A kinetic model predicts Sp Cas9 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 Jr., John A. Hawkins, Ilya J. Finkelstein, and Martin Depken (* co-first authors)

Nature Communications, Volume 13, Issue 1, Pages 1367 (2022)

DOI: 10.1038/s41467-022-28994-2

Table of Contents

• Abstract
• Introduction
• Results
• Discussion
• Methods
• Acknowledgements

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. Subject terms: Enzyme mechanisms, Kinetics, Computational models, CRISPR-Cas9 genome editing, Biological physics

Cas9 off-target sites can be predicted by many bioinformatics tools. Here the authors present low complexity mechanistic model that characterizes SpCas9 kinetics in free-energy terms, allowing quantitative prediction of off-target activity in bulk-biochemistry, single molecule, and whole-genome profiling experiments. —

Introduction

CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats—CRISPR-associated protein 9) has become a ubiquitous tool in the biological sciences[1],[2], with applications ranging from live-cell imaging[3] and gene knockdown/overexpression[4],[5] to genetic engineering[6],[7] and gene therapy[8],[9]. Streptococcus pyogenes (Sp) Cas9 can be programmed with a ~100 nucleotide (nt) single-guide RNA (sgRNA) to target DNAs based on the level of complementarity to a 20 nt segment of the sgRNA[10]. Wildtype Sp Cas9 (henceforth Cas9) induces site-specific double-stranded breaks and the catalytically dead Cas9 (dCas9) mutant allows for binding without cleavage[3],[5]. Apart from complimentary on-targets, Cas9-sgRNA also binds and cleaves non-complementary off-targets[11]–[18]. Off-target cleavage risks deleterious genomic alterations, which has so far impeded the widespread implementation of the CRISPR toolkit in human therapeutics[19]. Strong off-target sites are identified in silico by a growing set of tools. These tools use bioinformatics[20],[21], machine learning[22],[23], or heuristic[12],[14],[24],[25] approaches to rank genomic sites based on distinctive off-target activity scores. Though such models can identify strong off-targets, they are not quantitative and cannot assess activity on the many lesser off-targets; nor can they predict how activity changes with exposure time and enzyme concentration—even though these parameters are frequently exploited to limit off-target activity in cells[26]. To implement quantitative activity prediction, Cas9 targeting must be modelled in physical terms. Existing physical models[24],[27],[28] assume binding equilibration before cleavage, and it remains unclear what predictive power such approaches can ultimately deliver in this non-equilibrium system[29],[30]. To account for the nonequilibrium nature of the targeting reaction, we construct a mechanistic model that captures binding and cleavage reactions in kinetic terms. To gain insights into general mechanisms, we train and validate our model on high-throughput datasets that capture both binding and cleavage in bulk experiments[15],[31]. Though we restrict our training to off-targets with two or less mismatches, we accurately predict the activities on all more highly mismatched off-targets in the same datasets, as well as those reported in two independent high-throughput datasets[11]. To reveal the physical basis of Cas9 fidelity on genomic scales, we extract the free-energy landscapes that control PAM binding, strand-replacement, and cleavage on any target. Our characterization of Cas9 supports the notion that observed differences in binding and cleavage activities[32]–[41] stem from a relatively long-lived DNA-bound RNA-DNA hybrid (R-loop) intermediate. This R-loop intermediate was recently observed directly in single-molecule experiments[42], and our model predicts both its location and its conversion rates. Though the strengths of our model lies in that it allows us to calculate how (d)Cas9 activity evolves in time under various conditions, we also sought to compare our approach to existing binary off-target classifiers that identify strong off-targets. To this end, we reduce our quantitative activity predictor to a binary off-target classifier that outperforms the leading tools used today[12],[24],[28],[43]. —

