美国国家癌症研究所Yun-Xing Wang研究团队，使用原子力显微镜和深度神经网络确定了RNA构象的结构。相关论文于2024年12月18日在线发表在《自然》杂志上。

据介绍，人类基因组的大部分被转录成RNA，其中许多包含对其功能至关重要的结构元件。这些RNA分子——包括那些结构化且折叠良好的RNA——在构象上是异质的且具有灵活性，这是其功能的前提。然而，这也限制了NMR、晶体学和冷冻电子显微镜等方法在结构阐明中的应用。

此外，由于缺乏大型RNA结构数据库，以及序列与结构之间没有明确的关联，像AlphaFold这样的蛋白质结构预测方法并不适用于RNA。因此，确定异质RNA的结构仍然是一个未解决的挑战。

使用原子力显微镜、无监督机器学习和深度神经网络（HORNET），这是一种通过原子力显微镜图像来确定溶液中单分子RNA的三维拓扑结构的全新方法，研究人员报告了一种综合性的RNA结构测定方法。由于原子力显微镜具有较高的信噪比，这种方法非常适合捕捉大RNA分子在不同构象下的结构。除了六个基准案例外，研究人员还通过HORNET方法确定了RNase P RNA和HIV-1 Rev响应元件（RRE）RNA的多个异质结构。因此，该方法解决了大且灵活RNA分子异质结构测定中的一项重大挑战，并有助于RNA结构生物学的基础理解。

附：英文原文

Title: Determining structures of RNA conformers using AFM and deep neural networks

Author: Degenhardt, Maximilia F. S., Degenhardt, Hermann F., Bhandari, Yuba R., Lee, Yun-Tzai, Ding, Jienyu, Yu, Ping, Heinz, William F., Stagno, Jason R., Schwieters, Charles D., Watts, Norman R., Wingfield, Paul T., Rein, Alan, Zhang, Jinwei, Wang, Yun-Xing

Issue&Volume: 2024-12-18

Abstract: Much of the human genome is transcribed into RNAs1, many of which contain structural elements that are important for their function. Such RNA molecules—including those that are structured and well-folded2—are conformationally heterogeneous and flexible, which is a prerequisite for function3,4, but this limits the applicability of methods such as NMR, crystallography and cryo-electron microscopy for structure elucidation. Moreover, owing to the lack of a large RNA structure database, and no clear correlation between sequence and structure, approaches such as AlphaFold5 for protein structure prediction do not apply to RNA. Therefore, determining the structures of heterogeneous RNAs remains an unmet challenge. Here we report holistic RNA structure determination method using atomic force microscopy, unsupervised machine learning and deep neural networks (HORNET), a novel method for determining three-dimensional topological structures of RNA using atomic force microscopy images of individual molecules in solution. Owing to the high signal-to-noise ratio of atomic force microscopy, this method is ideal for capturing structures of large RNA molecules in distinct conformations. In addition to six benchmark cases, we demonstrate the utility of HORNET by determining multiple heterogeneous structures of RNase P RNA and the HIV-1 Rev response element (RRE) RNA. Thus, our method addresses one of the major challenges in determining heterogeneous structures of large and flexible RNA molecules, and contributes to the fundamental understanding of RNA structural biology.

DOI: 10.1038/s41586-024-07559-x

Source: https://www.nature.com/articles/s41586-024-07559-x