Jiuming Wang , Yimin Fan , Liang Hong, Zhihang Hu, Yu Li
{"title":"Deep learning for RNA structure prediction","authors":"Jiuming Wang , Yimin Fan , Liang Hong, Zhihang Hu, Yu Li","doi":"10.1016/j.sbi.2025.102991","DOIUrl":null,"url":null,"abstract":"<div><div>Predicting RNA structures from sequences with computational approaches is of vital importance in RNA biology considering the high costs of experimental determination. AI methods have revolutionized this field in recent years, enabling RNA structure prediction with increasingly higher accuracy and efficiency. With an increase in the number of models proposed for this task, this review presents a timely summary of the applications of AI, particularly deep learning, in RNA structure prediction, highlighting their methodology advances as well as the challenges and opportunities for further work in this field.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102991"},"PeriodicalIF":6.1000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in structural biology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959440X25000090","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Predicting RNA structures from sequences with computational approaches is of vital importance in RNA biology considering the high costs of experimental determination. AI methods have revolutionized this field in recent years, enabling RNA structure prediction with increasingly higher accuracy and efficiency. With an increase in the number of models proposed for this task, this review presents a timely summary of the applications of AI, particularly deep learning, in RNA structure prediction, highlighting their methodology advances as well as the challenges and opportunities for further work in this field.
期刊介绍：
Current Opinion in Structural Biology (COSB) aims to stimulate scientifically grounded, interdisciplinary, multi-scale debate and exchange of ideas. It contains polished, concise and timely reviews and opinions, with particular emphasis on those articles published in the past two years. In addition to describing recent trends, the authors are encouraged to give their subjective opinion of the topics discussed.
In COSB, we help the reader by providing in a systematic manner:
1. The views of experts on current advances in their field in a clear and readable form.
2. Evaluations of the most interesting papers, annotated by experts, from the great wealth of original publications.
[...]
The subject of Structural Biology is divided into twelve themed sections, each of which is reviewed once a year. Each issue contains two sections, and the amount of space devoted to each section is related to its importance.
-Folding and Binding-
Nucleic acids and their protein complexes-
Macromolecular Machines-
Theory and Simulation-
Sequences and Topology-
New constructs and expression of proteins-
Membranes-
Engineering and Design-
Carbohydrate-protein interactions and glycosylation-
Biophysical and molecular biological methods-
Multi-protein assemblies in signalling-
Catalysis and Regulation
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