Results

The kinetic model

In Fig. [1a] we show the reaction pathway that underpins the Cas9 targeting reaction on every target[44]. The reaction starts with Cas9-sgRNA ribonucleoprotein complex exiting the solution state to specifically bind to a 3nt protospacer adjacent motif (PAM) DNA sequence—canonically 5’-NGG-3’—via protein-DNA interactions[44],[45]. Binding to the PAM sequence (state 0) opens the DNA double helix, and allows the first base of the target sequence to hybridize with the sgRNA[44],[45], forming the first R-loop state (state 1). The DNA double helix further denatures as the RNA-DNA hybrid is extended in the guide-target strand-replacement reaction[46]–[49] (state 2-20). The hybrid grows and shrinks in single-nucleotide steps, until it is either reversed and Cas9 dissociates, or it reaches completion at 20 base pairs (bp) in state 20. If the full hybrid is formed, Cas9 can use its HNH and RuvC nuclease domains to cleave both DNA strands[50].

If we know the conversion rates in Fig. [1a] for a particular guide and target, the reaction scheme can be solved to calculate the binding and cleavage probabilities at any time (Methods). Fully parameterizing the model over all guide and target sequences requires the estimation of ~1026 rates. To render parameter estimation tractable, we make four mechanistic-model assumptions:

1. Mismatch positions are more important than mismatch types (e.g. G-G vs. G-A). This can be directly inferred from data[11],[15], and we treat all 12 mismatch types equally.
2. Mismatch energies are determined by local interactions. The energetic cost of multiple mismatches is taken to be equal to the sum of the energetic costs of the individual mismatches.
3. dCas9 differs from Cas9 only in that dsDNA bond-cleavage catalysis is completely suppressed (k cat = 0); all other rates are taken to be identical[51],[52].
4. All selectivity is governed by the hybrid-bond-reversal rates. Hybrid-bond-formation rates are treated as equal, independent of complementarity and location.

These assumptions reduce the total number of microscopic parameters to 44 (see Methods): the (concentration dependent) rate of PAM binding from solution (k on) and the associated free-energy gain (F 0); a single internal forward bond-formation rate (k f); 20 free energies dictating R-loop progression at the on-target (k cat). Once model parameters are estimated, all possible off-target free energies can be directly calculated using assumptions 1–4 above. In Fig. [1b] we illustrate the calculation taking us from the on-target (pink) to the off-target (blue) free-energy landscape with mismatches entering the hybrid at the 3rd and 15th bp. How to translate between free energies and rates is detailed in Methods. Base-pairing interactions, protein-DNA interactions[52], and induced conformational changes[50],[51],[53],[54] all contribute to the stability of the Cas9-sgRNA-DNA complex. To account for the varying nature of these interactions, we allow for varying gains and losses in the on-target free-energy landscape as the hybrid is extended. These variable gains and losses allow for the formation of metastable states on the on-target, and constitutes an essential extension of our previous fixed-gain model for RNA-guided nuclease kinetics[30], as well as of models describing DNA displacement reactions occurring in solution[55]–[58].

Training on binding and cleavage for moderately mismatched targets

We seek to reveal general properties of Sp Cas9 DNA targeting on genomic scales. To this end, we train and validate our model on data from two highly reproducible bulk-biochemical experiments performed on a large library of moderately to highly mismatched off-targets. The first set[15] (NucleaSeq) has 97% correlation between replicated experiments, and estimates the effective cleavage rates ([15],[31] (CHAMP) has 94% correlation between replicated experiments, and reports on the effective association constant ( We estimate the model parameters by minimizing the total experimental-error weighted residue between prediction and experiment for off-targets (see Methods) with no more than two mismatches in the NucleaSeq (Fig. [2a–c]) and CHAMP (Fig. [2d–f]) experiments. The rates and association constants from different types of mismatches are averaged (see Methods and Supplementary Data [1]), and the optimal solution is sought with a Simulated Annealing algorithm[59] (see Methods).

The two training sets differ significantly (Fig. [2], and Supplementary Fig. 1a). Our model still reproduces effective cleavage rates (Fig. [2a–c]) and effective association constants (Fig. [2d–f]) with a Pearson correlation of 93% and 98% respectively, and quantitatively captures the difference between binding and cleavage activity. The time and concentration dependence of (d)Cas9 activity can be explored through a dashboard we provide (see Code Availability).

