Computational prediction of RNA tertiary structures using machine learning methods

doi:10.1088/1674-1056/abb303

中国物理B ›› 2020, Vol. 29 ›› Issue (10): 108704-.doi: 10.1088/1674-1056/abb303

所属专题： SPECIAL TOPIC — Modeling and simulations for the structures and functions of proteins and nucleic acids

Bin Huang(黄斌)¹^,², Yuanyang Du(杜渊洋)¹^,², Shuai Zhang(张帅)¹^,², Wenfei Li(李文飞)¹^,², Jun Wang(王骏)¹^,², Jian Zhang(张建)¹^,²^,†()

收稿日期:2020-06-27 修回日期:2020-08-22 接受日期:2020-08-27 出版日期:2020-10-05 发布日期:2020-10-05
通讯作者: Jian Zhang(张建)

Computational prediction of RNA tertiary structures using machine learning methods

Bin Huang(黄斌)^1,2, Yuanyang Du(杜渊洋)^1,2, Shuai Zhang(张帅)^1,2, Wenfei Li(李文飞)^1,2, Jun Wang (王骏)^1,2, and Jian Zhang(张建)^1,2,†

¹ National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
² Institute for Brain Sciences, Kuang Yaming Honors School, Nanjing University, Nanjing 210093, China

Received:2020-06-27 Revised:2020-08-22 Accepted:2020-08-27 Online:2020-10-05 Published:2020-10-05
Contact: ^†Corresponding author. E-mail: jzhang@nju.edu.cn
About author:
†Corresponding author. E-mail: jzhang@nju.edu.cn
* Project supported by the National Natural Science Foundation of China (Grant Nos. 11774158, 11974173, 11774157, and 11934008).

摘要/Abstract

Abstract:

RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.

Key words: RNA structure prediction, RNA scoring function, knowledge-based potentials, machine learning, convolutional neural networks

中图分类号: (Structure of biomolecules)

87.15.B-

87.14.gn (RNA) 07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)

Bin Huang(黄斌), Yuanyang Du(杜渊洋), Shuai Zhang(张帅), Wenfei Li(李文飞), Jun Wang(王骏), Jian Zhang(张建). [J]. 中国物理B, 2020, 29(10): 108704-.

Bin Huang(黄斌), Yuanyang Du(杜渊洋), Shuai Zhang(张帅), Wenfei Li(李文飞), Jun Wang (王骏), and Jian Zhang(张建)†. Computational prediction of RNA tertiary structures using machine learning methods[J]. Chin. Phys. B, 2020, 29(10): 108704-.

图/表 3

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	3dRNAscore	KB	RASP	Rosetta	CNN model
Dataset-I	84/85	80/85	79/85	53/85	62/85
Dataset-II	17/20	20/20	12/20	12/20	19/20
Dataset-III	5/18	–	1/18	4/18	13/18

Computational prediction of RNA tertiary structures using machine learning methods

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