Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data

Abstract

The classical RNA secondary structure model considers A·U and G·C Watson–Crick as well as G·U wobble base pairs. Here we substitute it for a new one, in which sets of nucleotide cyclic motifs define RNA structures. This model allows us to unify all base pairing energetic contributions in an effective scoring function to tackle the problem of RNA folding. We show how pipelining two computer algorithms based on nucleotide cyclic motifs, MC-Fold and MC-Sym, reproduces a series of experimentally determined RNA three-dimensional structures from the sequence. This demonstrates how crucial the consideration of all base-pairing interactions is in filling the gap between sequence and structure. We use the pipeline to define rules of precursor microRNA folding in double helices, despite the presence of a number of presumed mismatches and bulges, and to propose a new model of the human immunodeficiency virus-1 -1 frame-shifting element.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: A selection of 3D structures predicted from sequence.
Figure 2: A selection of pre-miRNA 3D structures.
Figure 3: HIV-1 -1 frame-shifting-element models.

References

  1. The RNA World 3rd edn (eds Gesteland, R. F., Cech, T. R. & Atkins, J. F.) (CSHL, Cold Spring Harbor, 2006)

  2. Griffiths-Jones, S. et al. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 33, D121–D124 (2005)

    Article  CAS  Google Scholar 

  3. Kapranov, P. et al. RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316, 1484–1488 (2007)

    Article  ADS  CAS  Google Scholar 

  4. Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000)

    Article  ADS  CAS  Google Scholar 

  5. Benson, D. A. et al. GenBank. Nucleic Acids Res. 35, D21–D25 (2007)

    Article  CAS  Google Scholar 

  6. Shapiro, B. A. et al. Bridging the gap in RNA structure prediction. Curr. Opin. Struct. Biol. 17, 157–165 (2007)

    Article  CAS  Google Scholar 

  7. Mathews, D. H. & Turner, D. H. Prediction of RNA secondary structure by free energy minimization. Curr. Opin. Struct. Biol. 16, 270–278 (2006)

    Article  CAS  Google Scholar 

  8. Gutell, R. R., Lee, J. C. & Cannone, J. J. The accuracy of ribosomal RNA comparative structure models. Curr. Opin. Struct. Biol. 12, 301–310 (2002)

    Article  CAS  Google Scholar 

  9. Mathews, D. H. Revolutions in RNA secondary structure prediction. J. Mol. Biol. 359, 526–532 (2006)

    Article  CAS  Google Scholar 

  10. Mathews, D. H. et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl Acad. Sci. USA 101, 7287–7292 (2004)

    Article  ADS  CAS  Google Scholar 

  11. Major, F. et al. The combination of symbolic and numerical computation for three-dimensional modeling of RNA. Science 253, 1255–1260 (1991)

    Article  CAS  Google Scholar 

  12. Lescoute, A. et al. Recurrent structural RNA motifs, isostericity matrices and sequence alignments. Nucleic Acids Res. 33, 2395–2409 (2005)

    Article  CAS  Google Scholar 

  13. Dima, R. I., Hyeon, C. & Thirumalai, D. Extracting stacking interaction parameters for RNA from the data set of native structures. J. Mol. Biol. 347, 53–69 (2005)

    Article  CAS  Google Scholar 

  14. Do, C. B., Woods, D. A. & Batzoglou, S. CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 22, e90–e98 (2006)

    Article  CAS  Google Scholar 

  15. Das, R. & Baker, D. Automated de novo prediction of native-like RNA tertiary structures. Proc. Natl Acad. Sci. USA (2007)

  16. Lemieux, S. & Major, F. Automated extraction and classification of RNA tertiary structure cyclic motifs. Nucleic Acids Res. 34, 2340–2346 (2006)

    Article  CAS  Google Scholar 

  17. Kabsch, H. A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallogr. A 34, 827–828 (1978)

    Article  ADS  Google Scholar 

  18. Williamson, J. R. Induced fit in RNA-protein recognition. Nature Struct. Biol. 7, 834–837 (2000)

    Article  CAS  Google Scholar 

  19. Shankar, N. et al. The NMR structure of an internal loop from 23S ribosomal RNA differs from its structure in crystals of 50s ribosomal subunits. Biochemistry 45, 11776–11789 (2006)

    Article  CAS  Google Scholar 

  20. Kondo, J., Urzhumtsev, A. & Westhof, E. Two conformational states in the crystal structure of the Homo sapiens cytoplasmic ribosomal decoding A site. Nucleic Acids Res. 34, 676–685 (2006)

    Article  CAS  Google Scholar 

  21. Pley, H. W., Flaherty, K. M. & Mckay, D. B. Three-dimensional structure of a hammerhead ribozyme. Nature 372, 68–74 (1994)

    Article  ADS  CAS  Google Scholar 

  22. Lee, B. M. et al. Induced fit and “lock and key” recognition of 5S RNA by zinc fingers of transcription factor IIIA. J. Mol. Biol. 357, 275–291 (2006)

