A kinetic model predicts SpCas9 activity, improves off-target classification, and reveals the physical basis of targeting fidelity

Behrouz Eslami-Mossallam, Misha Klein, Constantijn V. D. Smagt, Koen V. D. Sanden, Stephen K. Jones, John A. Hawkins, Ilya J. Finkelstein and Martin Depken ()
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Behrouz Eslami-Mossallam: Delft University of Technology
Misha Klein: Delft University of Technology
Constantijn V. D. Smagt: Delft University of Technology
Koen V. D. Sanden: Delft University of Technology
Stephen K. Jones: University of Texas at Austin
John A. Hawkins: University of Texas at Austin
Ilya J. Finkelstein: University of Texas at Austin
Martin Depken: Delft University of Technology

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract The S. pyogenes (Sp) Cas9 endonuclease is an important gene-editing tool. SpCas9 is directed to target sites based on complementarity to a complexed single-guide RNA (sgRNA). However, SpCas9-sgRNA also binds and cleaves genomic off-targets with only partial complementarity. To date, we lack the ability to predict cleavage and binding activity quantitatively, and rely on binary classification schemes to identify strong off-targets. We report a quantitative kinetic model that captures the SpCas9-mediated strand-replacement reaction in free-energy terms. The model predicts binding and cleavage activity as a function of time, target, and experimental conditions. Trained and validated on high-throughput bulk-biochemical data, our model predicts the intermediate R-loop state recently observed in single-molecule experiments, as well as the associated conversion rates. Finally, we show that our quantitative activity predictor can be reduced to a binary off-target classifier that outperforms the established state-of-the-art. Our approach is extensible, and can characterize any CRISPR-Cas nuclease – benchmarking natural and future high-fidelity variants against SpCas9; elucidating determinants of CRISPR fidelity; and revealing pathways to increased specificity and efficiency in engineered systems.

Date: 2022
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DOI: 10.1038/s41467-022-28994-2

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