Determining structures of RNA conformers using AFM and deep neural networks

Maximilia F. S. Degenhardt, Hermann F. Degenhardt, Yuba R. Bhandari, Yun-Tzai Lee, Jienyu Ding, Ping Yu, William F. Heinz, Jason R. Stagno, Charles D. Schwieters, Norman R. Watts, Paul T. Wingfield, Alan Rein, Jinwei Zhang and Yun-Xing Wang ()
Additional contact information
Maximilia F. S. Degenhardt: National Cancer Institute
Hermann F. Degenhardt: National Cancer Institute
Yuba R. Bhandari: National Cancer Institute
Yun-Tzai Lee: National Cancer Institute
Jienyu Ding: National Cancer Institute
Ping Yu: National Cancer Institute
William F. Heinz: Frederick National Laboratory for Cancer Research
Jason R. Stagno: National Cancer Institute
Charles D. Schwieters: National Institutes of Health
Norman R. Watts: National Institutes of Health
Paul T. Wingfield: National Institutes of Health
Alan Rein: National Cancer Institute
Jinwei Zhang: National Institutes of Health
Yun-Xing Wang: National Cancer Institute

Nature, 2025, vol. 637, issue 8048, 1234-1243

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41586-024-07559-x

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