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 function^3,4, 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.

大部分人类基因组被转录成 RNA¹，其中许多包含对其功能很重要的结构元件。这种 RNA 分子（包括那些结构合理且折叠良好的² 分子）在构象上具有异质性和柔性，这是功能的先决条件^3,4，但这限制了 NMR、晶体学和冷冻电子显微镜等方法对结构解析的适用性。此外，由于缺乏大型 RNA 结构数据库，并且序列和结构之间没有明确的相关性，因此用于蛋白质结构预测的 AlphaFold⁵ 等方法不适用于 RNA。因此，确定异质 RNA 的结构仍然是一个未解决的挑战。在这里，我们报告了使用原子力显微镜、无监督机器学习和深度神经网络 （HORNET） 的整体 RNA 结构测定方法，这是一种使用溶液中单个分子的原子力显微镜图像确定 RNA 三维拓扑结构的新方法。由于原子力显微镜的高信噪比，该方法非常适合捕获不同构象的大 RNA 分子的结构。除了六个基准案例外，我们还通过确定 RNase P RNA 和 HIV-1 Rev 反应元件 （RRE） RNA 的多个异质结构来证明 HORNET 的实用性。因此，我们的方法解决了确定大而灵活的 RNA 分子的异质结构的主要挑战之一，并有助于对 RNA 结构生物学的基本理解。