Automated RNA 3D Structure Prediction with RNAComposer

  • Marcin Biesiada
  • Katarzyna J. Purzycka
  • Marta Szachniuk
  • Jacek Blazewicz
  • Ryszard W. Adamiak
Part of the Methods in Molecular Biology book series (MIMB, volume 1490)


RNAs adopt specific structures to perform their activities and these are critical to virtually all RNA-mediated processes. Because of difficulties in experimentally assessing structures of large RNAs using NMR, X-ray crystallography, or cryo-microscopy, there is currently great demand for new high-resolution 3D structure prediction methods. Recently we reported on RNAComposer, a knowledge-based method for the fully automated RNA 3D structure prediction from a user-defined secondary structure. RNAComposer method is especially suited for structural biology users. Since our initial report in 2012, both servers, freely available at and have been often visited. Therefore this chapter provides guidance for using RNAComposer and discusses points that should be considered when predicting 3D RNA structure. An application example presents current scope and limitations of RNAComposer.

Key words

RNA tertiary structure RNA three-dimensional structure RNA modeling 



This work was supported by the National Science Center Poland [MAESTRO 2012/06/A/ST6/00384 (to R.W.A)] and Ministry of Science and Higher Education [0492/IP1/2013/72 (to K.J.P.)].


  1. 1.
    Spitale RC, Flynn RA, Torre EA, Kool ET, Chang HY (2014) RNA structural analysis by evolving SHAPE chemistry. Wiley Interdiscip Rev RNA 5(6):867–881. doi: 10.1002/wrna.1253 CrossRefPubMedGoogle Scholar
  2. 2.
    Tian S, Cordero P, Kladwang W, Das R (2014) High-throughput mutate-map-rescue evaluates SHAPE-directed RNA structure and uncovers excited states. RNA 20(11):1815–1826. doi: 10.1261/rna.044321.114 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Ding F, Sharma S, Chalasani P, Demidov VV, Broude NE, Dokholyan NV (2008) Ab initio RNA folding by discrete molecular dynamics: from structure prediction to folding mechanisms. RNA 14(6):1164–1173. doi: 10.1261/rna.894608 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Jonikas MA, Radmer RJ, Laederach A, Das R, Pearlman S, Herschlag D, Altman RB (2009) Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. RNA 15(2):189–199. doi: 10.1261/rna.1270809 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Rother M, Rother K, Puton T, Bujnicki JM (2011) ModeRNA: a tool for comparative modeling of RNA 3D structure. Nucleic Acids Res 39(10):4007–4022. doi: 10.1093/nar/gkq1320 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Das R, Baker D (2007) Automated de novo prediction of native-like RNA tertiary structures. Proc Natl Acad Sci U S A 104(37):14664–14669. doi: 10.1073/pnas.0703836104 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Parisien M, Major F (2008) The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452(7183):51–55. doi: 10.1038/nature06684 CrossRefPubMedGoogle Scholar
  8. 8.
    Cao S, Chen SJ (2011) Physics-based de novo prediction of RNA 3D structures. J Phys Chem B 115(14):4216–4226. doi: 10.1021/jp112059y CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Sharma S, Ding F, Dokholyan NV (2008) iFoldRNA: three-dimensional RNA structure prediction and folding. Bioinformatics 24(17):1951–1952. doi: 10.1093/bioinformatics/btn328 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Zhao Y, Huang Y, Gong Z, Wang Y, Man J, Xiao Y (2012) Automated and fast building of three-dimensional RNA structures. Sci Rep 2:734. doi: 10.1038/srep00734 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Popenda M, Szachniuk M, Antczak M, Purzycka KJ, Lukasiak P, Bartol N, Blazewicz J, Adamiak RW (2012) Automated 3D structure composition for large RNAs. Nucleic Acids Res 40(14):e112. doi: 10.1093/nar/gks339 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Popenda M, Blazewicz M, Szachniuk M, Adamiak RW (2008) RNA FRABASE version 1.0: an engine with a database to search for the three-dimensional fragments within RNA structures. Nucleic Acids Res 36(Database issue):D386–D391. doi: 10.1093/nar/gkm786 PubMedGoogle Scholar
  14. 14.
    Popenda M, Szachniuk M, Blazewicz M, Wasik S, Burke EK, Blazewicz J, Adamiak RW (2010) RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures. BMC Bioinformatics 11:231. doi: 10.1186/1471-2105-11-231 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Gan HH, Pasquali S, Schlick T (2003) Exploring the repertoire of RNA secondary motifs using graph theory; implications for RNA design. Nucleic Acids Res 31(11):2926–2943CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Schwieters CD, Kuszewski JJ, Tjandra N, Clore GM (2003) The Xplor-NIH NMR molecular structure determination package. J Magn Reson 160(1):65–73CrossRefPubMedGoogle Scholar
  17. 17.
    Herraez A (2006) Biomolecules in the computer: Jmol to the rescue. Biochem Mol Biol Educ 34(4):255–261. doi: 10.1002/bmb.2006.494034042644 CrossRefPubMedGoogle Scholar
  18. 18.
    Childs-Disney JL, Yildirim I, Park H, Lohman JR, Guan L, Tran T, Sarkar P, Schatz GC, Disney MD (2014) Structure of the myotonic dystrophy type 2 RNA and designed small molecules that reduce toxicity. ACS Chem Biol 9(2):538–550. doi: 10.1021/cb4007387 CrossRefPubMedGoogle Scholar
  19. 19.
