使用AFM和深度神经网络确定RNA对应物的结构
Maximilia F S Degenhardt1, Hermann F Degenhardt1, Yuba R Bhandari1
1Protein-Nucleic Acid Interaction Section, Center for Structural Biology, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA.
Nature
|December 18, 2024
概括
确定复杂的RNA结构是一个挑战. 一种名为HORNET的新方法使用原子力显微镜和机器学习来揭示灵活的RNA分子的3D形状,进步RNA结构生物学.
科学领域:
- 分子生物学
- 结构生物学
- 生物物理
背景情况:
- 许多功能性RNA在构造上是异质的和灵活的,对NMR和冷EM等传统结构确定技术构成挑战.
- 现有的计算方法,如AlphaFold,由于缺乏全面的结构数据库和清晰的序列结构相关性,因此不能直接应用于RNA.
- 准确确定三维RNA结构对于理解它们的生物功能至关重要.
研究的目的:
- 开发一种用于确定异构RNA分子的三维拓结构的新方法.
- 克服现有方法的局限性,以阐明大型,灵活和形状多样化的RNA结构.
- 为推进RNA结构生物学和了解RNA在生物系统中的作用提供一种新工具.
主要方法:
- 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结构确定方法的开发.
- 使用原子力显微镜 (AFM) 来捕获溶液中的单个RNA分子的高分辨率图像.
- 应用无监督机器学习和深度神经网络来分析AFM数据并确定3D拓结构.
主要成果:
- HORNET成功确定了RNase PRNA和HIV-1Rev响应元素 (RRE) 的多重异构结构.
- 该方法表明信号与噪声的比率很高,使得大RNA分子中的不同形状能够被捕获.
- 使用六个基准案例进行了验证,证实了该方法的准确性和适用性.
结论:
- HORNET提供了一个强大的解决方案来确定大型和灵活的RNA分子的异质结构.
- 这一进步解决了RNA结构生物学中的一个重大挑战,使得人们能够更深入地了解RNA的功能.
- 该方法有助于对RNA结构动态及其对生物过程的影响的基本理解.
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