Skip to main content
View expanded cover

Annual International Conference on Research in Computational Molecular Biology

RECOMB 2007: Research in Computational Molecular Biology pp 268–282Cite as

Tools for Simulating and Analyzing RNA Folding Kinetics

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNBI,volume 4453)

Abstract

It has recently been found that some RNA functions are determined by the actual folding kinetics and not just the RNA’s nucleotide sequence or its native structure. We present new computational tools for simulating and analyzing RNA folding kinetic metrics such as population kinetics, folding rates, and the folding of particular subsequences. Our method first builds an approximate representation (called a map) of the RNA’s folding energy landscape, and then uses specialized analysis techniques to extract folding kinetics from the map. We provide a new sampling strategy called Probabilistic Boltzmann Sampling (PBS) that enables us to approximate the folding landscape with much smaller maps, typically by several orders of magnitude. We also describe a new analysis technique, Map-based Monte Carlo (MMC) simulation, to stochastically extract folding pathways from the map. We demonstrate that our technique can be applied to large RNA (e.g., 200+ nucleotides), where representing the full landscape is infeasible, and that our tools provide results comparable to other simulation methods that work on complete energy landscapes. We present results showing that our approach computes the same relative functional rates as seen in experiments for the relative plasmid replication rates of ColE1 RNAII and its mutants, and for the relative gene expression rates of MS2 phage RNA and its mutants.

Keywords

  • Master Equation
  • Energy Landscape
  • Folding Process
  • Folding Pathway
  • Folding Rate

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.

Supported in part by NSF Grants EIA-0103742, ACR-0081510, ACR-0113971, CCR-0113974, ACI-0326350, by the DOE, and by HP. Thomas supported in part by a Dept. of Education GAANN Fellowship, a NSF Graduate Research Fellowship, and a P.E.O. Scholarship. Tapia supported in part by a NIH Molecular Biophysics Training Grant (T32GM065088) and a Dept. of Education GAANN Fellowship.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-71681-5_19
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-71681-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.00
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amato, N.M., Dill, K.A., Song, G.: Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures. J. Comput. Biol (Special issue of Int. Conf. Comput. Molecular Biology (RECOMB)) 10(3-4), 239–256 (2002)

    Google Scholar 

  2. Bartel, D.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004)

    CrossRef  Google Scholar 

  3. Chen, S.-J., Dill, K.A.: RNA folding energy landscapes. Proc. Natl. Acad. Sci. USA 97, 646–651 (2000)

    CrossRef  Google Scholar 

  4. Dill, K.A., Chan, H.S.: From Levinthal to pathways to funnels: The new view of protein folding kinetics. Nat. Struct. Biol. 4, 10–19 (1997)

    CrossRef  Google Scholar 

  5. Ding, Y., Lawrence, C.E.: A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Research 31, 7280–7301 (2003)

    CrossRef  Google Scholar 

  6. Dirks, R., Pierce, N.: A partition function algorithm for nucleic acid secondary structure including pseudoknots. Journal of Computational Chemistry 24, 1664–1677 (2003)

    CrossRef  Google Scholar 

  7. Flamm, C.: Kinetic Folding of RNA. PhD thesis, University of Vienna, Austria (August 1998)

    Google Scholar 

  8. Groeneveld, H., Thimon, K., van Duin, J.: Translational control of matruation-protein synthesis is phage MS2: a role of the kinetics of RNA folding? RNA 1, 79–88 (1995)

    Google Scholar 

  9. Gultyaev, A., Batenburg, F.V., Pleij, C.: The computer simulation of RNA folding pathways using a genetic algorithm. J. Mol. Biol. 250, 37–51 (1995)

    CrossRef  Google Scholar 

  10. Higgs, P.G.: RNA secondary structure: physical and computational aspects. Quarterly Reviews of Biophysics 33, 199–253 (2000)

    CrossRef  Google Scholar 

  11. Hofacker, I.L.: RNA secondary structures: A tractable model of biopolymer folding. J. Theor. Biol. 212, 35–46 (1998)

    Google Scholar 

  12. Kampen, N.G.V.: Stochastic Processes in Physics and Chemistry. North-Holland, New York (1992)

    Google Scholar 

  13. Kavraki, L.E., Svestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Automat. 12(4), 566–580 (1996)

    CrossRef  Google Scholar 

  14. Klaff, P., Riesner, D., Steger, G.: RNA structure and the regulation of gene expression. Plant Mol. Biol. 32, 89–106 (1996)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  16. Nagel, J.H., Pleij, C.W.: Self-induced structural switches in RNA. Biochimie 84, 913–923 (2002)

