A probabilistic model of RNA conformational space

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A probabilistic model of RNA conformational space. / Frellsen, Jes; Moltke, Ida; Thiim, Martin; Mardia, Kanti V; Ferkinghoff-Borg, Jesper; Hamelryck, Thomas.

In: PloS Computational Biology, Vol. 5, No. 6, 2009, p. e1000406.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Frellsen, J, Moltke, I, Thiim, M, Mardia, KV, Ferkinghoff-Borg, J & Hamelryck, T 2009, 'A probabilistic model of RNA conformational space', PloS Computational Biology, vol. 5, no. 6, pp. e1000406. https://doi.org/10.1371/journal.pcbi.1000406

APA

Frellsen, J., Moltke, I., Thiim, M., Mardia, K. V., Ferkinghoff-Borg, J., & Hamelryck, T. (2009). A probabilistic model of RNA conformational space. PloS Computational Biology, 5(6), e1000406. https://doi.org/10.1371/journal.pcbi.1000406

Vancouver

Frellsen J, Moltke I, Thiim M, Mardia KV, Ferkinghoff-Borg J, Hamelryck T. A probabilistic model of RNA conformational space. PloS Computational Biology. 2009;5(6):e1000406. https://doi.org/10.1371/journal.pcbi.1000406

Author

Frellsen, Jes ; Moltke, Ida ; Thiim, Martin ; Mardia, Kanti V ; Ferkinghoff-Borg, Jesper ; Hamelryck, Thomas. / A probabilistic model of RNA conformational space. In: PloS Computational Biology. 2009 ; Vol. 5, No. 6. pp. e1000406.

Bibtex

@article{93ba1ec0276611df8ed1000ea68e967b,
title = "A probabilistic model of RNA conformational space",
abstract = "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.",
author = "Jes Frellsen and Ida Moltke and Martin Thiim and Mardia, {Kanti V} and Jesper Ferkinghoff-Borg and Thomas Hamelryck",
note = "Keywords: Algorithms; Bayes Theorem; Computer Simulation; Databases, Nucleic Acid; Imaging, Three-Dimensional; Markov Chains; Models, Molecular; Models, Statistical; Monte Carlo Method; Nucleic Acid Conformation; RNA; Software",
year = "2009",
doi = "10.1371/journal.pcbi.1000406",
language = "English",
volume = "5",
pages = "e1000406",
journal = "P L o S Computational Biology (Online)",
issn = "1553-7358",
publisher = "Public Library of Science",
number = "6",

}

RIS

TY - JOUR

T1 - A probabilistic model of RNA conformational space

AU - Frellsen, Jes

AU - Moltke, Ida

AU - Thiim, Martin

AU - Mardia, Kanti V

AU - Ferkinghoff-Borg, Jesper

AU - Hamelryck, Thomas

N1 - Keywords: Algorithms; Bayes Theorem; Computer Simulation; Databases, Nucleic Acid; Imaging, Three-Dimensional; Markov Chains; Models, Molecular; Models, Statistical; Monte Carlo Method; Nucleic Acid Conformation; RNA; Software

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pcbi.1000406

DO - 10.1371/journal.pcbi.1000406

M3 - Journal article

C2 - 19543381

VL - 5

SP - e1000406

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

SN - 1553-7358

IS - 6

ER -

ID: 18364011