Controlling DNA–RNA strand displacement kinetics with base distribution

DNA-RNA hybrid strand displacement underpins the function of many natural and engineered systems. Understanding and controlling factors affecting DNA-RNA strand displacement reactions is necessary to enable control of processes such as CRISPR-Cas9 gene editing. By combining multiscale modeling with strand displacement experiments, we show that the distribution of bases within the displacement domain has a very strong effect on reaction kinetics, a feature unique to DNA-RNA hybrid strand displacement. Merely by redistributing bases within a displacement domain of fixed base composition, we are able to design sequences whose reaction rates span more than four orders of magnitude. We extensively characterize this effect in reactions involving the invasion of dsDNA by an RNA strand, as well as the invasion of a hybrid duplex by a DNA strand. In all-DNA strand displacement reactions, we find a predictable but relatively weak sequence dependence, confirming that DNA-RNA strand displacement permits far more thermodynamic and kinetic control than its all-DNA counterpart. We show that oxNA, a recently introduced coarse-grained model of DNA-RNA hybrids, can reproduce trends in experimentally observed reaction rates. We also develop a simple kinetic model for predicting strand displacement rates. On the basis of these results, we argue that base distribution effects may play an important role in natural R-loop formation and in the function of the guide RNAs that direct CRISPR-Cas systems.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-06-06
DOI
https://doi.org/10.1073/pnas.2416988122
Citations
7
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
5.64

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article

Controlling DNA–RNA strand displacement kinetics with base distribution

Andrew J. Turberfield, Jonathan Bath, Petr Šulc, Jonathan P. K. Doye et al.
7 citations
Proceedings of the National Academy of Sciences
Advanced biosensing and bioanalysis techniques
5.64
article

Controlling DNA–RNA strand displacement kinetics with base distribution

Andrew J. Turberfield, Jonathan Bath, Petr Šulc, Jonathan P. K. Doye, Ard A. Louis, Eryk J. Ratajczyk
article en
7 citations

Abstract

DNA-RNA hybrid strand displacement underpins the function of many natural and engineered systems. Understanding and controlling factors affecting DNA-RNA strand displacement reactions is necessary to enable control of processes such as CRISPR-Cas9 gene editing. By combining multiscale modeling with strand displacement experiments, we show that the distribution of bases within the displacement domain has a very strong effect on reaction kinetics, a feature unique to DNA-RNA hybrid strand displacement. Merely by redistributing bases within a displacement domain of fixed base composition, we are able to design sequences whose reaction rates span more than four orders of magnitude. We extensively characterize this effect in reactions involving the invasion of dsDNA by an RNA strand, as well as the invasion of a hybrid duplex by a DNA strand. In all-DNA strand displacement reactions, we find a predictable but relatively weak sequence dependence, confirming that DNA-RNA strand displacement permits far more thermodynamic and kinetic control than its all-DNA counterpart. We show that oxNA, a recently introduced coarse-grained model of DNA-RNA hybrids, can reproduce trends in experimentally observed reaction rates. We also develop a simple kinetic model for predicting strand displacement rates. On the basis of these results, we argue that base distribution effects may play an important role in natural R-loop formation and in the function of the guide RNAs that direct CRISPR-Cas systems.

Proceedings of the National Academy of SciencesVol. 122(23)
Clarendon College (US), University of Oxford (GB), Kavli Energy NanoScience Institute (US), Center for Theoretical Biological Physics (US), Centre for Policy Studies (IN), Arizona State University (US), Technical University of Munich (DE)
National Science Foundation, Engineering and Physical Sciences Research Council
Life below water
Openalex Percentile: Top 4%
Advanced biosensing and bioanalysis techniques
5.64
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