Skip to main content
SpringerLink
Log in

Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology

  • Protocol
  • First Online:
RNA Nanostructures

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2709))

Abstract

Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Jasinski D, Haque F, Binzel DW, Guo P (2017) Advancement of the emerging field of RNA nanotechnology. ACS Nano 11:1142–1164

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Guo P (2010) The emerging field of RNA nanotechnology. Nat Nanotechnol 5:833

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Chworos A, Severcan I, Koyfman AY et al (2004) Building programmable jigsaw puzzles with RNA. Science (80-) 306:2068–2072

    Article  CAS  Google Scholar 

  4. Guo P, Zhang C, Chen C et al (1998) Inter-RNA interaction of phage φ29 pRNA to form a hexameric complex for viral DNA transportation. Mol Cell 2:149–155

    Article  CAS  PubMed  Google Scholar 

  5. Sugimoto N, Nakano S, Katoh M et al (1995) Thermodynamic parameters to predict stability of RNA/DNA hybrid duplexes. Biochemistry 34:11211–11216

    Article  CAS  PubMed  Google Scholar 

  6. Searle MS, Williams DH (1993) On the stability of nucleic acid structures in solution: enthalpy-entropy compensations, internal rotations and reversibility. Nucleic Acids Res 21:2051–2056

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jaeger L, Leontis NB (2000) Tecto-RNA: one-dimensional self-assembly through tertiary interactions. Angew Chem Int Ed 39:2521–2524

    Article  CAS  Google Scholar 

  8. Shu D, Moll W-D, Deng Z et al (2004) Bottom-up assembly of RNA arrays and superstructures as potential parts in nanotechnology. Nano Lett 4:1717–1723

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ikawa Y, Tsuda K, Matsumura S, Inoue T (2004) De novo synthesis and development of an RNA enzyme. Proc Natl Acad Sci 101:13750–13755

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Matsumura S, Ohmori R, Saito H et al (2009) Coordinated control of a designed trans-acting ligase ribozyme by a loop–receptor interaction. FEBS Lett 583:2819–2826

    Article  CAS  PubMed  Google Scholar 

  11. Liu B, Baudrey S, Jaeger L, Bazan GC (2004) Characterization of tectoRNA assembly with cationic conjugated polymers. J Am Chem Soc 126:4076–4077

    Article  CAS  PubMed  Google Scholar 

  12. Shu Y, Pi F, Sharma A et al (2014) Stable RNA nanoparticles as potential new generation drugs for cancer therapy. Adv Drug Deliv Rev 66:74–89

    Article  CAS  PubMed  Google Scholar 

  13. Lin Y-X, Wang Y, Blake S et al (2020) RNA nanotechnology-mediated cancer immunotherapy. Theranostics 10:281

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ke W, Hong E, Saito RF et al (2018) RNA-DNA fibers and polygons with controlled immunorecognition activate RNAi, FRET and transcriptional regulation of NF-κB in human cells. Nucleic Acids Res 47:1350–1361

    Article  PubMed Central  Google Scholar 

  15. Laing C, Schlick T (2011) Computational approaches to RNA structure prediction, analysis, and design. Curr Opin Struct Biol 21:306–318. https://doi.org/10.1016/j.sbi.2011.03.015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Miao Z, Adamiak RW, Blanchet M-FM-F et al (2015) RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures. RNA 21:1066–1084. https://doi.org/10.1261/rna.049502.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Miao Z, Adamiak RW, Antczak M et al (2017) RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme. RNA 23:655–672. https://doi.org/10.1261/rna.060368.116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wang J, Mao K, Zhao Y et al (2017) Optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide–nucleotide interactions from direct coupling analysis. Nucleic Acids Res 45:6299–6309

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang J, Wang J, Huang Y, Xiao Y (2019) 3dRNA v2. 0: an updated web server for RNA 3D structure prediction. Int J Mol Sci 20:4116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Popenda M, Szachniuk M, Antczak M et al (2012) Automated 3D structure composition for large RNAs. Nucleic Acids Res 40:e112. https://doi.org/10.1093/nar/gks339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Cao S, Chen S-J (2011) Physics-based De novo prediction of RNA 3D structures. J Phys Chem B 115:4216–4226. https://doi.org/10.1021/jp112059y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Xu XJ, Zhao PN, Chen SJ (2014) Vfold: a web server for RNA structure and folding thermodynamics prediction. PLoS One 9:e107504. https://doi.org/10.1371/journal.pone.0107504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Rother M, Rother K, Puton T, Bujnicki JM (2011) ModeRNA: a tool for comparative modeling of RNA 3D structure. Nucleic Acids Res 39:4007–4022. https://doi.org/10.1093/nar/gkq1320

