Identification of metal ion-binding sites in RNA structures using deep learning method
Identification of metal ion-binding sites in RNA structures using deep learning method
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DOI：
10.1093/bib/bbad049
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发表时间：
2023-02
影响因子：
9.5
通讯作者：
Yanpeng Zhao;Jingjing Wang;Fubin Chang;Weikang Gong;Yang Liu;Chunhua Li
中科院分区：
文献类型：
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作者：
Yanpeng Zhao;Jingjing Wang;Fubin Chang;Weikang Gong;Yang Liu;Chunhua Li
金属离子是RNA分子正确折叠、结构稳定性和功能不可或缺的因素。然而，实验方法很难在RNA中检测到它们。随着实验解析RNA结构的增加，通过计算机模拟方法识别RNA结构中的金属离子结合位点成为可能。在这里，我们提出了一种称为 Metal3DRNA 的方法，通过使用三维卷积神经网络模型来识别 RNA 结构中最常见的金属离子（Mg2+、Na+ 和 K+）的结合位点。根据金属离子结合环境分析筛选出的负样本比随机选择的样本更像正样本，这有利于构建强大的预测器。样品周围 C、O、N 和 P 原子空间分布的微环境被提取为特征。 Metal3DRNA 显示出令人鼓舞的预测能力，通常超过最先进的方法 FEATURE 和 MetalionRNA。最后，利用可视化方法，我们检查了几种情况下核苷酸原子对分类的贡献，这提供了有助于理解模型的可视化。该方法将有助于RNA结构预测和动力学模拟研究。可用性和实现：源代码可在 https://github.com/ChunhuaLiLab/Metal3DRNA 获取。
Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for experimental methods to detect them in RNAs. With the increase of experimentally resolved RNA structures, it becomes possible to identify the metal ion-binding sites in RNA structures through in-silico methods. Here, we propose an approach called Metal3DRNA to identify the binding sites of the most common metal ions (Mg2+, Na+ and K+) in RNA structures by using a three-dimensional convolutional neural network model. The negative samples, screened out based on the analysis for binding surroundings of metal ions, are more like positive ones than the randomly selected ones, which are beneficial to a powerful predictor construction. The microenvironments of the spatial distributions of C, O, N and P atoms around a sample are extracted as features. Metal3DRNA shows a promising prediction power, generally surpassing the state-of-the-art methods FEATURE and MetalionRNA. Finally, utilizing the visualization method, we inspect the contributions of nucleotide atoms to the classification in several cases, which provides a visualization that helps to comprehend the model. The method will be helpful for RNA structure prediction and dynamics simulation study. Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/Metal3DRNA.