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RNA 3D Structure Prediction Using Coarse-Grained Models

The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequence...

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Detalles Bibliográficos
Autores principales: Li, Jun, Chen, Shi-Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283274/
https://www.ncbi.nlm.nih.gov/pubmed/34277713
http://dx.doi.org/10.3389/fmolb.2021.720937
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author Li, Jun
Chen, Shi-Jie
author_facet Li, Jun
Chen, Shi-Jie
author_sort Li, Jun
collection PubMed
description The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments.
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spelling pubmed-82832742021-07-17 RNA 3D Structure Prediction Using Coarse-Grained Models Li, Jun Chen, Shi-Jie Front Mol Biosci Molecular Biosciences The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments. Frontiers Media S.A. 2021-07-02 /pmc/articles/PMC8283274/ /pubmed/34277713 http://dx.doi.org/10.3389/fmolb.2021.720937 Text en Copyright © 2021 Li and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Li, Jun
Chen, Shi-Jie
RNA 3D Structure Prediction Using Coarse-Grained Models
title RNA 3D Structure Prediction Using Coarse-Grained Models
title_full RNA 3D Structure Prediction Using Coarse-Grained Models
title_fullStr RNA 3D Structure Prediction Using Coarse-Grained Models
title_full_unstemmed RNA 3D Structure Prediction Using Coarse-Grained Models
title_short RNA 3D Structure Prediction Using Coarse-Grained Models
title_sort rna 3d structure prediction using coarse-grained models
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283274/
https://www.ncbi.nlm.nih.gov/pubmed/34277713
http://dx.doi.org/10.3389/fmolb.2021.720937
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