Constantijn van der Smagt

Vrije Universiteit Amsterdam

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Ilya J. Finkelstein

The University of Texas at Austin

Misha Klein

Delft University of Technology

John A. Hawkins

University of California, Davis

Behrouz Eslami-Mossallam

Delft University of Technology

Martin Depken

Delft University of Technology

Koen van der Sanden

Netherlands Organisation for Applied Scientific Research

Stephen K. Jones

Vilnius University

Cooperative Institutions

Delft University of Technology

The University of Texas at Austin

Leiden University

Center for Systems Biology

Institut de Biologie Moléculaire et Cellulaire

Columbia University

National Institutes of Health

Howard Hughes Medical Institute

Stanford University

University of Minnesota

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

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

Nature Communications (2022)

Behrouz Eslami-Mossallam Misha Klein Constantijn van der Smagt Koen van der Sanden Stephen K. Jones

Abstract The S. pyogenes (Sp) Cas9 endonuclease is an important gene-editing tool. Sp Cas9 is directed to target sites based on complementarity to a complexed single-guide RNA (sgRNA). However, Sp Cas9-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 Sp Cas9-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 Sp Cas9; elucidating determinants of CRISPR fidelity; and revealing pathways to increased specificity and efficiency in engineered systems.

Guide RNA

Nuclease

Complementarity (molecular biology)

10.1038/s41467-022-28994-2

Cite

Citations (29)