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Pseudoknots in RNA folding landscapes

Motivation: The function of an RNA molecule is not only linked to its native structure, which is usually taken to be the ground state of its folding landscape, but also in many cases crucially depends on the details of the folding pathways such as stable folding intermediates or the timing of the fo...

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Autores principales: Kucharík, Marcel, Hofacker, Ivo L., Stadler, Peter F., Qin, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4708108/
https://www.ncbi.nlm.nih.gov/pubmed/26428288
http://dx.doi.org/10.1093/bioinformatics/btv572
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author Kucharík, Marcel
Hofacker, Ivo L.
Stadler, Peter F.
Qin, Jing
author_facet Kucharík, Marcel
Hofacker, Ivo L.
Stadler, Peter F.
Qin, Jing
author_sort Kucharík, Marcel
collection PubMed
description Motivation: The function of an RNA molecule is not only linked to its native structure, which is usually taken to be the ground state of its folding landscape, but also in many cases crucially depends on the details of the folding pathways such as stable folding intermediates or the timing of the folding process itself. To model and understand these processes, it is necessary to go beyond ground state structures. The study of rugged RNA folding landscapes holds the key to answer these questions. Efficient coarse-graining methods are required to reduce the intractably vast energy landscapes into condensed representations such as barrier trees or basin hopping graphs (BHG) that convey an approximate but comprehensive picture of the folding kinetics. So far, exact and heuristic coarse-graining methods have been mostly restricted to the pseudoknot-free secondary structures. Pseudoknots, which are common motifs and have been repeatedly hypothesized to play an important role in guiding folding trajectories, were usually excluded. Results: We generalize the BHG framework to include pseudoknotted RNA structures and systematically study the differences in predicted folding behavior depending on whether pseudoknotted structures are allowed to occur as folding intermediates or not. We observe that RNAs with pseudoknotted ground state structures tend to have more pseudoknotted folding intermediates than RNAs with pseudoknot-free ground state structures. The occurrence and influence of pseudoknotted intermediates on the folding pathway, however, appear to depend very strongly on the individual RNAs so that no general rule can be inferred. Availability and implementation: The algorithms described here are implemented in C++ as standalone programs. Its source code and Supplemental material can be freely downloaded from http://www.tbi.univie.ac.at/bhg.html. Contact: qin@bioinf.uni-leipzig.de Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-47081082016-01-12 Pseudoknots in RNA folding landscapes Kucharík, Marcel Hofacker, Ivo L. Stadler, Peter F. Qin, Jing Bioinformatics Original Papers Motivation: The function of an RNA molecule is not only linked to its native structure, which is usually taken to be the ground state of its folding landscape, but also in many cases crucially depends on the details of the folding pathways such as stable folding intermediates or the timing of the folding process itself. To model and understand these processes, it is necessary to go beyond ground state structures. The study of rugged RNA folding landscapes holds the key to answer these questions. Efficient coarse-graining methods are required to reduce the intractably vast energy landscapes into condensed representations such as barrier trees or basin hopping graphs (BHG) that convey an approximate but comprehensive picture of the folding kinetics. So far, exact and heuristic coarse-graining methods have been mostly restricted to the pseudoknot-free secondary structures. Pseudoknots, which are common motifs and have been repeatedly hypothesized to play an important role in guiding folding trajectories, were usually excluded. Results: We generalize the BHG framework to include pseudoknotted RNA structures and systematically study the differences in predicted folding behavior depending on whether pseudoknotted structures are allowed to occur as folding intermediates or not. We observe that RNAs with pseudoknotted ground state structures tend to have more pseudoknotted folding intermediates than RNAs with pseudoknot-free ground state structures. The occurrence and influence of pseudoknotted intermediates on the folding pathway, however, appear to depend very strongly on the individual RNAs so that no general rule can be inferred. Availability and implementation: The algorithms described here are implemented in C++ as standalone programs. Its source code and Supplemental material can be freely downloaded from http://www.tbi.univie.ac.at/bhg.html. Contact: qin@bioinf.uni-leipzig.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-01-15 2015-10-01 /pmc/articles/PMC4708108/ /pubmed/26428288 http://dx.doi.org/10.1093/bioinformatics/btv572 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Kucharík, Marcel
Hofacker, Ivo L.
Stadler, Peter F.
Qin, Jing
Pseudoknots in RNA folding landscapes
title Pseudoknots in RNA folding landscapes
title_full Pseudoknots in RNA folding landscapes
title_fullStr Pseudoknots in RNA folding landscapes
title_full_unstemmed Pseudoknots in RNA folding landscapes
title_short Pseudoknots in RNA folding landscapes
title_sort pseudoknots in rna folding landscapes
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4708108/
https://www.ncbi.nlm.nih.gov/pubmed/26428288
http://dx.doi.org/10.1093/bioinformatics/btv572
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