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A Method to Predict the 3D Structure of an RNA Scaffold

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1316)

Abstract

The ever increasing discoveries of noncoding RNA functions draw a strong demand for RNA structure determination from the sequence. In recently years, computational studies for RNA structures, at both the two-dimensional and the three-dimensional levels, led to several highly promising new developments. In this chapter, we describe a recently developed RNA structure prediction method based on the virtual bond-based coarse-grained folding model (Vfold). The main emphasis in the Vfold method is placed on the loop entropy calculations, the treatment of noncanonical (mismatch) interactions and the 3D structure assembly from motif-based template library. As case studies, we use the glycine riboswitch and the G310-U376 domain of MLV RNA to illustrate the Vfold-based prediction of RNA 3D structures from the sequences.

Key words

Partition function Loop entropy Mismatched stacks 2D structure motif Structure assembly 
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Notes

Acknowledgment

This research was supported by NIH grant GM063732 and NSF grant MCB0920411.

References

  1. 1.
    Doudna JA, Cech TR (2002) The chemical repertoire of natural ribozymes. Nature 418:222–228CrossRefPubMedGoogle Scholar
  2. 2.
    Bachellerie JP, Cavaille J, Huttenhofer A (2002) The expanding snoRNA world. Biochimie 84:774–790CrossRefGoogle Scholar
  3. 3.
    Gong C, Maquat LE (2011) lncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3 UTRs via Alu elements. Nature 470:284–288CrossRefPubMedCentralPubMedGoogle Scholar
  4. 4.
    Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233CrossRefPubMedCentralPubMedGoogle Scholar
  5. 5.
    Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284CrossRefPubMedGoogle Scholar
  6. 6.
    Gardner PP, Giegerich R (2004) A comprehensive comparison of comparative RNA structure prediction approaches. BMC Bioinformatics 5:140CrossRefPubMedCentralPubMedGoogle Scholar
  7. 7.
    Mathews DH, Moss WN, Turner DH (2010) Folding and finding RNA secondary structure. Cold Spring Harb Perspect Biol 2:a003665CrossRefPubMedCentralPubMedGoogle Scholar
  8. 8.
    Washietl S (2010) Sequence and structure analysis of noncoding RNAs. Methods Mol Biol 609:285–306CrossRefPubMedGoogle Scholar
  9. 9.
    Machado-Lima A, del Portillo HA, Durham AM (2008) Computational methods in noncoding RNA research. J Math Biol 56:15–49CrossRefPubMedGoogle Scholar
  10. 10.
    Mathews DH, Turner DH (2006) Prediction of RNA secondary structure by free energy minimization. Curr Opin Struct Biol 16:270–278CrossRefPubMedGoogle Scholar
  11. 11.
    Turner DH, Mathews DH (2010) NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure. Nucleic Acids Res 38:D280–D282CrossRefPubMedCentralPubMedGoogle Scholar
  12. 12.
    Cao S, Chen S-J (2005) Predicting RNA folding thermodynamics with a reduced chain representation model. RNA 11:1884–1897CrossRefPubMedCentralPubMedGoogle Scholar
  13. 13.
    Chen S-J (2008) RNA folding: conformational statistics, folding kinetics, and ion electrostatics. Annu Rev Biophys 37:197–214CrossRefPubMedCentralPubMedGoogle Scholar
  14. 14.
    Xu X, Zhao P, Chen S-J (2014) Vfold: a web server for RNA structure and folding thermodynamics prediction. PLoS ONE 9(9): e107504Google Scholar
  15. 15.
    Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415CrossRefPubMedCentralPubMedGoogle Scholar
  16. 16.
    Bellaousov S, Reuter JS, Seetin MG, Methews DH (2013) RNAstructure: web servers for RNA secondary structure prediction and analysis. Nucleic Acids Res 41:W471–W474CrossRefPubMedCentralPubMedGoogle Scholar
  17. 17.
    Xu X, Chen S-J (2012) Kinetic mechanism of conformational switch between bistable RNA hairpins. J Am Chem Soc 134:12499–12507CrossRefPubMedCentralPubMedGoogle Scholar
  18. 18.
    Cao S, Chen S-J (2012) Predicting kissing interactions in microRNA-target complex and assessment of microRNA activity. Nucleic Acids Res 40:4681–4690CrossRefPubMedCentralPubMedGoogle Scholar
  19. 19.
    Cao S, Chen S-J (2011) Structure and stability of RNA/RNA kissing complex: with application of HIV dimerization initiation signal. RNA 17:2130–2143CrossRefPubMedCentralPubMedGoogle Scholar
  20. 20.
    Cao S, Xu X, Chen S-J (2014) Predicting structure and stability for RNA complexes with intermolecular loop- loop base pairing. RNA 20: 835–845Google Scholar
  21. 21.
    Shapiro BA, Yingling YG, Kasprzak W, Bindewald E (2007) Bridging the gap in RNA structure prediction. Curr Opin Struct Biol 17:157–165CrossRefPubMedGoogle Scholar
  22. 22.
    Rother K, Rother M, Boniecki M, Puton T, Bujnicki JM (2011) RNA and protein 3D structure modeling: similarities and differences. J Mol Model 17:2325–2336CrossRefPubMedCentralPubMedGoogle Scholar
  23. 23.
    Laing C, Schlick T (2011) Computational approaches to RNA structure prediction, analysis, and design. Curr Opin Struct Biol 21:306–318CrossRefPubMedCentralPubMedGoogle Scholar
  24. 24.
    Sim AY, Minary P, Levitt M (2012) Modeling nucleic acids. Curr Opin Struct Biol 22:1–6CrossRefGoogle Scholar
  25. 25.
    Tan RK, Petrov AS, Harvey SC (2006) YUP: a molecular simulation program for coarse-grained and multi-scaled models. J Chem Theory Comput 2:529–540CrossRefPubMedCentralPubMedGoogle Scholar
  26. 26.
    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 15:189–199CrossRefPubMedCentralPubMedGoogle Scholar
  27. 27.
    Sharma S, Ding F, Dokholyan NV (2008) iFoldRNA: three-dimensional RNA structure prediction and folding. Bioinformatics 24:1951–1952CrossRefPubMedCentralPubMedGoogle Scholar
  28. 28.
    Xia Z, Bell DR, Shi Y, Ren P (2013) RNA 3D structure prediction by using a coarse-grained model and experimental data. J Phys Chem B 117:3135–3144CrossRefPubMedGoogle Scholar
  29. 29.
    Das R, Karanicolas J, Baker D (2010) Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Methods 7:291–294CrossRefPubMedCentralPubMedGoogle Scholar
  30. 30.
    Cao S, Chen S-J (2011) Physics-based de novo prediction of RNA 3D structures. J Phys Chem B 115:4216–4226CrossRefPubMedCentralPubMedGoogle Scholar
  31. 31.
    Parisien M, Major F (2008) The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452:51–55CrossRefPubMedGoogle Scholar
  32. 32.
    Cao S, Chen S-J (2006) Predicting RNA psuedoknot folding thermodynamics. Nucleic Acids Res 34:2634–2652CrossRefPubMedCentralPubMedGoogle Scholar
  33. 33.
    Cao S, Chen S-J (2009) Predicting structures and stabilities for H-type pseudoknots with inter-helix loop. RNA 15:696–706CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of PhysicsUniversity of MissouriColumbiaUSA
  2. 2.Department of BiochemistryUniversity of MissouriColumbiaUSA
  3. 3.Informatics InstituteUniversity of MissouriColumbiaUSA

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