级结构特征与结构几何和物理化学环境信息，以捕获 RNA 空间构象识别小分子的特异性。我们的结果表明，ZHMol-RLinter 在已发表的 RL98 测试集中的成功率为 90.8%，明显优于现有方法。此外，ZHMol-RLinter 在未知小分子 UNK96 测试集中的成功率为 77.1%，明显优于现有方法。预测结构的评估证实，ZHMol-RLinter 可靠且准确地预测了 RNA-小分子结合偏好，即使对于具有挑战性的未知小分子测试也是如此。预测 RNA-小分子结合偏好可以帮助理解 RNA-小分子相互作用，并促进 RNA 相关药物的设计，用于生物和医学应用。