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Parisien M, Major F (2012) Determining RNA three-dimensional structures using low-resolution data. J Struct Biol 179:252–260. https://doi.org/10.1016/j.jsb.2011.12.024. Copyright (c) 2012 Elsevier Inc. All rights reserved

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ding F, Sharma S, Chalasani P et al (2008) Ab initio RNA folding by discrete molecular dynamics: from structure prediction to folding mechanisms. RNA 14:1164–1173. https://doi.org/10.1261/rna.894608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ding F, Lavender CA, Weeks KM, Dokholyan NV (2012) Three-dimensional RNA structure refinement by hydroxyl radical probing. Nat Methods 9:603–608. https://doi.org/10.1038/nmeth.1976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sharma S, Ding F, Dokholyan NV (2008) iFoldRNA: three-dimensional RNA structure prediction and folding. Bioinformatics 24:1951–1952. https://doi.org/10.1093/bioinformatics/btn328

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jonikas MA, Radmer RJ, Laederach A et al (2009) Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. RNA 15:189–199. https://doi.org/10.1261/rna.1270809

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Rother K, Rother M, Boniecki ML et al (2012) Template-based and template-free modeling of RNA 3D structure: inspirations from protein structure modeling. In: RNA 3D structure analysis and prediction, pp 67–90. https://doi.org/10.1007/978-3-642-25740-7_5

    Chapter  Google Scholar 

  30. Sripakdeevong P, Cevec M, Chang AT et al (2014) Structure determination of noncanonical RNA motifs guided by 1H NMR chemical shifts. Nat Methods 11:413

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Das R, Baker D (2007) Automated de novo prediction of native-like RNA tertiary structures. Proc Natl Acad Sci U S A 104:14664–14669. https://doi.org/10.1073/pnas.0703836104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Berman HM, Bhat TN, Bourne PE et al (2000) The protein data Bank and the challenge of structural genomics. Nat Struct Mol Biol 7:957–959

    Article  CAS  Google Scholar 

  33. Williams Benfeard II, Zhao B, Tandon A et al (2017) Structure modeling of RNA using sparse NMR constraints. Nucleic Acids Res 45:12638–12647

    Article  PubMed  PubMed Central  Google Scholar 

  34. Dokholyan NV, Buldyrev SV, Stanley HE, Shakhnovich EI (1998) Discrete molecular dynamics studies of the folding of a protein-like model. Fold Des 3:577–587. https://doi.org/10.1016/s1359-0278(98)00072-8

    Article  CAS  PubMed  Google Scholar 

  35. Ding F, Tsao D, Nie H, Dokholyan NV (2008) Ab initio folding of proteins with all-atom discrete molecular dynamics. Structure 16:1010–1018. https://doi.org/10.1016/j.str.2008.03.013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Proctor EA, Ding F, Dokholyan NV (2011) Discrete molecular dynamics. Wiley Interdiscip Rev Comput Mol Sci 1:80–92. https://doi.org/10.1002/wcms.4

    Article  CAS  Google Scholar 

  37. Frank AT, Horowitz S, Andricioaei I, Al-Hashimi HM (2013) Utility of 1H NMR chemical shifts in determining RNA structure and dynamics. J Phys Chem B 117:2045–2052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Dejaegere A, Bryce RA, Case DA (1999) An empirical analysis of proton chemical shifts in nucleic acids. ACS Publications

    Book  Google Scholar 

  39. Barton S, Heng X, Johnson BA, Summers MF (2013) Database proton NMR chemical shifts for RNA signal assignment and validation. J Biomol NMR 55:33–46

    Article  CAS  PubMed  Google Scholar 

  40. Parisien M, Major F (2012) Determining RNA three-dimensional structures using low-resolution data. J Struct Biol 179:252–260

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sim AYL, Minary P, Levitt M (2012) Modeling nucleic acids. Curr Opin Struct Biol 22:273–278

