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Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation.

Julie Bernauer 1 Xuhui Huang 2 Adelene y L Sim 3 Michael Levitt 3
1 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
Abstract : RNA molecules play integral roles in gene regulation, and understanding their structures gives us important insights into their biological functions. Despite recent developments in template-based and parameterized energy functions, the structure of RNA--in particular the nonhelical regions--is still difficult to predict. Knowledge-based potentials have proven efficient in protein structure prediction. In this work, we describe two differentiable knowledge-based potentials derived from a curated data set of RNA structures, with all-atom or coarse-grained representation, respectively. We focus on one aspect of the prediction problem: the identification of native-like RNA conformations from a set of near-native models. Using a variety of near-native RNA models generated from three independent methods, we show that our potential is able to distinguish the native structure and identify native-like conformations, even at the coarse-grained level. The all-atom version of our knowledge-based potential performs better and appears to be more effective at discriminating near-native RNA conformations than one of the most highly regarded parameterized potential. The fully differentiable form of our potentials will additionally likely be useful for structure refinement and/or molecular dynamics simulations.
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Submitted on : Tuesday, September 20, 2011 - 12:25:07 PM
Last modification on : Thursday, July 8, 2021 - 3:47:58 AM

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Julie Bernauer, Xuhui Huang, Adelene y L Sim, Michael Levitt. Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation.. RNA, Cold Spring Harbor Laboratory Press, 2011, 17 (6), pp.1066-75. ⟨10.1261/rna.2543711⟩. ⟨inria-00624999⟩



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