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An Algorithmic Game-Theory Approach for Coarse-Grain Prediction of RNA 3D Structure
Jan.-Feb. 2013 (vol. 10 no. 1)
pp. 193-199
Alexis Lamiable, PRiSM, Univ. de Versailles-St-Quentin-en-Yvelines, Versailles, France
Franck Quessette, PRiSM, Univ. de Versailles-St-Quentin-en-Yvelines, Versailles, France
Sandrine Vial, PRiSM, Univ. de Versailles-St-Quentin-en-Yvelines, Versailles, France
Dominique Barth, PRiSM, Univ. de Versailles-St-Quentin-en-Yvelines, Versailles, France
Alain Denise, LRI, Univ. Paris-Sud, Orsay, France
We present a new approach for the prediction of the coarse-grain 3D structure of RNA molecules. We model a molecule as being made of helices and junctions. Those junctions are classified into topological families that determine their preferred 3D shapes. All the parts of the molecule are then allowed to establish long-distance contacts that induce a 3D folding of the molecule. An algorithm relying on game theory is proposed to discover such long-distance contacts that allow the molecule to reach a Nash equilibrium. As reported by our experiments, this approach allows one to predict the global shape of large molecules of several hundreds of nucleotides that are out of reach of the state-of-the-art methods.
Index Terms:
Junctions,Shape,Skeleton,Optimization,Games,RNA,Game theory,game theory,RNA,tertiary structure prediction,coarse-grain structure prediction
Citation:
Alexis Lamiable, Franck Quessette, Sandrine Vial, Dominique Barth, Alain Denise, "An Algorithmic Game-Theory Approach for Coarse-Grain Prediction of RNA 3D Structure," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 1, pp. 193-199, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.148
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