Computational Approaches for RNA Structure Ensemble Deconvolution from Structure Probing Data.

RNA structure ensemble SHAPE clustering partition function thermodynamics

Journal

Journal of molecular biology
ISSN: 1089-8638
Titre abrégé: J Mol Biol
Pays: Netherlands
ID NLM: 2985088R

Informations de publication

Date de publication:
30 09 2022
Historique:
received: 14 03 2022
revised: 29 04 2022
accepted: 05 05 2022
pubmed: 21 5 2022
medline: 16 9 2022
entrez: 20 5 2022
Statut: ppublish

Résumé

RNA structure probing experiments have emerged over the last decade as a straightforward way to determine the structure of RNA molecules in a number of different contexts. Although powerful, the ability of RNA to dynamically interconvert between, and to simultaneously populate, alternative structural configurations, poses a nontrivial challenge to the interpretation of data derived from these experiments. Recent efforts aimed at developing computational methods for the reconstruction of coexisting alternative RNA conformations from structure probing data are paving the way to the study of RNA structure ensembles, even in the context of living cells. In this review, we critically discuss these methods, their limitations and possible future improvements.

Identifiants

pubmed: 35595163
pii: S0022-2836(22)00215-7
doi: 10.1016/j.jmb.2022.167635
pii:
doi:

Substances chimiques

RNA 63231-63-0

Types de publication

Journal Article Review Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

167635

Informations de copyright

Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Sharon Aviran (S)

Biomedical Engineering Department and Genome Center, University of California, Davis, CA, USA. Electronic address: saviran@ucdavis.edu.

Danny Incarnato (D)

Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, the Netherlands. Electronic address: saviran@ucdavis.edu.

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Classifications MeSH