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Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks
The vast percentage of the human genome is transcribed into RNA, many of which contain various structural elements and are important for functions. RNA molecules are conformationally heterogeneous and functionally dyanmics(1), even when they are structured and well-folded(2), which limit the applica...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327248/ https://www.ncbi.nlm.nih.gov/pubmed/37425706 http://dx.doi.org/10.21203/rs.3.rs-2798658/v1 |
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author | Degenhardt, Maximilia F. S. Degenhardt, Hermann F. Bhandari, Yuba R. Lee, Yun-Tzai Ding, Jienyu Heinz, William F. Stagno, Jason R. Schwieters, Charles D. Zhang, Jinwei Wang, Yun-Xing |
author_facet | Degenhardt, Maximilia F. S. Degenhardt, Hermann F. Bhandari, Yuba R. Lee, Yun-Tzai Ding, Jienyu Heinz, William F. Stagno, Jason R. Schwieters, Charles D. Zhang, Jinwei Wang, Yun-Xing |
author_sort | Degenhardt, Maximilia F. S. |
collection | PubMed |
description | The vast percentage of the human genome is transcribed into RNA, many of which contain various structural elements and are important for functions. RNA molecules are conformationally heterogeneous and functionally dyanmics(1), even when they are structured and well-folded(2), which limit the applicability of methods such as NMR, crystallography, or cryo-EM. Moreover, because of the lack of a large structure RNA database, and no clear correlation between sequence and structure, approaches like AlphaFold(3) for protein structure prediction, do not apply to RNA. Therefore determining the structures of heterogeneous RNA is an unmet challenge. Here we report a novel method of determining RNA three-dimensional topological structures using deep neural networks and atomic force microscopy (AFM) images of individual RNA molecules in solution. Owing to the high signal-to-noise ratio of AFM, our method is ideal for capturing structures of individual conformationally heterogeneous RNA. We show that our method can determine 3D topological structures of any large folded RNA conformers, from ~ 200 to ~ 420 residues, the size range that most functional RNA structures or structural elements fall into. Thus our method addresses one of the major challenges in frontier RNA structural biology and may impact our fundamental understanding of RNA structure. |
format | Online Article Text |
id | pubmed-10327248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-103272482023-07-08 Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks Degenhardt, Maximilia F. S. Degenhardt, Hermann F. Bhandari, Yuba R. Lee, Yun-Tzai Ding, Jienyu Heinz, William F. Stagno, Jason R. Schwieters, Charles D. Zhang, Jinwei Wang, Yun-Xing Res Sq Article The vast percentage of the human genome is transcribed into RNA, many of which contain various structural elements and are important for functions. RNA molecules are conformationally heterogeneous and functionally dyanmics(1), even when they are structured and well-folded(2), which limit the applicability of methods such as NMR, crystallography, or cryo-EM. Moreover, because of the lack of a large structure RNA database, and no clear correlation between sequence and structure, approaches like AlphaFold(3) for protein structure prediction, do not apply to RNA. Therefore determining the structures of heterogeneous RNA is an unmet challenge. Here we report a novel method of determining RNA three-dimensional topological structures using deep neural networks and atomic force microscopy (AFM) images of individual RNA molecules in solution. Owing to the high signal-to-noise ratio of AFM, our method is ideal for capturing structures of individual conformationally heterogeneous RNA. We show that our method can determine 3D topological structures of any large folded RNA conformers, from ~ 200 to ~ 420 residues, the size range that most functional RNA structures or structural elements fall into. Thus our method addresses one of the major challenges in frontier RNA structural biology and may impact our fundamental understanding of RNA structure. American Journal Experts 2023-06-07 /pmc/articles/PMC10327248/ /pubmed/37425706 http://dx.doi.org/10.21203/rs.3.rs-2798658/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Degenhardt, Maximilia F. S. Degenhardt, Hermann F. Bhandari, Yuba R. Lee, Yun-Tzai Ding, Jienyu Heinz, William F. Stagno, Jason R. Schwieters, Charles D. Zhang, Jinwei Wang, Yun-Xing Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks |
title | Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks |
title_full | Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks |
title_fullStr | Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks |
title_full_unstemmed | Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks |
title_short | Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks |
title_sort | determining structures of individual rna conformers using atomic force microscopy images and deep neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327248/ https://www.ncbi.nlm.nih.gov/pubmed/37425706 http://dx.doi.org/10.21203/rs.3.rs-2798658/v1 |
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