Modeling and Predicting RNA Three-Dimensional Structures

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1269)

Abstract

Modeling the three-dimensional structure of RNAs is a milestone toward better understanding and prediction of nucleic acids molecular functions. Physics-based approaches and molecular dynamics simulations are not tractable on large molecules with all-atom models. To address this issue, coarse-grained models of RNA three-dimensional structures have been developed. In this chapter, we describe a graphical modeling based on the Leontis–Westhof extended base-pair classification. This representation of RNA structures enables us to identify highly conserved structural motifs with complex nucleotide interactions in structure databases. Further, we show how to take advantage of this knowledge to quickly and simply predict three-dimensional structures of large RNA molecules.

Key words

Tertiary structure RNA motifs Extended secondary structure Base-pair classification Modeling Prediction 

References

  1. 1.
    Bekaert M et al (2003) Towards a computational model for -1 eukaryotic frameshifting sites. Bioinformatics 19(3):327–335PubMedCrossRefGoogle Scholar
  2. 2.
    Vitreschak AG et al (2004) Riboswitches: the oldest mechanism for the regulation of gene expression? Trends Genet 20(1):44–50PubMedCrossRefGoogle Scholar
  3. 3.
    Szewczak AA et al (1993) The conformation of the sarcin/ricin loop from 28S ribosomal RNA. Proc Natl Acad Sci U S A 90(20):9581–9585PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Sponer J et al (2012) Chapter 6 molecular dynamics simulations of RNA molecules, in innovations in biomolecular modeling and simulations. R Soc Chem 2:129–155Google Scholar
  5. 5.
    Bernauer J et al (2011) Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation. RNA 17(6):1066–1075PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Ding F et al (2008) Ab initio RNA folding by discrete molecular dynamics: from structure prediction to folding mechanisms. RNA 14(6):1164–1173PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Jonikas MA et al (2009) Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. RNA 15(2):189–199PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Poursina M et al (2011) Strategies for articulated multibody-based adaptive coarse grain simulation of RNA. Methods Enzymol 487:73–98PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Parisien M, Major F (2008) The MC-fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452(7183):51–55PubMedCrossRefGoogle Scholar
  10. 10.
    Martinez HM, Maizel JV Jr, Shapiro BA (2008) RNA2D3D: a program for generating, viewing, and comparing 3-dimensional models of RNA. J Biomol Struct Dyn 25(6):669–683PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Zhao Y et al (2012) Automated and fast building of three-dimensional RNA structures. Sci Rep 2:734PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Das R, Baker D (2007) Automated de novo prediction of native-like RNA tertiary structures. Proc Natl Acad Sci U S A 104(37):14664–14669PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    Das R, Karanicolas J, Baker D (2010) Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Methods 7(4):291–294PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Wang Z, Xu J (2011) A conditional random fields method for RNA sequence-structure relationship modeling and conformation sampling. Bioinformatics 27(13):i102–i110PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Rother M et al (2011) ModeRNA: a tool for comparative modeling of RNA 3D structure. Nucleic Acids Res 39(10):4007–4022PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Leontis NB, Westhof E (2001) Geometric nomenclature and classification of RNA base pairs. RNA 7(4):499–512PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Yang H et al (2003) Tools for the automatic identification and classification of RNA base pairs. Nucleic Acids Res 31(13):3450–3460PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Djelloul M, Denise A (2008) Automated motif extraction and classification in RNA tertiary structures. RNA 14(12):2489–2497PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Leontis N, Zirbel CL (2012) Nonredundant 3D structure datasets for RNA knowledge extraction and benchmarking. In: Leontis N, Westhof E (eds) RNA 3D structure analysis and prediction. Springer, Berlin, pp 281–298CrossRefGoogle Scholar
  20. 20.
    Hofacker IL et al (1994) Fast folding and comparison of RNA secondary structures. Monatsh Chem 125(2):167–188CrossRefGoogle Scholar
  21. 21.
    Reinharz V, Major F, Waldispuhl J (2012) Towards 3D structure prediction of large RNA molecules: an integer programming framework to insert local 3D motifs in RNA secondary structure. Bioinformatics 28(12):i207–i214PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Berman HM et al (1992) The nucleic acid database. A comprehensive relational database of three-dimensional structures of nucleic acids. Biophys J 63(3):751–759PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Bernstein FC et al (1977) The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol 112(3):535–542PubMedCrossRefGoogle Scholar
  24. 24.
    Fukunaga R, Yokoyama S (2007) Structural insights into the first step of RNA-dependent cysteine biosynthesis in archaea. Nat Struct Mol Biol 14(4):272–279PubMedCrossRefGoogle Scholar
  25. 25.
    Lorenz R et al (2011) ViennaRNA Package 2.0. Algorithms Mol Biol 6:26PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    Waugh A et al (2002) RNAML: a standard syntax for exchanging RNA information. RNA 8(6):707–717PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Lemieux S, Major F (2002) RNA canonical and non-canonical base pairing types: a recognition method and complete repertoire. Nucleic Acids Res 30(19):4250–4263PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Chojnowski G, Walen T, Bujnicki JM (2014) RNA Bricks–a database of RNA 3D motifs and their interactions. Nucleic Acids Res 42(1):D123–D131PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Ding Y, Chan CY, Lawrence CE (2005) RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. RNA 11(8):1157–1166PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Zuker M (1989) On finding all suboptimal foldings of an RNA molecule. Science 244(4900):48–52PubMedCrossRefGoogle Scholar
  31. 31.
    Zuker M, Mathews DH, Turner DH (1999) Algorithms and Thermodynamics for RNA Secondary Structure Prediction: A Practical Guide. In: Barciszewski J, Clark BFC (eds) RNA Biochemistry and Biotechnology. Springer, Netherlands, pp 11–43Google Scholar
  32. 32.
    Ding Y, Lawrence CE (2003) A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res 31(24):7280–7301PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Reuter JS, Mathews DH (2010) RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics 11:129PubMedCentralPubMedCrossRefGoogle Scholar
  34. 34.
    Bellaousov S et al (2013) RNAstructure: Web servers for RNA secondary structure prediction and analysis. Nucleic Acids Res 41(Web Server issue):W471–W474PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31(13):3406–3415PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Ding Y, Chan CY, Lawrence CE (2004) Sfold web server for statistical folding and rational design of nucleic acids. Nucleic Acids Res 32(Web Server issue):W135–W141PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Honer zu Siederdissen C et al (2011) A folding algorithm for extended RNA secondary structures. Bioinformatics 27(13):i129–i136PubMedCrossRefGoogle Scholar
  38. 38.
    Do CB, Woods DA, Batzoglou S (2006) CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 22(14):e90–e98PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Computer ScienceMcGill UniversityMontrealCanada

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