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Programmable energy landscapes for kinetic control of DNA strand displacement

Author

Listed:
  • Robert R. F. Machinek

    (Clarendon Laboratory, University of Oxford)

  • Thomas E. Ouldridge

    (Rudolf Peierls Centre for Theoretical Physics, University of Oxford)

  • Natalie E. C. Haley

    (Clarendon Laboratory, University of Oxford)

  • Jonathan Bath

    (Clarendon Laboratory, University of Oxford)

  • Andrew J. Turberfield

    (Clarendon Laboratory, University of Oxford)

Abstract

DNA is used to construct synthetic systems that sense, actuate, move and compute. The operation of many dynamic DNA devices depends on toehold-mediated strand displacement, by which one DNA strand displaces another from a duplex. Kinetic control of strand displacement is particularly important in autonomous molecular machinery and molecular computation, in which non-equilibrium systems are controlled through rates of competing processes. Here, we introduce a new method based on the creation of mismatched base pairs as kinetic barriers to strand displacement. Reaction rate constants can be tuned across three orders of magnitude by altering the position of such a defect without significantly changing the stabilities of reactants or products. By modelling reaction free-energy landscapes, we explore the mechanistic basis of this control mechanism. We also demonstrate that oxDNA, a coarse-grained model of DNA, is capable of accurately predicting and explaining the impact of mismatches on displacement kinetics.

Suggested Citation

  • Robert R. F. Machinek & Thomas E. Ouldridge & Natalie E. C. Haley & Jonathan Bath & Andrew J. Turberfield, 2014. "Programmable energy landscapes for kinetic control of DNA strand displacement," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6324
    DOI: 10.1038/ncomms6324
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    Cited by:

    1. Marius Rutkauskas & Inga Songailiene & Patrick Irmisch & Felix E. Kemmerich & Tomas Sinkunas & Virginijus Siksnys & Ralf Seidel, 2022. "A quantitative model for the dynamics of target recognition and off-target rejection by the CRISPR-Cas Cascade complex," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Chisato Terada & Kaho Oh & Ryutaro Tsubaki & Bun Chan & Nozomi Aibara & Kaname Ohyama & Masa-Aki Shibata & Takehiko Wada & Mariko Harada-Shiba & Asako Yamayoshi & Tsuyoshi Yamamoto, 2023. "Dynamic and static control of the off-target interactions of antisense oligonucleotides using toehold chemistry," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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