Skip to main content
SpringerLink
Log in

RNA Secondary Structure Determination and Representation Based on Constraints Satisfaction

  • Published:
Constraints Aims and scope Submit manuscript

Abstract

A characteristic common to several problems of molecular biology consists in the satisfaction of a set of constraints coming from different sources of biological knowledge. In this paper, we present two problems that take advantage of a constraint satisfaction formulation. The first problem deals with the representation and visualization of RNA secondary structures. The program RNASEARCH implements an original backtracking based algorithm that evaluates at each node the satisfaction of spatial constraints with the aim at drawing a representation without overlap between secondary structural elements. The second problem addresses the determination of RNA secondary structure in accordance with data. With the program SAPSSARN, the application of classic filtering algorithm is used and we discuss a new search algorithm which computes only so called saturated secondary structures. The main result certainly is the possibility to relax the constraint of the absence of secondary structural elements forbidden in secondary structures computed with dynamic programming based approaches: pseudoknots. Finally, we show how each program takes advantage from the other through a protocol driven by constraints.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Altman, R. B., Weiser, B., & Noller, H. F. (1994). Constraint satisfaction techniques for modeling large complexes: application to the central domain of 16s ribosomal RNA. In Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, Stanford, CA: Stanford University, pages 10–18.

    Google Scholar 

  2. Antao, V. P., & Tinoco, I. (1992). Thermodynamic Parameters for Loop Formation in RNA and DNA Hairpin Tetraloops. Nucleic Acids Research, 20: 819–824.

    Google Scholar 

  3. Bessiere, C., Freuder, E. C., & Régin, J. C. (1995). Using inference to reduce arc-consistency computation. In Proceedings of the 14th IJCAI, Montréal, Canada.

  4. Billoud, B., Kontic, M., & Viari, A. (1996). Palingol: a declarative programming language to describe nucleic acids' secondary structures and to scan sequence databases. Nucleic Acids Research, 24(8): 1395–1403.

    Google Scholar 

  5. Bron, C., & Kerbosh, J. (1973). Algorithm 457: finding all cliques of an undirected graph. Communications of the ACM, 16(9): 575–577.

    Google Scholar 

  6. Bruccoleri, R. E., & Heinrich, G. (1988). An improved algorithm for nucleic acid secondary structure dispaly. Computer Applications in BioSciences, 4(1): 167–173.

    Google Scholar 

  7. Chetouani, F., Monestie, P., Thebault, P., Gaspin, C., & Michot, B. (1997). Essa: an integrated and interactive computer tool for analysing RNA secondary structure. Nucleic Acid Research, 25(17).

  8. Clark, D. A., Rawlings, C. J., & Doursenot, S. (1994). Genetic map construction with constraints. In Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, Stanford, CA: Stanford University, pages 78–86.

    Google Scholar 

  9. Dechter, R. J., & Pearl, J. (1987). Network-based heuristics for constraint-satisfaction problems. Artificial Intelligence, 34(1): 1–38.

    Google Scholar 

  10. Di, T., & Yee, C. N. (1994). A restriction mapping engine using constraint logic programming. In Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology. Stanford, CA: Stanford University, pages 112–120.

    Google Scholar 

  11. Freuder, E. C. (1982). A sufficient condition for backtrack-free search. Journal of the ACM, 29(1): 24–32.

    Google Scholar 

  12. Freuder, E. C. (1985). A sufficient condition for backtrack-bounded search. Journal of the ACM, 32(4): 755–766.

    Google Scholar 

  13. Freuder, E. C., & Wallace, R. J. (1992). Partial constraint satisfaction. Artificial Intelligence, 58: 21–70.

    Google Scholar 

  14. Gaspin, C., & Westhof, E. (1995). An interactive framework for RNA secondary structure prediction with a dynamical treatment of constraints. Journal of Molecular Biology, 254: 163–174.

    Google Scholar 

  15. Golumbic, M. C. (1992). Algorithm graph theory and perfect graphs. Computer Science and Applied Mathematics, Ed. Werner Rheinbolt, Academic Press, Inc., Harcourt Brace Jovanovich, Publishers.

  16. Gouy, M. (1987). Secondary structure prediction of RNA. Nucleic Acids and Protein Sequence Analysis, A Practical Approach, pages 259–284. M. J. Bishop and C. J. Rawlings, eds. (Oxford: IRL)

    Google Scholar 

  17. Haralick, R. M., & Elliot, G. L. (1980). Increasing tree search efficiently for constraint satisfaction problems. Artificial Intelligence, 14: 263–313.

    Google Scholar 

  18. Inglehart, J., & Nelson, P. C. (1994). On the limitations of automated restriction mapping. Computer Applications in BioSciences, 10: 249–261.

    Google Scholar 

  19. Jaeger, J. A., Turner, D. H., & Zuker, M. (1989). Improved predictions of secondary structures for RNA. Proceedings of the National Academy of Sciences USA, 86: 7706–7710.

    Google Scholar 

  20. James, B. D., Olsen, G. J., & Pace, N. R. (1989). Phylogenetic comparative analysis of RNA secondary structures. Methods in Enzymology, 180: 227–239.

    Google Scholar 

  21. Kiss-LaŚzlo, Z., Henry, Y., Bachellerie, J.-P., Caizergues-Ferrer, M., & Kiss, T. (1996). Site-specific ribose methylation of preribosomal RNA: a novel function for small nucleolar RNAs. Cell, 85: 1077–1088.