    Mathews DH (2014) RNA secondary structure analysis using RNAstructure. Curr Protoc Bioinformatics 46:12.16.11–12.16.25. doi: 10.1002/0471250953.bi1206s46 Google Scholar
  20. 20.
    Do CB, Woods DA, Batzoglou S (2006) CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 22(14):e90–e98. doi: 10.1093/bioinformatics/btl246 CrossRefPubMedGoogle Scholar
  21. 21.
    Hofacker IL, Fontana W, Stadler PF, Bonhoeffer LS, Tacker M, Schuster P (1994) Fast folding and comparison of RNA secondary structures. Monatshefte Fur Chem 125(2):167–188. doi: 10.1007/Bf00818163 CrossRefGoogle Scholar
  22. 22.
    Sergiev PV, Dontsova OA, Bogdanov AA (2001) Chemical methods for the structural study of the ribosome: judgment day. Mol Biol 35(4):472–495. doi: 10.1023/A:1010506522897 CrossRefGoogle Scholar
  23. 23.
    Furtig B, Richter C, Wohnert J, Schwalbe H (2003) NMR spectroscopy of RNA. Chembiochem 4(10):936–962. doi: 10.1002/cbic.200300700 CrossRefPubMedGoogle Scholar
  24. 24.
    Wozniak AK, Nottrott S, Kuhn-Holsken E, Schroder GF, Grubmuller H, Luhrmann R, Seidel CA, Oesterhelt F (2005) Detecting protein-induced folding of the U4 snRNA kink-turn by single-molecule multiparameter FRET measurements. RNA 11(10):1545–1554. doi: 10.1261/rna.2950605 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Frolow O, Endeward B, Schiemann O, Prisner TF, Engels JW (2008) Nitroxide spin labeled RNA for long range distance measurements by EPR-PELDOR. Nucleic Acids Symp Ser (Oxf) 52:153–154. doi: 10.1093/nass/nrn078 CrossRefGoogle Scholar
  26. 26.
    Huang LL, Serganov A, Patel DJ (2010) Structural insights into ligand recognition by a sensing domain of the cooperative glycine riboswitch. Mol Cell 40(5):774–786. doi: 10.1016/j.molcel.2010.11.026 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Grundy FJ, Lehman SC, Henkin TM (2003) The L box regulon: lysine sensing by leader RNAs of bacterial lysine biosynthesis genes. Proc Natl Acad Sci U S A 100(21):12057–12062. doi: 10.1073/pnas.2133705100 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Serganov A, Huang L, Patel DJ (2008) Structural insights into amino acid binding and gene control by a lysine riboswitch. Nature 455(7217):1263–1267. doi: 10.1038/nature07326 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Blouin S, Lafontaine DA (2007) A loop-loop interaction and a K-turn motif located in the lysine aptamer domain are important for the riboswitch gene regulation control. RNA 13(8):1256–1267. doi: 10.1261/Rna.560307 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Xayaphoummine A, Bucher T, Isambert H (2005) Kinefold web server for RNA/DNA folding path and structure prediction including pseudoknots and knots. Nucleic Acids Res 33(Web Server issue):W605–W610. doi: 10.1093/nar/gki447 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405(2):442–451CrossRefPubMedGoogle Scholar
  32. 32.
    Spitale RC, Crisalli P, Flynn RA, Torre EA, Kool ET, Chang HY (2013) RNA SHAPE analysis in living cells. Nat Chem Biol 9(1):18–20. doi: 10.1038/nchembio.1131 CrossRefPubMedGoogle Scholar
  33. 33.
    Purzycka KJ, Pachulska-Wieczorek K, Adamiak RW (2011) The in vitro loose dimer structure and rearrangements of the HIV-2 leader RNA. Nucleic Acids Res 39(16):7234–7248. doi: 10.1093/nar/gkr385 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Legiewicz M, Zolotukhin AS, Pilkington GR, Purzycka KJ, Mitchell M, Uranishi H, Bear J, Pavlakis GN, Le Grice SF, Felber BK (2010) The RNA transport element of the murine musD retrotransposon requires long-range intramolecular interactions for function. J Biol Chem 285(53):42097–42104. doi: 10.1074/jbc.M110.182840 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Purzycka KJ, Legiewicz M, Matsuda E, Eizentstat LD, Lusvarghi S, Saha A, Le Grice SF, Garfinkel DJ (2013) Exploring Ty1 retrotransposon RNA structure within virus-like particles. Nucleic Acids Res 41(1):463–473. doi: 10.1093/nar/gks983 CrossRefPubMedGoogle Scholar
  36. 36.
    Huang Q, Purzycka KJ, Lusvarghi S, Li D, Legrice SF, Boeke JD (2013) Retrotransposon Ty1 RNA contains a 5′-terminal long-range pseudoknot required for efficient reverse transcription. RNA 19(3):320–332. doi: 10.1261/rna.035535.112 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Lusvarghi S, Sztuba-Solinska J, Purzycka KJ, Pauly GT, Rausch JW, Grice SF (2013) The HIV-2 Rev-response element: determining secondary structure and defining folding intermediates. Nucleic Acids Res 41(13):6637–6649. doi: 10.1093/nar/gkt353 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Krahenbuhl B, Lukavsky P, Wider G (2014) Strategy for automated NMR resonance assignment of RNA: application to 48-nucleotide K10. J Biomol NMR 59(4):231–240. doi: 10.1007/s10858-014-9841-3 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Marcin Biesiada
    • 1
    • 2
  • Katarzyna J. Purzycka
    • 2
  • Marta Szachniuk
    • 1
    • 3
  • Jacek Blazewicz
    • 1
    • 3
  • Ryszard W. Adamiak
    • 1
    • 2
  1. 1.European Center for Bioinformatics and Genomics, Institute of Computing SciencePoznan University of TechnologyPoznanPoland
  2. 2.Department of Structural Chemistry and Biology of Nucleic Acids, Institute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland
  3. 3.Department of Bioinformatics, Institute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland

Personalised recommendations