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

Author

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

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/.

Suggested Citation

  • Mélanie Boudard & Julie Bernauer & Dominique Barth & Johanne Cohen & Alain Denise, 2015. "GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
    • Handle: RePEc:plo:pone00:0136444
    DOI: 10.1371/journal.pone.0136444
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    References listed on IDEAS

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    3. Marc Parisien & François Major, 2008. "The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data," Nature, Nature, vol. 452(7183), pages 51-55, March.
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