Advertisement

Prediction of RNA Secondary Structure Including Kissing Hairpin Motifs

  • Corinna Theis
  • Stefan Janssen
  • Robert Giegerich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6293)

Abstract

We present three heuristic strategies for folding RNA sequences into secondary structures including kissing hairpin motifs. The new idea is to construct a kissing hairpin motif from an overlay of two simple canonical pseudoknots. The difficulty is that the overlay does not satisfy Bellman’s Principle of Optimality, and the kissing hairpin cannot simply be built from optimal pseudoknots. Our strategies have time/space complexities of O(n 4) / O(n 2), O(n 4) / O(n 3), and O(n 5) / O(n 2). All strategies have been implemented in the program pKiss and were evaluated against known structures. Surprisingly, our simplest strategy performs best. As it has the same complexity as the previous algorithm for simple pseudoknots, the overlay idea opens a way to construct a variety of practically useful algorithms for pseudoknots of higher topological complexity within O(n 4) time and O(n 2) space.

Keywords

Minimum Free Energy Nest Structure Human Coronavirus Pseudoknotted Structure Minimum Free Energy Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akutsu, T.: Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots. Discrete Appl. Math. 104(1-3), 45–62 (2000)CrossRefGoogle Scholar
  2. 2.
    Andronescu, M.S., Condon, A.E., Hoos, H.H., Mathews, D.H., Murphy, K.P.: Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics 23, 19–28 (2007)CrossRefGoogle Scholar
  3. 3.
    Andronescu, M.S., Pop, C., Condon, A.E.: Improved free energy parameters for RNA pseudoknotted secondary structure prediction. RNA 16(1), 26–42 (2010)CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Chan, C.Y., Lawrence, C.E., Ding, Y.: Structure clustering features on the Sfold Web server. Bioinformatics 21(20), 3926–3928 (2005)CrossRefPubMedGoogle Scholar
  5. 5.
    Chang, K.Y., Tinoco, I.: Characterization of a kissing hairpin complex derived from the human immunodeficiency virus genome. Proc. Natl. Acad. Sci. USA 91(18), 8705–8709 (1994)CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Chen, H.L., Condon, A.E., Jabbari, H.: An O(n 5) Algorithm for MFE Prediction of Kissing Hairpins and 4-Chains in Nucleic Acids. J. Comput. Biol. 16(6), 803–815 (2009)CrossRefPubMedGoogle Scholar
  7. 7.
    Condon, A.E., Jabbari, H.: Computational prediction of nucleic acid secondary structure: Methods, applications, and challenges. Theoretical Computer Science 410(4-5), 294–301 (2009)CrossRefGoogle Scholar
  8. 8.
    Deblasio, D., Bruand, J., Zhang, S.: PMFastR: A New Approach to Multiple RNA Structure Alignment. In: Salzberg, S.L., Warnow, T. (eds.) WABI 2009. LNCS, vol. 5724, pp. 49–61. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Frid, Y., Gusfield, D.: A simple, practical and complete O(n 3 / logn)-time Algorithm for RNA folding using the Four-Russians Speedup. Algorithms Mol. Biol. 5(1), 13 (2010)CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Giegerich, R.: Explaining and Controlling Ambiguity in Dynamic Programming. In: Giancarlo, R., Sankoff, D. (eds.) CPM 2000. LNCS, vol. 1848, pp. 46–59. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Giegerich, R., Hoener, C., Siederdissen, z.: Semantics and Ambiguity of Stochastic RNA Family Models. IEEE/ACM Transactions on Computational Biology and Bioinformatics 99(PrePrints) (2010)Google Scholar
  12. 12.
    Giegerich, R., Meyer, C., Steffen, P.: A discipline of dynamic programming over sequence data. Science of Computer Programming 51(3), 215–263 (2004)CrossRefGoogle Scholar
  13. 13.
    Herold, J., Siddell, S.G.: An ‘elaborated’ pseudoknot is required for high frequency frameshifting during translation of HCV 229E polymerase mRNA. Nucl. Acids Res. 21(25), 5838–5842 (1993)CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Hofacker, I.L., Fontana, W., Stadler, P.F., Bonhoeffer, S.L., Tacker, M., Schuster, P.: Fast Folding and Comparison of RNA Secondary Structures. Monatsh. Chem. 125, 167–188 (1994)CrossRefGoogle Scholar
  15. 15.
    Li, P.T.X., Bustamante, C., Tinoco, I.: Unusual mechanical stability of a minimal RNA kissing complex. Proc. Natl. Acad. Sci. USA 103(43), 15847–15852 (2006)CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Lyngsø, R.B., Pedersen, C.N.S.: RNA Pseudoknot Prediction in Energy-Based Models. J. Comput. Biol. 7(3-4), 409–427 (2000)CrossRefPubMedGoogle Scholar
  17. 17.
    Mathews, D.H., Disney, M.D., Childs, J.L., Schroeder, S.J., Zuker, M., Turner, D.H.: Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl. Acad. Sci. USA 101(19), 7287–7292 (2004)CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Mathews, D.H., Turner, D.H.: Prediction of RNA secondary structure by free energy minimization. Curr. Opin. Struct. Biol. 16(3), 270–278 (2006)CrossRefPubMedGoogle Scholar
  19. 19.
    Melchers, W.J.G., Hoenderop, J.G.J., Slot, H.J.B., Pleij, C.W.A., Pilipenko, E.V., Agol, V.I., Galama, J.M.D.: Kissing of the two predominant hairpin loops in the coxsackie B virus 3’ untranslated region is the essential structural feature of the origin of replication required for negative-strand RNA synthesis. J. Virol. 71(1), 686–696 (1997)PubMedPubMedCentralGoogle Scholar
  20. 20.
    Reeder, J., Giegerich, R.: Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics. BMC Bioinformatics 5(1), 104 (2004)CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Ren, J., Rastegari, B., Condon, A.E., Hoos, H.H.: HotKnots: Heuristic prediction of RNA secondary structures including pseudoknots. RNA 11(10), 1494–1504 (2005)CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Rivas, E., Eddy, S.R.: A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol. 285(5), 2053–2068 (1999)CrossRefPubMedGoogle Scholar
  23. 23.
    Rødland, E.A.: Pseudoknots in RNA Secondary Structures: Representation, Enumeration, and Prevalence. J. Comput. Biol. 13(6), 1197–1213 (2006)CrossRefPubMedGoogle Scholar
  24. 24.
    Tuerk, C., MacDougal, S., Gold, L.: RNA pseudoknots that inhibit HIV type 1 reverse transcriptase. Proc. Natl. Acad. Sci. USA 89(15), 6988–6992 (1992)CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    van Batenburg, F.H.D., Gultyaev, A.P., Pleij, C.W.A.: PseudoBase: structural information on RNA pseudoknots. Nucl. Acids Res. 29(1), 194–195 (2001)CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Wuchty, S., Fontana, W., Hofacker, I.L., Schuster, P.: Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers 49(2), 145–165 (1999)CrossRefPubMedGoogle Scholar
  27. 27.
    Zuker, M., Stiegler, P.: Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucl. Acids Res. 9(1), 133–148 (1981)CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Corinna Theis
    • 1
  • Stefan Janssen
    • 1
  • Robert Giegerich
    • 1
  1. 1.Faculty of TechnologyBielefeld UniversityBielefeldGermany

Personalised recommendations