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.

The SpCas9 endonuclease has become an important tool in gene-editing and basic science alike. Though easily programmed to target any sequence, SpCas9 also shows considerable activity over genomic offtargets. Many empirical facts regarding the targeting reaction have been established, but a comprehensive mechanistic description is still lacking-limiting fundamental understanding, our ability to predict off-target activity, and ultimately the safe adaptation of the SpCas9 toolkit for therapeutics. By mechanistically modelling the SpCas9 structure-function relationship, we simultaneously capture binding and cleavage dynamics for SpCas9 and Sp-dCas9 in terms of free-energies. When our model is trained on high-throughput data, we outperform state-of-the-art off-target prediction tools. Based on the biophysical parameters we extract, our model predicts the open, intermediate, and closed complex configurations described in singlemolecule FRET experiments, and indicates that R-loop progression is tightly coupled to structural changes in the targeting complex. We further show that SpCas9 targeting kinetics are tuned for extended sequence specificity while maintaining on-target efficiency. Our approach can be used to characterize any other CRISPR derived nuclease, and contrasting future studies of high-fidelity variants with the SpCas9 benchmark we here provide will help elucidate the determinants of CRISPR fidelity and the path to increased specificity and efficiency in engineered systems.