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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9029)


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.


Root Mean Square Deviation Normal Mode Analysis Ribose Ring Elastic Network Model Conformational Substate 
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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.INRIA Saclay Ile de FrancePalaiseauFrance
  2. 2.Laboratoire D’Informatique de L’École Polytechnique (LIX), CNRS UMR 7161École PolytechniquePalaiseauFrance
  3. 3.Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark
  4. 4.Joint Center for Structural Genomics, Stanford Synchrotron Radiation LightsourceStanford UniversityMenlo ParkUSA

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