GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies.

Mélanie Boudard,Dominique Barth,Johanne Cohen,Alain Denise,Julie Bernauer

doi:10.1371/journal.pone.0136444

Abstract

Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is predicting the spatial arrangement of the various structural elements of RNA. As RNA folding is generally hierarchical, methods involving coarse-grained models hold great promise for this purpose. We present here a novel coarse-grained method for sampling, based on game theory and knowledge-based potentials. This strategy, GARN (Game Algorithm for RNa sampling), is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints. GARN is available from: http://garn.lri.fr/.

Highlights

RNA molecules are involved in diverse biological processes in the cell
An ability to design RNA molecules with a particular function is essential for therapeutics [2], but would be very useful in emerging fields such as nanotechnology [3]
The idea is that a stable solution, referred to as a Nash equilibrium in Game Theory, could be used to represent a stable 3D structure for RNA

Summary

Introduction

RNA molecules are involved in diverse biological processes in the cell. An understanding of the way in which RNA molecules adopt a 3D structure provides considerable insight in to the functional roles of these molecules. An ability to design RNA molecules with a particular function is essential for therapeutics [2], but would be very useful in emerging fields such as nanotechnology [3]. The structural diversity of RNA folds has made prediction a difficult task. The hierarchical nature of the RNA folding process [4,5,6] is the key to successful prediction strategies. Secondary structure prediction strategies [7,8,9,10,11] are very useful as a first step in modeling, because they often provide essential accurate information about the local base structure. Developed methods for the prediction of 3D structure for RNA [12,13,14,15]

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Discussion

Conclusion