    Google Scholar 

  22. Letovsky, S., & Berlyn, M. B. (1992). CPROP: a rule-based program for constructing genetic maps. Genomics, 12: 435–446.

    Google Scholar 

  23. Mackworth, A. K. (1977). Consistency in networks of relations. Artificial Intelligence, 8: 99–118.

    Google Scholar 

  24. Major, F., Turcotte, M., Gautheret, D., Lapalme, G., Fillion, E., & Cedergren, R. (1991). The combination of symbolic and numerical computation for three-dimensional modeling of RNA. Science, 253: 1255–1260.

    Google Scholar 

  25. McCaskill, J. (1990). The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers, 29: 1105–1119.

    Google Scholar 

  26. Michel, F. & Westhof, E. (1990). Modelling of the three-dimensional architecture of group I catalytic introns based on comparative sequence analysis. Journal of Molecular Biology, 216: 585–610.

    Google Scholar 

  27. Michel, F., & Westhof, E. (1996). Visualizing the logic behind RNA self-assembly. Science, 273: 1676–1677.

    Google Scholar 

  28. Mittal, S., & Falkenhainer, B. (1990). Dynamic constraint satisfaction problems. In AAAI90, Proceedings of the Eighth National Conference on Artificial Intelligence.

  29. Muller, G., Gaspin, C., Etienne, A., & Westhof, E. (1993). Automatic display of RNA secondary structures. Computer Applications in BioSciences, 9(5): 551–561.

    Google Scholar 

  30. Nakaya, A., Taura, K., Yamamoto, K., & Yonezawa, A. (1996). Visualization of RNA secondary structures using highly parallel computers. Computer Applications in BioSciences, 12(3): 205–211.

    Google Scholar 

  31. Nussinov, R., & Jacobson, A. B. (1980). Fast algorithm for predicting the secondary structure of single stranded RNA. Proceedings of the National Academy of Sciences USA, 77: 6309–6313.

    Google Scholar 

  32. Nussinov, R., Pieczenick, G., Griggs, J. R., & Kleitman, D. J. (1978). Algorithms for loop matching. SIAM Journal of Applied Mathematics, 35: 68–82.

    Google Scholar 

  33. Perrochon-Dorisse, J., Chetouani, F., Aurel, S., Iscolo, N., & Michot, B. (1995). RNA_d2: a computer program for editing and display RNA secondary structures. Computer Applications in BioSciences, 11(1): 101–109.

    Google Scholar 

  34. Philippe, C., Bnard, L., Portier, C., Westhof, E., Ereshmann, B., & Ehresmann, C. (1995). Molecular dissection of the pseudoknot governing the translational regulation of Escherichia coli ribosomal protein S15. Nucleic Acids Research, 23: 18–28.

    Google Scholar 

  35. De Rijk, P., & De Wachter, R. (1997). RnaViz, a program for the visualisation of RNA secondary structure. Nucleic Acids Research, 25(22): 4679–4684.

    Google Scholar 

  36. Stefik, M. (1981). Planning with constraints (MOL GEN:part 1). Artificial Intelligence, 16: 111–170.

    Google Scholar 

  37. Tsang, E. (1993). Foundations of Constraint Satisfaction. San Diego: Academic Press Inc.

    Google Scholar 

  38. Turner, D. H., Sugimoto, N., & Freier, S. M. (1988). RNA structure prediction. Annual Review of Biophysics and Chemistry, 17: 167–192.

    Google Scholar 

  39. Walter, A. E., Turner, D. H., Kim, J., Lytlle, M. H., Muller, P., Mathews, D. H., & Zuker, M. (1994). Coaxial stacking of helixes enhances binding of oligoribonucleotides and improves predictions of RNA folding. Proceedings of the National Academy of Sciences USA, 91: 9218–9222.

    Google Scholar 

  40. Westhof, E., Romby, P., Ehresmann, C., & Ehresmann, B. (1990). Computer-aided structural biochemistry of ribonucleic acids. In D. L. Beveridge and R. Lavery, eds., Theoretical Biochemistry and Molecular Biophysics, pages 399–409. Adeninc Press, New York.

    Google Scholar 

  41. Yap, R. H. C. (1993). A constraint logic programming framework for constructing DNA restriction maps. Artificial Intelligence in Medicine, 5: 447–464.

    Google Scholar 

  42. Zimmerman, D. E., Kulikowski, C. A., & Montelione, G. T. (1993). A constraint reasoning system for automating sequence-specific resonance assignments from multidimensional protein NMR spectra. In Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology. Bethesda, MD: National Library of Medicine, pages 447–455.

    Google Scholar 

  43. Zuker, M. (1989). On finding all suboptimal foldings of a RNA molecule. Science, 244: 48–52.

    Google Scholar 

  44. Zuker, M., & Stiegler, P. (1981). Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Research, 9: 133–148.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biometrics and Artificial Intelligence Department, National Institute of Agronomical Research, Chemin de Borde-Rouge, Auzeville BP 27, 31326, Castanet-Tolosan, France

    Christine Gaspin

Authors
  1. Christine Gaspin
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gaspin, C. RNA Secondary Structure Determination and Representation Based on Constraints Satisfaction. Constraints 6, 201–221 (2001). https://doi.org/10.1023/A:1011433605905

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1011433605905

Search

Navigation