Validation on highly mismatched targets and independent data sets

Apart from the data we use for training (two or less mismatches), the NucleaSeq[15] and CHAMP[15],[31] sequence libraries also includes block-mismatched targets with more than two mismatches. In Fig. [3a, b] we show that we quantitatively predict effective association constants on these highly mismatched targets at a correlation of 98%. Our method also successfully separates out the single dominating off-target present among highly mismatched targets in the NucleaSeq experiments (Supplementary Fig. 1b), resulting in a perfect correlation.

To further validate our model, we test against two data sets from HiTS-FLIP experiments reported in the literature[11]. The first independent validation set records the association rate relative to the on-target, estimated over 1500 seconds of exposure to dCas9-sgRNA at 1 nM concentration (Fig. [3c–e]). The second independent validation set records the dissociation rate relative to the on-target, estimated over 1500 seconds following 12 hours of exposure to a saturating dCas9-sgRNA concentration (Fig. [3f–h]). Our model quantitatively captures the relative association rates for all reported targets with 82% correlation (Fig. [3e]). For the relative dissociation rates, the correlation is more modest at 46% (Fig. [3h]), and the quantitative agreement is lost in some regions (Fig. [3f–h]). We still seem to capture the general trends on moderately mismatched targets (Fig. [3f, g]), though our model will never give binding/dissociation rates above/below that of the on-target, as is reported for some highly mismatched targets (Fig. [3e, h])

Physical characterization of SpCas9 and the intermediate R-loop state

As our model parameters carry physical meaning, estimating them from data amounts to characterizing the system in physical terms. For Cas9, it has been experimentally shown that R-loop progression is controlled by an intermediate metastable state on the on-target[42]. We expect this intermediate state to show up as a local minimum in our estimated on-target free-energy landscape. The free energy of any metastable state will have a strong influence on the observed dynamics, and we expect such energies to be well constrained by the data. We expect barriers between metastable states to be harder to resolve, as the details of barrier regions matter less for the observable dynamics. We here report 33 near-equivalent optimization runs that all resulted in a residue that fell within 15% of the best solution found (see Supplementary Video [1]). In Fig. [4a] we plot the resulting on-target free-energy landscapes, with the optimal solution highlighted in pink. As expected, we see metastable states in the on-target free-energy landscape. With Cas9 in solution or PAM-bound, we have a well-defined free-energy minimum where the R-loop is closed (C). The on-target free energy (Fig. [4a]) increases substantially when forming the first hybrid bp in state 1, and remains relatively high and poorly constrained up to and including state 8. The energy of state 9-12 are well constrained, and among them we find a second local minimum. We identify these states as belonging to an intermediate (I) R-loop state. For hybrids of length 13 to 19 bp we again see an ill-constrained barrier, ending when we enter a well-constrained local minimum of a fully formed hybrid at state 20. This last minima defines the open (O) R-loop.

Mismatch penalties are all around 5 k B T (Fig. [4b]), but show reproducible variation along the hybrid. Comparing Fig. [2a, d] with Fig. [4b], it is clear that variations in mismatch penalties in the first 8 states correlate strongly with the measured effective cleavage rate/dissociation constant on targets with a single seed mismatch at the corresponding hybrid position. It is not clear if these variations are due to varying interactions with the protein, or reflects the fact that the possible mismatch types vary with position. In Fig. [4c] we show the remaining rates needed to predict Cas9 cleavage activity at any target, time, and Cas9-sgRNA concentration (see Methods).

R-loop dynamics captures single-molecule experiments

The recent direct observation of the R-loop dynamics between metastable states[42] allows us to further test our model against quantitative single-molecule data. To this end, we define a coarse-grained model (Fig. [5a]) and calculate the effective rates between metastable states from our microscopic free-energy landscapes (see Methods). In Supplementary Fig. [2] we show that predictions based on our coarse-grained model replicate those of the microscopic model.

