Computational prediction of RNA tertiary structures using machine learning methods?

• 作者: Bin Huang Jian Zhang Jun Wang Shuai Zhang Wenfei Li Yuanyang Du
• 来源期刊: 中国物理B（英文版）
• 年，卷（期）: 2020,（10）
• 所属期刊栏目: TOPICAL REVIEW — Modeling and simulations for the structures and functions of proteins and nucleic acids
• 页码范围: 19-26
• 页数: 8页
• 语种: 英文
• DOI: 10.1088/1674-1056/abb303

摘要

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.