A Probabilistic Model of RNA Conformational Space
| Autor: | Jes Frellsen, Martin Thiim, Kanti V. Mardia, Ida Moltke, Thomas Hamelryck, Jesper Ferkinghoff-Borg |
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| Rok vydání: | 2009 |
| Předmět: |
Biophysics/Theory and Simulation
Models Molecular Theoretical computer science QH301-705.5 Computer science Monte Carlo method Computational Biology/Macromolecular Structure Analysis Computational Biology/Molecular Dynamics Molecular Biology/Bioinformatics 03 medical and health sciences Cellular and Molecular Neuroscience Imaging Three-Dimensional 0302 clinical medicine Fragment (logic) Genetics Computer Simulation Biology (General) Nucleic acid structure Molecular Biology Ecology Evolution Behavior and Systematics 030304 developmental biology Quantitative Biology::Biomolecules 0303 health sciences Models Statistical Ecology Markov chain RNA Conformation RNA Sampling (statistics) Bayes Theorem Statistical model Markov Chains Biophysics/RNA Structure 3. Good health Computational Theory and Mathematics Modeling and Simulation Nucleic Acid Conformation Mathematics/Statistics Databases Nucleic Acid Biological system Monte Carlo Method Algorithms Software 030217 neurology & neurosurgery Research Article |
| Zdroj: | 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, vol. 5, no. 6, pp. e1000406 . https://doi.org/10.1371/journal.pcbi.1000406 Frellsen, J, Moltke, I, Thiim, M, Mardia, K, Ferkinghoff-Borg, J & Hamelryck, T 2009, ' A probabilistic model of RNA conformational space ', P L o S Computational Biology (Online), vol. 5, no. 6, pp. e1000406 . https://doi.org/10.1371/journal.pcbi.1000406 PLoS Computational Biology PLoS Computational Biology, Vol 5, Iss 6, p e1000406 (2009) |
| ISSN: | 1553-7358 |
| DOI: | 10.1371/journal.pcbi.1000406 |
| Popis: | 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 Summary The importance of RNA in biology and medicine has increased immensely over the last several years, due to the discovery of a wide range of important biological processes that are under the guidance of non-coding RNA. As is the case with proteins, the function of an RNA molecule is encoded in its three-dimensional (3-D) structure, which in turn is determined by the molecule's sequence. Therefore, interest in the computational prediction of the 3-D structure of RNA from sequence is great. One of the main bottlenecks in routine prediction and simulation of RNA structure and dynamics is sampling, the efficient generation of RNA-like conformations, ideally in a mathematically and physically sound way. Current methods require the use of unphysical energy functions to amend the shortcomings of the sampling procedure. We have developed a mathematical model that describes RNA's conformational space in atomic detail, without the shortcomings of other sampling methods. As an illustration of its potential, we describe a simple yet efficient method to sample conformations that are compatible with a given secondary structure. An implementation of the sampling method, called BARNACLE, is freely available. |
| Databáze: | OpenAIRE |
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