Using effective rates between metastable states, we can rationalize the broad strokes of Cas9 fidelity by considering a few important examples[42]. For on-targets (Fig. [5b]), the transition between the PAM bound state and the intermediate R-loop state is reversible ([5c]). Complexes that enter the intermediate state typically also enter the fully opened state ( Mismatches between the target DNA and the sgRNA have differential effects on R-loop propagation depending on position. A PAM-proximal mismatch (position 1–8) (Fig. [5d]) strongly suppresses the rate of transition from a closed to intermediate R-loop state (Fig. [5e]). In contrast, a PAM-distal mismatch (position 12–17) (Fig. [5f]) limits the effective rate of cleavage by reducing the intermediate to open transition rate (Fig. [5g]), and allowing for re-closure of the R-loop before entering the open state ( These observations are in agreement with the experimental observation[42], and in Fig. [5c, e] we use purple triangles to indicate measured rates[42] when available at zero torque. We quantitatively predict the conversion rates out of the intermediate R-loop state. The model also captures the position of the on-target intermediate state as being around hybrid length 9-12. Our model does not capture the rate of the open to intermediate transition, and future work will have to determine if this is due to a difference in experimental conditions or because our choice of training data is ill-suited to determine the free energies past the intermediate state. Our model predicts rates on all off-targets, and so extends and refines the long-established rule of thumb that off-target rejection in the PAM proximal seed requires only one mismatch, while off-target rejection outside the seed region requires multiple mismatches[10]. In particular, our model quantifies the intermediate activity resulting from PAM distal mismatch, and so enables prediction of activity titration.

R-loop dynamics resembles conformational dynamics

Next, we wondered what structural properties of Cas9 give rise to the free-energy landscape of Fig. [4a]. A comparison between DNA-bound and unbound Cas9-sgRNA structures have revealed that Cas9 repositions its HNH and RuvC nuclease domains to catalyze cleavage[45],[60],[61]. Ensemble FRET experiments detected two dominant Cas9 conformers with distinct HNH states[50], and single-molecule FRET studies have identified a third intermediate conformer[51],[53],[54]. The relative position and occupancy of the HNH states is affected by R-loop mismatches[51],[53],[54], and Ivanov et al.[42] suggest that the intermediate R-loop state is linked to the intermediate structural state seen in FRET experiments[51]. To test this hypothesis, we mimicked the experiments of Dagdas et al.[51], and considered the time evolution of the occupancy of our metastable R-loop states for two target sequences (Fig. [6]). The HNH-domain completes its conformational change within seconds after Cas9-sgRNA binds to on-target DNA[51], and our model demonstrates a similar behavior for R-loop progression (Fig. [6a]). The intermediate structural state is visited only transiently[51], as is the intermediate R-loop state in our model (Fig. [6a]). Compared to the on-target, PAM-distal mismatches maintain the entry rate into the intermediate structural state, while increasing the time spent in this state[51]; again in close agreement with our findings for the intermediate and open metastable R-loop states in the presence of a PAM distal mismatch (Fig. [6b]). Taken together, our model supports the notion that the intermediate R-loop state is linked to the intermediate structural state seen in FRET experiments.

