201. Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation
- Author
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Xuhui Huang, Michael Levitt, Julie Bernauer, Adelene Y. L. Sim, Algorithms and Models for Integrative Biology (AMIB ), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Chemistry [HKUST], Hong Kong University of Science and Technology (HKUST), Department of Structural Biology [Stanford], Stanford Medicine, Stanford University-Stanford University, EA GNAPI, and École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Recherche en Informatique (LRI)
- Subjects
Models, Molecular ,Knowledge Bases ,Parameterized complexity ,Biology ,Molecular Dynamics Simulation ,Bioinformatics ,Crystallography, X-Ray ,01 natural sciences ,Article ,03 medical and health sciences ,Molecular dynamics ,0103 physical sciences ,Differentiable function ,Nucleic acid structure ,Representation (mathematics) ,Molecular Biology ,030304 developmental biology ,0303 health sciences ,010304 chemical physics ,[SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM] ,RNA Conformation ,RNA ,Protein structure prediction ,[SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM] ,Nucleic Acid Conformation ,Biological system - Abstract
International audience; 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.
- Published
- 2011
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