Geometric deep learning of RNA structure

doi:10.1126/science.abe5650

GSTDTAP > 气候变化

DOI	10.1126/science.abe5650
	Geometric deep learning of RNA structure
	Raphael J. L. Townshend; Stephan Eismann; Andrew M. Watkins; Ramya Rangan; Maria Karelina; Rhiju Das; Ron O. Dror
	2021-08-27
发表期刊	Science
出版年	2021
英文摘要	RNA molecules fold into complex three-dimensional shapes that are difficult to determine experimentally or predict computationally. Understanding these structures may aid in the discovery of drugs for currently untreatable diseases. Townshend et al. introduced a machine-learning method that significantly improves prediction of RNA structures (see the Perspective by Weeks). Most other recent advances in deep learning have required a tremendous amount of data for training. The fact that this method succeeds given very little training data suggests that related methods could address unsolved problems in many fields where data are scarce. Science , abe5650, this issue p. [1047][1]; see also abk1971, p. [964][2] RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond. [1]: /lookup/doi/10.1126/science.abe5650 [2]: /lookup/doi/10.1126/science.abk1971
领域	气候变化 ; 资源环境
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/336678
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Raphael J. L. Townshend,Stephan Eismann,Andrew M. Watkins,et al. Geometric deep learning of RNA structure[J]. Science,2021.
APA	Raphael J. L. Townshend.,Stephan Eismann.,Andrew M. Watkins.,Ramya Rangan.,Maria Karelina.,...&Ron O. Dror.(2021).Geometric deep learning of RNA structure.Science.
MLA	Raphael J. L. Townshend,et al."Geometric deep learning of RNA structure".Science (2021).