Kinetic modelling improves genome-wide off-target prediction

Current methods[12],[14],[20]–[25],[28],[43] for identifying strong off-targets rank genomic sequences according to various measures of activity. They do not quantitatively predict biochemically measurable parameters, nor do they normally capture changes in conditions or activity over time. Our approach overcomes these limitations, and we do not suggest that these benefits should be abandoned in order to construct a binary off-target classifier. Still, to strengthen the case for including the full non-equilibrium nature of the problem in any Cas9 modelling, we reduce our quantitative kinetic model to a binary classifier (referred to as kinetic classifier) and test how well it performs against three established state-of-the-art off-target predictors: a recent benchmarking of models[28] shows the CRISPRoff classifier to outperform the competition, so we first test against this tool; second, we test against the more recent uCRISPR[24] tool, which is based on hybrid energetics and has not been tested against CRISPRoff; lastly, we test against the Cutting Frequency Determination (CFD) score[12], since it is a much-used tool for off-target classification. To compare our model against the three selected off-target classifiers, we choose to rank all genomic sites based on cleavage activity in the low enzyme-concentration limit (see Methods). We make our comparison over all canonical PAM sites in the human genome. True positive off-targets are collected from sequencing-based cleavage experiments that used industry-standard sgRNAs and reported multiple off-target cleavage sites[35]–[38],[40],[41],[62] (Supplementary Table [1]). We tested how well our kinetic model’s ranking of activity compares to that of the CFD score[12], CRISPRoff[28], and uCRISPR[24]. For each sgRNA, we separately tested the models by using the union (sites found in any experiment) and intersection (sites found in every experiment) sets of the reported off-target sites as true positives. We perform precision-recall (PR) analysis (Supplementary Fig. [3]) rather than using receiver-operator characteristics (Supplementary Fig. [4]) since the datasets are highly unbalanced, with many more true negatives than true positives. Figure [7a] shows the PR curve when models are tested against the union of all reported off-targets while targeting the HBB gene. As the threshold for what is judged a strong off-target is swept, PR curves display the fraction of predicted off-targets that are found experimentally (precision) against the fraction of experimentally found off-targets that are also predicted (recall). Our kinetic classifier typically produces higher precision for all recalls, outperforming the other classifying schemes for the union set on the HBB gene. More importantly, the kinetic classifier also outperforms the leading off-target predictors for highly-mismatched genomic off-targets of other sgRNAs: performing best on the majority of targets in every pairwise matchup on both union (Fig. [7b, c]) and intersection (Fig. [7d, e]) sets, and irrespectively of if max. F1 or area under the curve (AUC) scores are used.

Discussion

Training our model (Fig. [1]) of Sp Cas9 target activity on moderately mismatched targets, we extract the physical parameters (Fig. [4]) that control activity on any target (Figs. [2] and [3]). Going beyond present-day binary off-target classification schemes, we quantitatively predict cleavage and binding activity as a function of both time and Sp Cas9-sgRNA concentration. We show that Sp Cas9’s targeting reaction contain an intermediate R-loop state, with both position and conversion rates that agree with single-molecule experiments[42] (Fig. [5]). Mismatches affect the dynamics of the R-loop states (Fig. [6]) in a manner similarity to how they affect the configurational states of Sp Cas9’s nuclease domains[42],[51],[53]. Based on this, we lend support to the notion that R-loop formation is tightly coupled to protein conformation—pointing toward the relevant structure-function relation for the most important RNA-guided nuclease in use today. Though our model captures the abundant low-activity off-targets that are discarded by binary classifiers, we sought to demonstrate the general utility of kinetic modelling by reducing our quantitative activity predictor to a binary classification tool. The resulting kinetic classifier outperforms established state-of-the-art classification tools on canonical PAM sites in the human genome (Fig. [7]). In a recent study, Jost et al.[5] demonstrated that a series of mismatched guides can be used to titrate gene expression using CRISPRa/CRISPRi. Wildtype Sp Cas9 can also be (effectively) inactivated with PAM-distal mismatches in the guide[63]. Our model can guide such titration of Sp Cas9-sgRNA inactivation by careful placement of mismatches. Our approach can also be used to calculate the total off-target activity over a genome, and so inform the design of sgRNAs for novel gene targets. For simplicity and robustness, we built our model to exclude mismatch type parameters. This allows for extensive training using datasets based on a single guide sequence and off-target DNAs containing up to two mismatches. The limited set of adjustable model parameters (44 in total) and efficient data usage (422 data points used for training) does not seem to limit the model’s applicability (Figs. [2], [3], [7]). The success of our low-complexity model strongly suggest that the path to increased predictive power and therapeutic relevance runs through bottom-up modelling of RNA-guided nucleases in kinetic terms. Taken together, we have shown that our mechanistic and kinetic model gives biophysical insight and quantitative predictive power far beyond the training sets. This predictive power is only expected to increase when including sequence features and allowing for alternative PAM sequences in future modelling efforts. Sp Cas9 is only one of many RNA-guided nucleases with biotechnological applications, and other CRISPR associated nucleases (such as Cas12a, Cas13 and Cas14) offer a diversified genome-engineering toolkit[15],[64]–[69]. These nucleases can all be characterized with our approach, and it will be especially interesting to compare the free-energy landscape of our Sp Cas9 benchmark to that of engineered[41],[54],[70] and natural (e.g. N. meningitides Cas9[71]) high-fidelity Cas9 variants. —