    CrossRef  Google Scholar 

  17. Newman, M.E.J., Barkenma, G.T.: Monte Carlo Methods in Statistical Physics. Clarendon Press, Oxford (1999)

    MATH  Google Scholar 

  18. Nussinov, R., Piecznik, G., Griggs, J.R., Kleitman, D.J.: Algorithms for loop matching. SIAM J. Appl. Math. 35, 68–82 (1972)

    CrossRef  Google Scholar 

  19. Ozkan, S.B., Dill, K.A., Bahar, I.: Computing the transition state population in simple protein models. Biopolymers 68, 35–46 (2003)

    CrossRef  Google Scholar 

  20. Rivas, E., Eddy, S.: A dynamic programming algorithm for rna structure prediction including pseduoknots. JMB 285, 2053–2068 (2000)

    CrossRef  Google Scholar 

  21. Shapiro, B.A., Bengali, D., Kasprzak, W., Wu, J.C.: RNA folding pathway functional intermediates: Their prediction and analysis. J. Mol. Biol. 312, 27–44 (2001)

    CrossRef  Google Scholar 

  22. Song, G., Thomas, S., Dill, K., Scholtz, J., Amato, N.: A path planning-based study of protein folding with a case study of hairpin formation in protein G and L. In: Proc. Pacific Symposium of Biocomputing (PSB), pp. 240–251 (2003)

    Google Scholar 

  23. Tang, X., Kirkpatrick, B., Thomas, S., Song, G., Amato, N.M.: Using motion planning to study RNA folding kinetics. J. Comput. Biol. 12(6), 862–881 (2004)

    CrossRef  Google Scholar 

  24. Thomas, S., Tang, X., Tapia, L., Amato, N.M.: Simulating protein motions with rigidity analysis. In: Proc. Int. Conf. Comput. Molecular Biology (RECOMB), pp. 394–409 (2006)

    Google Scholar 

  25. Tinoco, I., Bustamante, C.: How RNA folds. J. Mol. Biol. 293, 271–281 (1999)

    CrossRef  Google Scholar 

  26. Wolfinger, M.: The energy landscape of RNA folding. Master’s thesis, University of Vienna, Austria (March 2001)

    Google Scholar 

  27. Wuchty, S.: Suboptimal secondary structures of RNA. Master’s thesis, University of Vienna, Austria (March 1998)

    Google Scholar 

  28. Xayaphoummine, A., Bucher, T., Thalmann, F., Isambert, H.: Prediction and statistics of pseudoknots in RNA structures using exactly clustered stochastic simulations. Proc. Natl. Acad. Sci. USA 100, 15310–15315 (2003)

    MATH  CrossRef  MathSciNet  Google Scholar 

  29. Zuker, M., Mathews, D.H., Turner, D.H.: Algorithms and thermodynamics for RNA secondary structure prediction: A practical guide. In: Barciszewski, J., Clark, B.F.C. (eds.) RNA Biochemistry and Biotechnology. NATO ASI Series, Kluwer Academic Publishers, Dordrecht (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Parasol Lab, Dept. of Comp. Sci., Texas A&M University, College Station, TX 77843,  

    Xinyu Tang, Shawna Thomas, Lydia Tapia & Nancy M. Amato

Authors
  1. Xinyu Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shawna Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lydia Tapia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nancy M. Amato
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

    Rights and permissions

    Reprints and Permissions

    Copyright information

    © 2007 Springer Berlin Heidelberg

    About this paper

    Cite this paper

    Tang, X., Thomas, S., Tapia, L., Amato, N.M. (2007). Tools for Simulating and Analyzing RNA Folding Kinetics. In: Speed, T., Huang, H. (eds) Research in Computational Molecular Biology. RECOMB 2007. Lecture Notes in Computer Science(), vol 4453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71681-5_19

    Download citation

    • DOI: https://doi.org/10.1007/978-3-540-71681-5_19

    • Publisher Name: Springer, Berlin, Heidelberg

    • Print ISBN: 978-3-540-71680-8

    • Online ISBN: 978-3-540-71681-5

    • eBook Packages: Computer ScienceComputer Science (R0)