A probabilistic model of RNA conformational space.

The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling p...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jes Frellsen, Ida Moltke, Martin Thiim, Kanti V Mardia, Jesper Ferkinghoff-Borg, Thomas Hamelryck
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/40f5cd202b474cacb055f9a358bb7385
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:40f5cd202b474cacb055f9a358bb7385
record_format	dspace
spelling	oai:doaj.org-article:40f5cd202b474cacb055f9a358bb73852021-11-25T05:42:20ZA probabilistic model of RNA conformational space.1553-734X1553-735810.1371/journal.pcbi.1000406https://doaj.org/article/40f5cd202b474cacb055f9a358bb73852009-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19543381/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling procedure. Both are only partly solved problems. Here, we focus on the problem of conformational sampling. The current state of the art solution is based on fragment assembly methods, which construct plausible conformations by stringing together short fragments obtained from experimental structures. However, the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows efficient sampling of RNA conformations in continuous space, and with associated probabilities. We show that the model captures several key features of RNA structure, such as its rotameric nature and the distribution of the helix lengths. Furthermore, the model readily generates native-like 3-D conformations for 9 out of 10 test structures, solely using coarse-grained base-pairing information. In conclusion, the method provides a theoretical and practical solution for a major bottleneck on the way to routine prediction and simulation of RNA structure and dynamics in atomic detail.Jes FrellsenIda MoltkeMartin ThiimKanti V MardiaJesper Ferkinghoff-BorgThomas HamelryckPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 6, p e1000406 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jes Frellsen Ida Moltke Martin Thiim Kanti V Mardia Jesper Ferkinghoff-Borg Thomas Hamelryck A probabilistic model of RNA conformational space.
description	The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling procedure. Both are only partly solved problems. Here, we focus on the problem of conformational sampling. The current state of the art solution is based on fragment assembly methods, which construct plausible conformations by stringing together short fragments obtained from experimental structures. However, the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows efficient sampling of RNA conformations in continuous space, and with associated probabilities. We show that the model captures several key features of RNA structure, such as its rotameric nature and the distribution of the helix lengths. Furthermore, the model readily generates native-like 3-D conformations for 9 out of 10 test structures, solely using coarse-grained base-pairing information. In conclusion, the method provides a theoretical and practical solution for a major bottleneck on the way to routine prediction and simulation of RNA structure and dynamics in atomic detail.
format	article
author	Jes Frellsen Ida Moltke Martin Thiim Kanti V Mardia Jesper Ferkinghoff-Borg Thomas Hamelryck
author_facet	Jes Frellsen Ida Moltke Martin Thiim Kanti V Mardia Jesper Ferkinghoff-Borg Thomas Hamelryck
author_sort	Jes Frellsen
title	A probabilistic model of RNA conformational space.
title_short	A probabilistic model of RNA conformational space.
title_full	A probabilistic model of RNA conformational space.
title_fullStr	A probabilistic model of RNA conformational space.
title_full_unstemmed	A probabilistic model of RNA conformational space.
title_sort	probabilistic model of rna conformational space.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/40f5cd202b474cacb055f9a358bb7385
work_keys_str_mv	AT jesfrellsen aprobabilisticmodelofrnaconformationalspace AT idamoltke aprobabilisticmodelofrnaconformationalspace AT martinthiim aprobabilisticmodelofrnaconformationalspace AT kantivmardia aprobabilisticmodelofrnaconformationalspace AT jesperferkinghoffborg aprobabilisticmodelofrnaconformationalspace AT thomashamelryck aprobabilisticmodelofrnaconformationalspace AT jesfrellsen probabilisticmodelofrnaconformationalspace AT idamoltke probabilisticmodelofrnaconformationalspace AT martinthiim probabilisticmodelofrnaconformationalspace AT kantivmardia probabilisticmodelofrnaconformationalspace AT jesperferkinghoffborg probabilisticmodelofrnaconformationalspace AT thomashamelryck probabilisticmodelofrnaconformationalspace
_version_	1718414512542973952

A probabilistic model of RNA conformational space.

Ejemplares similares