Methods

Modelling of the (d)Cas9 targeting reaction

We consider a single DNA target sequence with a PAM, in contact with (d)Cas9-sgRNA in solution at fixed concentration (Fig. [1a]). (d)Cas9-sgRNA binding to the PAM site is assumed to be first order,
—
where [Cas9-sgRNA] is the concentration of active complexes relative to some reference concentration (we use 1 nM). Binding is followed by a Cas9-mediated strand exchange reaction between sgRNA and the DNA. Once a 20 bp hybrid is formed, Cas9 can cleave the DNA, while dCas9 cannot. We model the targeting recognition as a stochastic hopping process along a sequence of states: target unbound (n = −1), PAM bound (t. The evolution of probabilities is captured by the Master Equation
—
where [1a]), and defining
—
The Master Equation has the formal solution
—
which can be computed numerically, given any set of rates

Parameter reduction

Based on the mechanistic-model assumption 1, we average the data over mismatch types (see below), and only keep track of if there is a match or a mismatch at every position. Model assumption 3 means that the model of dCas9 is the same as for Cas9, but with Denote the free energy of any state n with F n , and imagine that states n and
—
The above relationships tie rates to free-energy differences through
—
Using
—
Here
—
Here
—
The model is now parameterized it in terms of 41 free energies (

Predicting NucleaSeq cleavage rates

To produce predications for training and validation, we model experimental setups. To model NucleaSeq data[15], we use the solution to the Master Equation to calculate the expected cleaved fraction at any complementarity pattern. NucleaSeq is performed by exposing targets to saturating concentrations of Cas9-sgRNA, which we model by setting t = 0 s, 12 s, 60 s, 180 s, 600 s, 1800 s, 6000 s, 18000 s, and 60000 s, and fit-out a single effective cleavage rate

Predicting CHAMP association constants

We model the CHAMP experiments[15],[31] by calculating the bound fraction (
—
to fit out an effective association constant

Predicting HiTS-FLIP association rates

To predict measured association rates in the HiTS-FLIP experiment[11], we assume the recorded fluorescence signal to be proportional to our calculated bound fraction of dCas9-sgRNA, when starting with the probabilities

Predicting HiTS-FLIP dissociation rates

To predict measured dissociation rates in the HiTS-FLIP experimen[11], we again compared the fluorescence signal to our calculated bound fraction of dCas9, starting with the probabilities

Averaging over mismatch types

Our model does not account for mismatch types, and for training we need to average over all experimentally measured mismatch sequences
—
Here
—
Here
—

Cost function

We look to simultaneously optimize our predictions of both effective cleavage rates from NucleaSeq (
—
by summing log deviations
—
In the above
—

Simulated annealing

The Simulated Annealing algorithm[59] is commonly used for high-dimensional optimization problems. We optimize with respect to model parameters
—
In the above, [4].

Calculating coarse-grained transition rates

First we find the intermediate state on every possible target. As the central-local minimum in free energy (Fig. [4a]) can be slightly displaced by mismatches on off-targets, we seek the free-energy minimum [5a], we consider the first passage between metastable states. Take for example the passage from the PAM-bound state (
—
and
—
The effective transition rate
—
The same process was used to calculate all other rates of directly transitioning between meta-stable states, repeated over every target sequence.

Constructing a binary off-target predictor

We rank all canonical PAM sites in the human genome according to their relative cleavage rate in the low concentration limit. In this limit, the cleavage rate is given by the PAM binding rate times the probability to cleave once the PAM site is bound. As the PAM binding rate is not expected to depend on the sgRNA sequence [30],
—
Here

Statistics & Reproducibility

Only experimental data giving physical positive values for mismatch-averaged rates and association constants were included in the correlation analysis. See Supplementary Data [1].

