A Machine Learning Method for RNA–Small Molecule Binding Preference Prediction,Journal of Chemical Information and Modeling

当前位置： X-MOL 学术 › J. Chem. Inf. Model. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A Machine Learning Method for RNA–Small Molecule Binding Preference Prediction
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-09-12 , DOI: 10.1021/acs.jcim.4c01324
Chen Zhuo ₁ , Jiaming Gao ₁ , Anbang Li ₁ , Xuefeng Liu ₂ , Yunjie Zhao ₁

Affiliation

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.

中文翻译：

一种用于 RNA-小分子结合偏好预测的机器学习方法

RNA 和小分子之间的相互作用在各种生物学功能中至关重要。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，而忽视了小分子特性，导致未知小分子检测性能不佳。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 和小分子特征来捕获它们的相互作用信息，从而更有效地预测 RNA-小分子结合偏好。ZHMol-RLinter 还将序列和二级结构特征与结构几何和物理化学环境信息相结合，以捕获 RNA 空间构象在识别小分子方面的特异性。我们的结果表明，ZHMol-RLinter 在已发布的 RL98 测试集上的成功率为 90.8%，与现有方法相比有了显着改进。此外，ZHMol-RLinter 在未知小分子 UNK96 测试集上取得了 77.1% 的成功率，与现有方法相比有了实质性的改进。对预测结构的评估证实，ZHMol-RLinter 在预测 RNA-小分子结合偏好方面是可靠和准确的，即使对于具有挑战性的未知小分子测试也是如此。预测 RNA-小分子结合偏好有助于了解 RNA-小分子相互作用，并促进用于生物和医学应用的 RNA 相关药物的设计。

更新日期：2024-09-12

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南