An Algorithmic Game-Theory Approach for Coarse-Grain Prediction of RNA 3D Structure

Authors:
Alexis Lamiable;Franck Quessette;Sandrine Vial;Dominique Barth;Alain Denise
Affiliations:
Université de Versailles-St-Quentin-en-Yvelines / CNRS, Versailles and University of Paris-Sud / CNRS, Orsay;Université de Versailles-St-Quentin-en-Yvelines / CNRS, Versailles;Université de Versailles-St-Quentin-en-Yvelines / CNRS, Versailles;Université de Versailles-St-Quentin-en-Yvelines / CNRS, Versailles;University of Paris-Sud, Orsay and INRIA, Saclay
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2013

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Abstract

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.