大语言建模和深度学习为 RNA 结构预测提供了启示。

IF 36.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods Pub Date : 2024-11-21 DOI:10.1038/s41592-024-02488-z

引用次数: 0

摘要

我们提出了一个基于RNA语言模型的深度学习管道，用于准确和快速的从头开始RNA 3D结构预测，展示了单链RNA建模的高准确性和跨RNA家族和类型的出色泛化，同时也能够捕获局部特征，如螺旋间角和二级结构。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Large language modeling and deep learning shed light on RNA structure prediction

查看原文本刊更多论文

Large language modeling and deep learning shed light on RNA structure prediction

We present an RNA language model-based deep learning pipeline for accurate and rapid de novo RNA 3D structure prediction, demonstrating strong accuracy in modeling single-stranded RNAs and excellent generalization across RNA families and types while also being capable of capturing local features such as interhelical angles and secondary structures.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Nature Methods 生物-生化研究方法

CiteScore

58.70

自引率

1.70%

发文量

326

审稿时长

1 months

期刊介绍： Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.