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Feigon J, Skelenář V, Wang E et al (1992) [13] 1H NMR spectroscopy of DNA. Methods Enzymol 211:235–253

    Article  CAS  PubMed  Google Scholar 

  43. Patel DJ, Suri AK, Jiang F et al (1997) Structure, recognition and adaptive binding in RNA aptamer complexes. J Mol Biol 272:645–664

    Article  CAS  PubMed  Google Scholar 

  44. Buck J, Fürtig B, Noeske J et al (2007) Time-resolved NMR methods resolving ligand-induced RNA folding at atomic resolution. Proc Natl Acad Sci 104:15699–15704

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lee M-K, Gal M, Frydman L, Varani G (2010) Real-time multidimensional NMR follows RNA folding with second resolution. Proc Natl Acad Sci 107:9192–9197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Burke JE, Sashital DG, Zuo X et al (2012) Structure of the yeast U2/U6 snRNA complex. RNA 18:673–683

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kim I, Lukavsky PJ, Puglisi JD (2002) NMR study of 100 kDa HCV IRES RNA using segmental isotope labeling. J Am Chem Soc 124:9338–9339

    Article  CAS  PubMed  Google Scholar 

  48. Krokhotin A, Houlihan K, Dokholyan NV (2015) iFoldRNA v2: folding RNA with constraints. Bioinformatics 31:2891–2893. https://doi.org/10.1093/bioinformatics/btv221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dingley AJ, Grzesiek S (1998) Direct observation of hydrogen bonds in nucleic acid base pairs by internucleotide 2 J NN couplings. J Am Chem Soc 120:8293–8297

    Article  CAS  Google Scholar 

  50. Christy TW, Giannetti CA, Houlihan G et al (2021) Direct mapping of higher-order RNA interactions by SHAPE-JuMP. Biochemistry 60:1971–1982

    Article  CAS  PubMed  Google Scholar 

  51. Wang J, Williams B, Chirasani VR et al (2019) Limits in accuracy and a strategy of RNA structure prediction using experimental information. Nucleic Acids Res 47:5563–5572. https://doi.org/10.1093/nar/gkz427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Krokhotin A, Dokholyan NV (2015) Chapter three – computational methods toward accurate RNA structure prediction using coarse-grained and all-atom models. In: Chen S-J, Burke-Aguero DHBT-M (eds) Computational methods for understanding riboswitches. Academic, pp 65–89

    Chapter  Google Scholar 

  53. Proctor EA, Dokholyan NV (2016) Applications of discrete molecular dynamics in biology and medicine. Curr Opin Struct Biol 37:9–13. https://doi.org/10.1016/j.sbi.2015.11.001

    Article  CAS  PubMed  Google Scholar 

  54. Lazaridis T, Karplus M (1999) Effective energy function for proteins in solution. Proteins Struct Funct Bioinf 35:133–152

    Article  CAS  Google Scholar 

  55. Das R, Karanicolas J, Baker D (2010) Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Methods 7:291–294. https://doi.org/10.1038/nmeth.1433

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhao Y, Huang Y, Gong Z et al (2012) Automated and fast building of three-dimensional RNA structures. Sci Rep 2:734. https://doi.org/10.1038/srep00734

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA

    Jian Wang, Congzhou M. Sha & Nikolay V. Dokholyan

  2. Department of Engineering Science and Mechanics, Penn State University, State College, PA, USA

    Congzhou M. Sha & Nikolay V. Dokholyan

  3. Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, USA

    Nikolay V. Dokholyan

  4. Department of Chemistry, Penn State University, State College, PA, USA

    Nikolay V. Dokholyan

  5. Department of Biomedical Engineering, Penn State University, State College, PA, USA

    Nikolay V. Dokholyan

Authors
  1. Jian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Congzhou M. Sha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikolay V. Dokholyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikolay V. Dokholyan .

Editor information

Editors and Affiliations

  1. Department of Chemistry, UNC Charlotte, Charlotte, NC, USA

    Kirill A. Afonin

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Wang, J., Sha, C.M., Dokholyan, N.V. (2023). Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology. In: Afonin, K.A. (eds) RNA Nanostructures. Methods in Molecular Biology, vol 2709. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3417-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3417-2_3

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3416-5

  • Online ISBN: 978-1-0716-3417-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation