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Computing the Partition Function for Kinetically Trapped RNA Secondary Structures

An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any...

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Detalles Bibliográficos
Autores principales: Lorenz, William A., Clote, Peter
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3030561/
https://www.ncbi.nlm.nih.gov/pubmed/21297972
http://dx.doi.org/10.1371/journal.pone.0016178
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author Lorenz, William A.
Clote, Peter
author_facet Lorenz, William A.
Clote, Peter
author_sort Lorenz, William A.
collection PubMed
description An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any evolution away from a locally optimal structure must involve energetically unfavorable folding steps. Here, we present a novel, efficient algorithm to compute the partition function over all locally optimal secondary structures of a given RNA sequence. Our software, RNAlocopt runs in [Image: see text] time and [Image: see text] space. Additionally, RNAlocopt samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. We apply RNAlocopt to show that (1) the number of locally optimal structures is far fewer than the total number of structures – indeed, the number of locally optimal structures approximately equal to the square root of the number of all structures, (2) the structural diversity of this subensemble may be either similar to or quite different from the structural diversity of the entire Boltzmann ensemble, a situation that depends on the type of input RNA, (3) the (modified) maximum expected accuracy structure, computed by taking into account base pairing frequencies of locally optimal structures, is a more accurate prediction of the native structure than other current thermodynamics-based methods. The software RNAlocopt constitutes a technical breakthrough in our study of the folding landscape for RNA secondary structures. For the first time, locally optimal structures (kinetic traps in the Turner energy model) can be rapidly generated for long RNA sequences, previously impossible with methods that involved exhaustive enumeration. Use of locally optimal structure leads to state-of-the-art secondary structure prediction, as benchmarked against methods involving the computation of minimum free energy and of maximum expected accuracy. Web server and source code available at http://bioinformatics.bc.edu/clotelab/RNAlocopt/.
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spelling pubmed-30305612011-02-04 Computing the Partition Function for Kinetically Trapped RNA Secondary Structures Lorenz, William A. Clote, Peter PLoS One Research Article An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any evolution away from a locally optimal structure must involve energetically unfavorable folding steps. Here, we present a novel, efficient algorithm to compute the partition function over all locally optimal secondary structures of a given RNA sequence. Our software, RNAlocopt runs in [Image: see text] time and [Image: see text] space. Additionally, RNAlocopt samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. We apply RNAlocopt to show that (1) the number of locally optimal structures is far fewer than the total number of structures – indeed, the number of locally optimal structures approximately equal to the square root of the number of all structures, (2) the structural diversity of this subensemble may be either similar to or quite different from the structural diversity of the entire Boltzmann ensemble, a situation that depends on the type of input RNA, (3) the (modified) maximum expected accuracy structure, computed by taking into account base pairing frequencies of locally optimal structures, is a more accurate prediction of the native structure than other current thermodynamics-based methods. The software RNAlocopt constitutes a technical breakthrough in our study of the folding landscape for RNA secondary structures. For the first time, locally optimal structures (kinetic traps in the Turner energy model) can be rapidly generated for long RNA sequences, previously impossible with methods that involved exhaustive enumeration. Use of locally optimal structure leads to state-of-the-art secondary structure prediction, as benchmarked against methods involving the computation of minimum free energy and of maximum expected accuracy. Web server and source code available at http://bioinformatics.bc.edu/clotelab/RNAlocopt/. Public Library of Science 2011-01-28 /pmc/articles/PMC3030561/ /pubmed/21297972 http://dx.doi.org/10.1371/journal.pone.0016178 Text en Lorenz, Clote. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Lorenz, William A.
Clote, Peter
Computing the Partition Function for Kinetically Trapped RNA Secondary Structures
title Computing the Partition Function for Kinetically Trapped RNA Secondary Structures
title_full Computing the Partition Function for Kinetically Trapped RNA Secondary Structures
title_fullStr Computing the Partition Function for Kinetically Trapped RNA Secondary Structures
title_full_unstemmed Computing the Partition Function for Kinetically Trapped RNA Secondary Structures
title_short Computing the Partition Function for Kinetically Trapped RNA Secondary Structures
title_sort computing the partition function for kinetically trapped rna secondary structures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3030561/
https://www.ncbi.nlm.nih.gov/pubmed/21297972
http://dx.doi.org/10.1371/journal.pone.0016178
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