    Article  CAS  Google Scholar 

  23. Giedroc, D. P., Theimer, C. A. & Nixon, P. L. Structure, stability and function of RNA pseudoknots involved in stimulating ribosomal frameshifting. J. Mol. Biol. 298, 167–185 (2000)

    Article  CAS  Google Scholar 

  24. Griffiths-Jones, S. et al. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34, D140–D144 (2006)

    Article  CAS  Google Scholar 

  25. Han, J. et al. Molecular basis for the recognition of primary microRNAs by the Drosha–DGCR8 complex. Cell 125, 887–901 (2006)

    Article  CAS  Google Scholar 

  26. Macrae, I. J. et al. Structural basis for double-stranded RNA processing by Dicer. Science 311, 195–198 (2006)

    Article  ADS  CAS  Google Scholar 

  27. Leontis, N. B., Stombaugh, J. & Westhof, E. The non-Watson–Crick base pairs and their associated isostericity matrices. Nucleic Acids Res. 30, 3497–3531 (2002)

    Article  CAS  Google Scholar 

  28. Merino, E. J. et al. RNA structure analysis at single nucleotide resolution by selective 2'-hydroxyl acylation and primer extension (SHAPE). J. Am. Chem. Soc. 127, 4223–4231 (2005)

    Article  CAS  Google Scholar 

  29. Perret, V. et al. Conformation in solution of yeast tRNAAsp transcripts deprived of modified nucleotides. Biochimie 72, 735–743 (1990)

    Article  CAS  Google Scholar 

  30. Brunel, C. et al. Three-dimensional model of Escherichia coli ribosomal 5S RNA as deduced from structure probing in solution and computer modeling. J. Mol. Biol. 221, 293–308 (1991)

    Article  CAS  Google Scholar 

  31. Leontis, N. B. & Moore, P. B. NMR evidence for dynamic secondary structure in helices II and III of the RNA of Escherichia coli. Biochemistry 25, 3916–3925 (1986)

    Article  CAS  Google Scholar 

  32. Hentze, M. W. & Kuhn, L. C. Molecular control of vertebrate iron metabolism: mRNA-based regulatory circuits operated by iron, nitric oxide, and oxidative stress. Proc. Natl Acad. Sci. USA 93, 8175–8182 (1996)

    Article  ADS  CAS  Google Scholar 

  33. Jaffrey, S. R. et al. The interaction between the iron-responsive element binding protein and its cognate RNA is highly dependent upon both RNA sequence and structure. Nucleic Acids Res. 21, 4627–4631 (1993)

    Article  CAS  Google Scholar 

  34. Sierzputowska-Gracz, H., Mckenzie, R. A. & Theil, E. C. The importance of a single G in the hairpin loop of the iron responsive element (IRE) in ferritin mRNA for structure: an NMR spectroscopy study. Nucleic Acids Res. 23, 146–153 (1995)

    Article  CAS  Google Scholar 

  35. Griffiths-Jones, S. et al. Rfam: an RNA family database. Nucleic Acids Res. 31, 439–441 (2003)

    Article  CAS  Google Scholar 

  36. Leipuviene, R. & Theil, E. C. The family of iron responsive RNA structures regulated by changes in cellular iron and oxygen. Cell. Mol. Life Sci. (in the press)

  37. Clery, A. et al. An improved definition of the RNA-binding specificity of SECIS-binding protein 2, an essential component of the selenocysteine incorporation machinery. Nucleic Acids Res. 35, 1868–1884 (2007)

    Article  ADS  CAS  Google Scholar 

  38. Jacks, T. et al. Characterization of ribosomal frameshifting in HIV-1 gag-pol expression. Nature 331, 280–283 (1988)

    Article  ADS  CAS  Google Scholar 

  39. Gaudin, C. et al. Structure of the RNA signal essential for translational frameshifting in HIV-1. J. Mol. Biol. 349, 1024–1035 (2005)

    Article  CAS  Google Scholar 

  40. Staple, D. W. & Butcher, S. E. Solution structure and thermodynamic investigation of the HIV-1 frameshift inducing element. J. Mol. Biol. 349, 1011–1023 (2005)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank P. Thibault for updating MC-Sym and P. Gendron for helping us with the Condor and web services. We thank D. D’Amours, M.-F. Gaumont-Leclerc and V. Lisi for making suggestions to improve the manuscript. We thank D. H. Mathews and E. Westhof for discussions about MC-Fold. This project was supported by grants from the Canadian Institutes of Health Research (CIHR) and from the Natural Sciences and Engineering Research Council (NSERC) of Canada. M.P. holds Ph.D. scholarships from the NSERC and the Fonds Québécois de la Recherche sur la Nature et les Technologies. F.M. is a member of the Centre Robert-Cedergren of the Université de Montréal.

Author Contributions Both authors were involved in every aspect of the research. M.P. programmed MC-Fold and MC-Cons.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to François Major.

Supplementary information

Supplementary Information

The file contains Supplementary Methods, Supplementary Discussion, Supplementary Tables S1-S3, Supplementary Figures S1-S15 with Legends and additional references. (PDF 1907 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Parisien, M., Major, F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452, 51–55 (2008). https://doi.org/10.1038/nature06684

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature06684

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing