Skip to main content

Molecular Modeling and Simulations of DNA and RNA: DNAzyme as a Model System

  • Protocol
  • First Online:
DNAzymes

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2439))

Abstract

Nowadays, the structural dynamics of DNA and RNA is accessible on an atomistic level on a micro- to millisecond time scale via molecular dynamics simulations. However, as DNA or RNA are highly charged molecules, performing such simulations is challenging as to the representation of intramolecular electrostatic interactions and those to solvent molecules and ions. This is particularly true for DNAzymes, where DNA and RNA backbones can come as close as 2.4 Å with their charged phosphate groups during the catalytic cycle. Here, we present tools to simulate the structural dynamics of a DNAzyme, with a focus on detailed instructions for the Amber suite of programs. Furthermore, we will show how to analyze metal ion binding within the DNAzyme.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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

Notes

  1. 1.

    During typesetting of this article, an NMR structure of a 10-23 DNAzyme in complex with RNA has been solved: PDB ID 7PDU; [23].

References

  1. Li Y, Sen D (1997) Toward an efficient DNAzyme. Biochemistry 36(18):5589–5599

    Article  CAS  Google Scholar 

  2. Geyer CR, Sen D (1997) Evidence for the metal-cofactor independence of an RNA phosphodiester-cleaving DNA enzyme. Chem Biol 4(8):579–593

    Article  CAS  Google Scholar 

  3. Torabi S-F, Wu P, McGhee CE, Chen L, Hwang K, Zheng N, Cheng J, Lu Y (2015) In vitro selection of a sodium-specific DNAzyme and its application in intracellular sensing. Proc Natl Acad Sci U S A 112(19):5903–5908

    Article  CAS  Google Scholar 

  4. Hanke CA, Gohlke H (2015) Force field dependence of riboswitch dynamics. Methods Enzymol 553:163–191

    Article  CAS  Google Scholar 

  5. Sponer J, Bussi G, Krepl M, Banáš P, Bottaro S, Cunha RA, Gil-Ley A, Pinamonti G, Poblete S, Jurečka P (2018) RNA structural dynamics as captured by molecular simulations: a comprehensive overview. Chem Rev 118(8):4177–4338

    Article  CAS  Google Scholar 

  6. Salsbury AM, Lemkul JA (2021) Recent developments in empirical atomistic force fields for nucleic acids and applications to studies of folding and dynamics. Curr Opin Struct Biol 67:9–17

    Article  CAS  Google Scholar 

  7. D.A. Case KB, Ben-Shalom IY, Brozell SR, Cerutti DS, Cheatham III TE, Cruzeiro VWD, Darden TA, Duke RE, Giambasu G, Gilson MK, Gohlke H, Goetz AW, Harris R, Izadi S, Izmailov SA, Kasavajhala K, Kovalenko A, Krasny R, Kurtzman T, Lee TS, LeGrand S, Li P, Lin C, Liu J, Luchko T, Luo R, Man V, Merz KM, Miao Y, Mikhailovskii O, Monard G, Nguyen H, Onufriev A, Pan F, Pantano S, Qi R, Roe DR, Roitberg A, Sagui C, Schott-Verdugo S, Shen J, Simmerling C, Skrynnikov NR, Smith J, Swails J, Walker RC, Wang J, Wilson L, Wolf RM, Wu X, Xiong Y, Xue Y, York DM, Kollman PA (2020) AMBER 2020. University of California, San Francisco

    Google Scholar 

  8. Case DA, Cheatham TE III, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26(16):1668–1688

    Article  CAS  Google Scholar 

  9. Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J Chem Theory Comput 9(9):3878–3888

    Article  CAS  Google Scholar 

  10. Wang J, Cieplak P, Kollman PA (2000) How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem 21(12):1049–1074

    Article  CAS  Google Scholar 

  11. Pérez A, Marchán I, Svozil D, Sponer J, Cheatham TE III, Laughton CA, Orozco M (2007) Refinement of the AMBER force field for nucleic acids: improving the description of α/γ conformers. Biophys J 92(11):3817–3829

    Article  Google Scholar 

  12. Zgarbová M, Luque FJ, Sponer J, Cheatham TE III, Otyepka M, Jurecka P (2013) Toward improved description of DNA backbone: revisiting epsilon and zeta torsion force field parameters. J Chem Theory Comput 9(5):2339–2354

    Article  Google Scholar 

  13. Krepl M, Zgarbová M, Stadlbauer P, Otyepka M, Banáš P, Koca J, Cheatham TE III, Jurecka P, Sponer J (2012) Reference simulations of noncanonical nucleic acids with different χ variants of the AMBER force field: quadruplex DNA, quadruplex RNA, and Z-DNA. J Chem Theory Comput 8(7):2506–2520

    Article  CAS  Google Scholar 

  14. Zgarbová M, Sponer J, Otyepka M, Cheatham TE III, Galindo-Murillo R, Jurecka P (2015) Refinement of the sugar–phosphate backbone torsion beta for AMBER force fields improves the description of Z-and B-DNA. J Chem Theory Comput 11(12):5723–5736

    Article  Google Scholar 

  15. Zgarbová M, Otyepka M, Ji Š, At M, Banáš P, Cheatham TE III, Jurecka P (2011) Refinement of the Cornell et al. nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. J Chem Theory Comput 7(9):2886–2902

    Article  Google Scholar 

  16. Banás P, Hollas D, Zgarbová M, Jurecka P, Orozco M, Cheatham TE III, Sponer J, Otyepka M (2010) Performance of molecular mechanics force fields for RNA simulations: stability of UUCG and GNRA hairpins. J Chem Theory Comput 6(12):3836–3849

    Article  Google Scholar 

  17. Brooks BR, Brooks CL III, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30(10):1545–1614

    Article  CAS  Google Scholar 

  18. Bowers KJ, Chow DE, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: SC'06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing. IEEE, New York, pp 43–43

    Chapter  Google Scholar 

  19. Bekker H, Berendsen H, Dijkstra E, Achterop S, Vondrumen R, Vanderspoel D, Sijbers A, Keegstra H, Reitsma B, Renardus M (1993) Gromacs: a parallel computer for molecular dynamics simulations. In: de Groot R, Nadrchal J (eds) Physics computing 92. World Scientific, Singapore

    Google Scholar 

  20. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25

    Article  Google Scholar 

  21. Phillips JC, Hardy DJ, Maia JD, Stone JE, Ribeiro JV, Bernardi RC, Buch R, Fiorin G, Hénin J, Jiang W (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 153(4):044130

    Article  CAS  Google Scholar 

  22. Rackers JA, Wang Z, Lu C, Laury ML, Lagardère L, Schnieders MJ, Piquemal J-P, Ren P, Ponder JW (2018) Tinker 8: software tools for molecular design. J Chem Theory Comput 14(10):5273–5289

    Article  CAS  Google Scholar 

  23. Borggräfe J, Victor J, Rosenbach H, Viegas A, Gertzen CGW, Wuebben C, Kovacs H, Gopalswamy M, Riesner D, Steger G, Schiemann O, Gohlke H, Span I, Etzkorn M (2022) Time-resolved structural analysis of an RNA-cleaving DNA catalyst. Nature 601(7891):144–149. https://doi.org/10.1038/s41586-021-04225-4

  24. Perez-Garcia P, Kobus S, Gertzen CG, Hoeppner A, Holzscheck N, Strunk CH, Huber H, Jaeger K-E, Gohlke H, Kovacic F (2021) A promiscuous ancestral enzyme´ s structure unveils protein variable regions of the highly diverse metallo-β-lactamase family. Commun Biol 4(1):1–12

    Article  Google Scholar 

  25. Yoo AB, Jette MA, Grondona M (2003) Slurm: simple linux utility for resource management. In: Workshop on job scheduling strategies for parallel processing. Springer, New York, pp 44–60

    Chapter  Google Scholar 

  26. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38

    Article  CAS  Google Scholar 

  27. Schott-Verdugo S, Gohlke H (2019) PACKMOL-memgen: a simple-to-use, generalized workflow for membrane-protein–lipid-bilayer system building. J Chem Inf Model 59(6):2522–2528

    Article  CAS  Google Scholar 

  28. Joung IS, Cheatham TE III (2008) Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J Phys Chem B 112(30):9020–9041

    Article  CAS  Google Scholar 

  29. Li P, Roberts BP, Chakravorty DK, Merz KM Jr (2013) Rational design of particle mesh Ewald compatible Lennard-Jones parameters for+ 2 metal cations in explicit solvent. J Chem Theory Comput 9(6):2733–2748

    Article  CAS  Google Scholar 

  30. Hanke CA, Gohlke H (2017) Ligand-mediated and tertiary interactions cooperatively stabilize the P1 region in the guanine-sensing riboswitch. PLoS One 12(6):e0179271

    Article  Google Scholar 

  31. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N· log (N) method for Ewald sums in large systems. J Chem Phys 98(12):10089–10092

    Article  CAS  Google Scholar 

  32. Galindo-Murillo R, Cheatham TE 3rd (2019) Lessons learned in atomistic simulation of double-stranded DNA: solvation and salt concerns [article v1. 0]. Living J Comput Mol Sci 1(2):9974

    Article  Google Scholar 

Download references

Acknowledgments

H.G. is grateful for computational support and infrastructure provided by the “Zentrum für Informations- und Medientechnologie” (ZIM) at the Heinrich Heine University Düsseldorf and the John von Neumann Institute for Computing (NIC) (user ID: HKF7), VSK33. The Center for Structural Studies is funded by the DFG (Grant number 417919780).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Holger Gohlke .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Gertzen, C.G.W., Gohlke, H. (2022). Molecular Modeling and Simulations of DNA and RNA: DNAzyme as a Model System. In: Steger, G., Rosenbach, H., Span, I. (eds) DNAzymes. Methods in Molecular Biology, vol 2439. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2047-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2047-2_11

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2046-5

  • Online ISBN: 978-1-0716-2047-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics