Predicting RNA 3D structure using a coarse-grain helix-centered model

A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a fine-grain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures.

[1]  W. Kabsch A solution for the best rotation to relate two sets of vectors , 1976 .

[2]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[3]  M J Sippl,et al.  Knowledge-based potentials for proteins. , 1995, Current opinion in structural biology.

[4]  Thomas A. Steitz,et al.  RNA tertiary interactions in the large ribosomal subunit: The A-minor motif , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[5]  P. Gendron,et al.  Quantitative analysis of nucleic acid three-dimensional structures. , 2001, Journal of molecular biology.

[6]  Michael Zuker,et al.  Mfold web server for nucleic acid folding and hybridization prediction , 2003, Nucleic Acids Res..

[7]  Dominik Endres,et al.  A new metric for probability distributions , 2003, IEEE Transactions on Information Theory.

[8]  Eric Westhof,et al.  Recurrent structural RNA motifs, Isostericity Matrices and sequence alignments , 2005, Nucleic acids research.

[9]  Jason A Greenbaum,et al.  Mapping nucleic acid structure by hydroxyl radical cleavage. , 2005, Current opinion in chemical biology.

[10]  E. Westhof,et al.  The building blocks and motifs of RNA architecture. , 2006, Current opinion in structural biology.

[11]  Serafim Batzoglou,et al.  CONTRAfold: RNA secondary structure prediction without physics-based models , 2006, ISMB.

[12]  R. Altman,et al.  Coplanar and coaxial orientations of RNA bases and helices. , 2007, RNA.

[13]  D. Baker,et al.  Automated de novo prediction of native-like RNA tertiary structures , 2007, Proceedings of the National Academy of Sciences.

[14]  F. Ding,et al.  Ab initio RNA folding by discrete molecular dynamics: from structure prediction to folding mechanisms. , 2008, RNA.

[15]  Craig L. Zirbel,et al.  FR3D: finding local and composite recurrent structural motifs in RNA 3D structures , 2007, Journal of mathematical biology.

[16]  R. Knight,et al.  From knotted to nested RNA structures: a variety of computational methods for pseudoknot removal. , 2008, RNA.

[17]  F. Major,et al.  The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data , 2008, Nature.

[18]  Magdalena A. Jonikas,et al.  Structural inference of native and partially folded RNA by high-throughput contact mapping , 2008, Proceedings of the National Academy of Sciences.

[19]  Kanti V. Mardia,et al.  A Probabilistic Model of RNA Conformational Space , 2009, PLoS Comput. Biol..

[20]  Magdalena A. Jonikas,et al.  Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. , 2009, RNA.

[21]  Robert Giegerich,et al.  Prediction of RNA Secondary Structure Including Kissing Hairpin Motifs , 2010, WABI.

[22]  Sandro Bottaro,et al.  Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized , 2010, PloS one.

[23]  T. Ha,et al.  Multivector Fluorescence Analysis of the xpt Guanine Riboswitch Aptamer Domain and the Conformational Role of Guanine† , 2010, Biochemistry.

[24]  D. Herschlag,et al.  The ligand-free state of the TPP riboswitch: a partially folded RNA structure. , 2010, Journal of molecular biology.

[25]  Michael Sarver,et al.  FR 3 D : finding local and composite recurrent structural motifs in RNA 3 D structures , 2010 .

[26]  C. Brooks,et al.  3D maps of RNA interhelical junctions , 2011, Nature Protocols.

[27]  Michael Levitt,et al.  Clustering to identify RNA conformations constrained by secondary structure , 2011, Proceedings of the National Academy of Sciences.

[28]  Peter F. Stadler,et al.  A folding algorithm for extended RNA secondary structures , 2011, Bioinform..

[29]  A. Datta,et al.  Heuristic RNA pseudoknot prediction including intramolecular kissing hairpins. , 2011, RNA.

[30]  Peter F. Stadler,et al.  ViennaRNA Package 2.0 , 2011, Algorithms for Molecular Biology.

[31]  S. Butcher,et al.  The molecular interactions that stabilize RNA tertiary structure: RNA motifs, patterns, and networks. , 2011, Accounts of chemical research.

[32]  Jacek Blazewicz,et al.  Automated 3D structure composition for large RNAs , 2012, Nucleic acids research.

[33]  P. Cary,et al.  The low-resolution solution structure of Vibrio cholerae Hfq in complex with Qrr1 sRNA , 2012, Nucleic acids research.

[34]  Yangyu Huang,et al.  Automated and fast building of three-dimensional RNA structures , 2012, Scientific Reports.

[35]  Craig L. Zirbel,et al.  Nonredundant 3D Structure Datasets for RNA Knowledge Extraction and Benchmarking , 2012 .

[36]  Jérôme Waldispühl,et al.  Towards 3D structure prediction of large RNA molecules: an integer programming framework to insert local 3D motifs in RNA secondary structure , 2012, Bioinform..

[37]  Magdalena A. Jonikas,et al.  Understanding the role of three-dimensional topology in determining the folding intermediates of group I introns. , 2013, Biophysical journal.

[38]  Jan Gorodkin,et al.  Automated identification of RNA 3D modules with discriminative power in RNA structural alignments , 2013, Nucleic acids research.

[39]  Dominique Barth,et al.  An Algorithmic Game-Theory Approach for Coarse-Grain Prediction of RNA 3D Structure , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[40]  Anton I. Petrov,et al.  Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas , 2013, RNA.

[41]  K. Mardia,et al.  Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method , 2014, Proteins.

[42]  T. Schlick,et al.  Graph-based sampling for approximating global helical topologies of RNA , 2014, Proceedings of the National Academy of Sciences.

[43]  Christoph Flamm,et al.  Sequence-controlled RNA self-processing: computational design, biochemical analysis, and visualization by AFM , 2015, RNA.