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KGSrna: Efficient 3D Kinematics-Based Sampling for Nucleic Acids

Julie Bernauer 1, 2 Rasmus Fonseca 3, 2, 1 Henry van den Bedem 4
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 : Noncoding ribonucleic acids (RNA) play a critical role in a wide variety of cellular processes, ranging from regulating gene expression to post-translational modification and protein synthesis. Their activity is modulated by highly dynamic exchanges between three-dimensional conformational substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules. Our procedure projects degrees of freedom onto a subspace of conformation space defined by distance constraints in the tertiary structure. The dimensionality reduction enables efficient exploration of conformational space. We show that the conformational distributions obtained with our method broadly sample the conformational landscape observed in NMR experiments. Compared to normal mode analysis-based exploration, our procedure diffuses faster through the experimental ensemble while also accessing conformational substates to greater precision. Our results suggest that conformational sampling with a highly reduced but fully atomistic representation of noncoding RNA expresses key features of their dynamic nature.
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Submitted on : Monday, January 18, 2016 - 11:16:24 AM
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Julie Bernauer, Rasmus Fonseca, Henry van den Bedem. KGSrna: Efficient 3D Kinematics-Based Sampling for Nucleic Acids. RECOMB 2015, Apr 2015, Warsaw, Poland. ⟨10.1007/978-3-319-16706-0_11⟩. ⟨hal-01257755⟩



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