{"title":"RNA ensembles from in vitro to in vivo: Toward predictive models of RNA cellular function","authors":"","doi":"10.1016/j.sbi.2024.102915","DOIUrl":null,"url":null,"abstract":"<div><div>Deepening our understanding of RNA biology and accelerating development of RNA-based therapeutics go hand-in-hand—both requiring a transition from qualitative descriptions of RNA structure to quantitative models capable of predicting RNA behaviors, and from a static to an ensemble view. Ensembles are determined from their free energy landscapes, which define the relative populations of conformational states and the energetic barriers separating them. Experimental determination of RNA ensembles over the past decade has led to powerful predictive models of RNA behavior <em>in vitro</em>. It has also been shown during this time that the cellular environment redistributes RNA ensembles, changing the abundances of functionally relevant conformers relative to <em>in vitro</em> contexts with subsequent functional RNA consequences. However, recent studies have demonstrated that testing models built from <em>in vitro</em> ensembles with highly quantitative measurements of RNA cellular function, aided by emerging computational methodologies, enables predictive modelling of cellular activity and biological discovery.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2024-10-13","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/S0959440X24001428","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
Deepening our understanding of RNA biology and accelerating development of RNA-based therapeutics go hand-in-hand—both requiring a transition from qualitative descriptions of RNA structure to quantitative models capable of predicting RNA behaviors, and from a static to an ensemble view. Ensembles are determined from their free energy landscapes, which define the relative populations of conformational states and the energetic barriers separating them. Experimental determination of RNA ensembles over the past decade has led to powerful predictive models of RNA behavior in vitro. It has also been shown during this time that the cellular environment redistributes RNA ensembles, changing the abundances of functionally relevant conformers relative to in vitro contexts with subsequent functional RNA consequences. However, recent studies have demonstrated that testing models built from in vitro ensembles with highly quantitative measurements of RNA cellular function, aided by emerging computational methodologies, enables predictive modelling of cellular activity and biological discovery.
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