RNA 3D Structure Modeling by Combination of Template-Based Method ModeRNA, Template-Free Folding with SimRNA, and Refinement with QRNAS

  • Pawel Piatkowski
  • Joanna M. Kasprzak
  • Deepak Kumar
  • Marcin Magnus
  • Grzegorz Chojnowski
  • Janusz M. Bujnicki
Part of the Methods in Molecular Biology book series (MIMB, volume 1490)


RNA encompasses an essential part of all known forms of life. The functions of many RNA molecules are dependent on their ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that either utilize information derived from known structures of other RNA molecules (by way of template-based modeling) or attempt to simulate the physical process of RNA structure formation (by way of template-free modeling). All computational methods suffer from various limitations that make theoretical models less reliable than high-resolution experimentally determined structures. This chapter provides a protocol for computational modeling of RNA 3D structure that overcomes major limitations by combining two complementary approaches: template-based modeling that is capable of predicting global architectures based on similarity to other molecules but often fails to predict local unique features, and template-free modeling that can predict the local folding, but is limited to modeling the structure of relatively small molecules. Here, we combine the use of a template-based method ModeRNA with a template-free method SimRNA. ModeRNA requires a sequence alignment of the target RNA sequence to be modeled with a template of the known structure; it generates a model that predicts the structure of a conserved core and provides a starting point for modeling of variable regions. SimRNA can be used to fold small RNAs (<80 nt) without any additional structural information, and to refold parts of models for larger RNAs that have a correctly modeled core. ModeRNA can be either downloaded, compiled and run locally or run through a web interface at SimRNA is currently available to download for local use as a precompiled software package at and as a web server at For model optimization we use QRNAS, available at

Key words

RNA structure Comparative modeling Homology modeling Free modeling De novo modeling Monte Carlo simulation Statistical potential 



We would like to thank Wayne Dawson for critical reading of the manuscript and valuable comments and suggestions. This work was supported mainly by the National Science Centre (NCN) [2012/04/A/NZ2/00455 to J.M.B.]. D.K. was supported by the Foundation for Polish Science (FNP) [grant MPD/2010/3 to Prof. Artur Jarmolowski, project cofinanced by the European Union Regional Development Fund]. M.M. was supported by the National Science Centre (NCN)[2014/12/T/NZ2/00501]. J.M.B. and J.M.K. were also supported by the European Research Council [ERC, StG grant RNA + P = 123D to J.M.B.] and J.M.B. was supported by the “Ideas for Poland” fellowship from the FNP.


  1. 1.
    Doudna JA (2000) Structural genomics of RNA. Nat Struct Biol 7(Suppl):954–956CrossRefPubMedGoogle Scholar
  2. 2.
    Rother K et al (2011) RNA and protein 3D structure modeling: similarities and differences. J Mol Model 17(9):2325–2336CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Magnus M et al (2014) Computational modeling of RNA 3D structures, with the aid of experimental restraints. RNA Biol 11(5):522–536CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Rother M et al (2011) ModeRNA server: an online tool for modeling RNA 3D structures. Bioinformatics 27(17):2441–2442CrossRefPubMedGoogle Scholar
  5. 5.
    Rother M et al (2011) ModeRNA: a tool for comparative modeling of RNA 3D structure. Nucleic Acids Res 39(10):4007–4022CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Tozzini V (2009) Multiscale modeling of proteins. Acc Chem Res 43(2):220–230CrossRefGoogle Scholar
  7. 7.
    Kolinski A (2004) Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51(2):349–371PubMedGoogle Scholar
  8. 8.
    Boniecki M et al (2003) Protein fragment reconstruction using various modeling techniques. J Comput Aided Mol Des 17(11):725–738CrossRefPubMedGoogle Scholar
  9. 9.
    Kolinski A, Bujnicki JM (2005) Generalized protein structure prediction based on combination of fold-recognition with de novo folding and evaluation of models. Proteins 61(Suppl 7):84–90CrossRefPubMedGoogle Scholar
  10. 10.
    Magnus M, Boniecki MJ, Dawson W, Bujnicki JM (2016) SimRNAweb: a web server for RNA 3D structure modeling with optional restraints. Nucleic Acids Res 44(W1):W315–319. doi: 10.1093/nar/gkw279
  11. 11.
    Altschul SF et al (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410CrossRefPubMedGoogle Scholar
  12. 12.
    Nawrocki EP et al (2015) Rfam 12.0: updates to the RNA families database. Nucleic Acids Res 43(Database issue):D130–D137CrossRefPubMedGoogle Scholar
  13. 13.
    Nawrocki EP, Eddy SR (2013) Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29(22):2933–2935CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Zhou Y et al (2008) GISSD: group I intron sequence and structure database. Nucleic Acids Res 36(Database issue):D31–D37PubMedGoogle Scholar
  15. 15.
    Wilm A, Higgins DG, Notredame C (2008) R-Coffee: a method for multiple alignment of non-coding RNA. Nucleic Acids Res 36(9):e52CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Cruz JA et al (2012) RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction. RNA 14(4):610–625CrossRefGoogle Scholar
  17. 17.
    Chen VB et al (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr 66(Pt 1):12–21CrossRefPubMedGoogle Scholar
  18. 18.
    Hajdin CE et al (2010) On the significance of an RNA tertiary structure prediction. RNA 16(7):1340–1349CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Pawel Piatkowski
    • 1
  • Joanna M. Kasprzak
    • 1
    • 2
  • Deepak Kumar
    • 2
  • Marcin Magnus
    • 1
  • Grzegorz Chojnowski
    • 1
  • Janusz M. Bujnicki
    • 1
    • 2
  1. 1.Laboratory of Bioinformatics and Protein EngineeringInternational Institute of Molecular and Cell BiologyWarsawPoland
  2. 2.Laboratory of Bioinformatics, Institute of Molecular Biology and BiotechnologyAdam Mickiewicz UniversityPoznanPoland

Personalised recommendations