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Physics-based RNA structure prediction

Despite the success of RNA secondary structure prediction for simple, short RNAs, the problem of predicting RNAs with long-range tertiary folds remains. Furthermore, RNA 3D structure prediction is hampered by the lack of the knowledge about the tertiary contacts and their thermodynamic parameters. L...

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Detalles Bibliográficos
Autores principales: Xu, Xiaojun, Chen, Shi-Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4762127/
https://www.ncbi.nlm.nih.gov/pubmed/26942214
http://dx.doi.org/10.1007/s41048-015-0001-4
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author Xu, Xiaojun
Chen, Shi-Jie
author_facet Xu, Xiaojun
Chen, Shi-Jie
author_sort Xu, Xiaojun
collection PubMed
description Despite the success of RNA secondary structure prediction for simple, short RNAs, the problem of predicting RNAs with long-range tertiary folds remains. Furthermore, RNA 3D structure prediction is hampered by the lack of the knowledge about the tertiary contacts and their thermodynamic parameters. Low-resolution structural modeling enables us to estimate the conformational entropies for a number of tertiary folds through rigorous statistical mechanical calculations. The models lead to 3D tertiary folds at coarse-grained level. The coarse-grained structures serve as the initial structures for all-atom molecular dynamics refinement to build the final all-atom 3D structures. In this paper, we present an overview of RNA computational models for secondary and tertiary structures’ predictions and then focus on a recently developed RNA statistical mechanical model—the Vfold model. The main emphasis is placed on the physics behind the models, including the treatment of the non-canonical interactions in secondary and tertiary structure modelings, and the correlations to RNA functions.
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spelling pubmed-47621272016-03-01 Physics-based RNA structure prediction Xu, Xiaojun Chen, Shi-Jie Biophys Rep Methods Despite the success of RNA secondary structure prediction for simple, short RNAs, the problem of predicting RNAs with long-range tertiary folds remains. Furthermore, RNA 3D structure prediction is hampered by the lack of the knowledge about the tertiary contacts and their thermodynamic parameters. Low-resolution structural modeling enables us to estimate the conformational entropies for a number of tertiary folds through rigorous statistical mechanical calculations. The models lead to 3D tertiary folds at coarse-grained level. The coarse-grained structures serve as the initial structures for all-atom molecular dynamics refinement to build the final all-atom 3D structures. In this paper, we present an overview of RNA computational models for secondary and tertiary structures’ predictions and then focus on a recently developed RNA statistical mechanical model—the Vfold model. The main emphasis is placed on the physics behind the models, including the treatment of the non-canonical interactions in secondary and tertiary structure modelings, and the correlations to RNA functions. Springer Berlin Heidelberg 2015-07-09 2015 /pmc/articles/PMC4762127/ /pubmed/26942214 http://dx.doi.org/10.1007/s41048-015-0001-4 Text en © The Author(s) 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Methods
Xu, Xiaojun
Chen, Shi-Jie
Physics-based RNA structure prediction
title Physics-based RNA structure prediction
title_full Physics-based RNA structure prediction
title_fullStr Physics-based RNA structure prediction
title_full_unstemmed Physics-based RNA structure prediction
title_short Physics-based RNA structure prediction
title_sort physics-based rna structure prediction
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4762127/
https://www.ncbi.nlm.nih.gov/pubmed/26942214
http://dx.doi.org/10.1007/s41048-015-0001-4
work_keys_str_mv AT xuxiaojun physicsbasedrnastructureprediction
AT chenshijie physicsbasedrnastructureprediction