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

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.

A quantitative model for the dynamics of target recognition and off-target rejection by the CRISPR-Cas Cascade complex

“…Despite an increasing detailed mechanistic understanding of target recognition by CRISPR–Cas complexes, the wealth of mechanistic knowledge has until recently 32 not been exploited for off-target predictions nor, despite widely suggested, been applied to quantitatively understand the targeting dynamics in mechanistic studies 30 , 31 , 33 .…”

“…We show that the observed dynamics can be quantitatively modeled by describing R-loop formation as a random walk model in a simplified one-dimensional free energy landscape. The model was adapted from previous descriptions of protein-free strand displacement reactions in dynamic DNA nanotechnology 34 , 35 , which have recently been introduced to the CRISPR–Cas field 31 , 32 . Importantly, our modeling (i) provides direct evidence that R-loop expansion down to local sub-states follows a random-walk process (ii), shows that the single-base pair stepping of R-loop expansion occurs at a sub-millisecond time scale, (iii) returns absolute free energy penalties imposed by different mismatches, (iv) quantitatively predicts the non-trivial dependence of R-loop formation on the proximity between multiple mismatches and (v) reveals that the length of the seed region in Cascade is a function of the applied supercoiling rather than a structural property.…”

Titrating CD47 by mismatch CRISPRi reveals incomplete repression can eliminate IgG-opsonized tumors but CD47 heterogeneity limits induction of anti-tumor IgG

“…Nonetheless, the deep repression enables us to systematically perform nucleotide mismatches to greatly alter sgRNA efficacy to generate the desired titration. For choosing mismatches, we assumed that nucleotide position mattered more than the nucleotide itself, based on previous computational modeling experiments (Boyle et al, 2017; Eslami-Mossallam et al, 2022; Jones et al, 2021). We then generated an initial compact sgRNA library with mismatches at nearly all nucleotide positions to identify possibly sensitive base sites relative to the protospacer adjacent motif (PAM) ( Fig.…”

“…All free energy and kinetics numerical values were based on the calculations performed using the dashboard application described in (Eslami-Mossallam et al, 2022). Calculated parameters were done using the dashboard's initial assumptions.…”

“… (B) K d for each sgRNA as calculated from pre-existing modeling software (Eslami-Mossallam et al, 2022) and assuming the same unit ‘concentration’ in each cell for every sgRNA.…”