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

Mélanie Boudard 1, 2 Julie Bernauer 3, 4 Dominique Barth 1 Johanne Cohen 2 Alain Denise 5, 2, 3, 6
3 AMIB - Algorithms and Models for Integrative Biology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France
5 BIM - BioInformatique Moléculaire
DBG - Département Biologie des Génomes
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:
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Submitted on : Thursday, September 17, 2015 - 4:51:38 PM
Last modification on : Wednesday, April 3, 2019 - 1:57:02 AM

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Mélanie Boudard, Julie Bernauer, Dominique Barth, Johanne Cohen, Alain Denise. GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies.. PLoS ONE, Public Library of Science, 2015, 10 (8), pp.e0136444. ⟨hal-01201665⟩



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