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Acknowledgements

We would like to thank Kristian Blom, Diewertje Dekker, and Sonny de Jong for valuable discussions and/or their help during the project. We also thank the members of the Chirlmin Joo lab and Stan Brouns lab for valuable discussions. We thank Evan Boyle for sharing data and answering all our questions. This work was supported by: Netherlands Organization for Scientific Research (NWO) (FOM-140), B.E.M.; Zwaartekracht NanoFront, NWO M.K.; Parents in KIND program, The Kavli Institute of Nanoscience Delft/the Department of Bionanoscience at TU Delft/through a Spinoza Prize awarded to M. Dogterom, M.D.; University of Texas College of Natural Sciences Catalyst award and the Welch Foundation (F-1808) I.J.F.; U.S. National Institute of Health (R01GM124141, F32AG053051) I.J.F. and S.K.J.

Author contributions

B.E.M. and M.K.: Designed and performed the research, and wrote the manuscript K.v.d.S. and C.v.d.S.: Performed the research. S.K.J.: Provided data, and wrote manuscript J.H.: Provided data, and wrote manuscript I.J.F.: Provided data, and wrote manuscript M.D.: Conceived of the project, designed the research, and wrote the manuscript.

Data availability

The data supporting the findings of this study are available from the corresponding authors upon reasonable request. Mismatch averaged experimental data used for training and validation (Figs. [2] and [3]), estimated microscopic parameters (Fig. [4]), and genome wide off-target classification evaluation (Fig. [7b–e]), are all provided as Supplementary Data [1].

Code availability

The code enabling quantitative off-target activity prediction for any guide-target pair is available on our GitLab page (https://gitlab.tudelft.nl/depken_group/SpCas9_kinetic_model_dashboard). There you will also find a small dashboard application, allowing time resolved activity predictions given a particular sequence and enzyme concentration. A clone of the repository at publication is also permanently available at 10.5281/zenodo.5790798. The purpose made optimization code will be made available upon request.

References

1. Sander JD, Joung JK. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 2014;32:347-350. doi:10.1038/nbt.2842

2. Wang H, La Russa M, Qi LS. CRISPR/Cas9 in Genome Editing and Beyond. Annu. Rev. Biochem. 2016;85:227-264. doi:10.1146/annurev-biochem-060815-014607

3. Chen B, et al. Dynamic imaging of genomic loci in living human cells by an optimized CRISPR/Cas system. Cell. 2013;155:1479-1491. doi:10.1016/j.cell.2013.12.001

4. Gilbert LA, et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell. 2013;154:442. doi:10.1016/j.cell.2013.06.044

5. Jost M, et al. Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs. Nat. Biotechnol. 2020;38:355-364. doi:10.1038/s41587-019-0387-5

6. Niu D, et al. Inactivation of porcine endogenous retrovirus in pigs using CRISPR-Cas9. Science. 2017;357:1303-1307. doi:10.1126/science.aan4187

7. Hammond A, et al. A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae. Nat. Biotechnol. 2016;34:78-83. doi:10.1038/nbt.3439

8. Amoasii L, et al. Gene editing restores dystrophin expression in a canine model of Duchenne muscular dystrophy. Science. 2018;362:1-6. doi:10.1126/science.aau1549

9. Park CY, et al. Functional Correction of Large Factor VIII Gene Chromosomal Inversions in Hemophilia A Patient-Derived iPSCs Using CRISPR-Cas9. Cell Stem Cell. 2015;17:213-220. doi:10.1016/j.stem.2015.07.001

10. Jinek M, et al. A Programmable Dual-RNA-Guided DNA Endonuclease in Adaptive Bacterial Immunity. Science. 2012;337:816-822. doi:10.1126/science.1225829

11. Boyle EA, et al. High-throughput biochemical profiling reveals sequence determinants of dCas9 off-target binding and unbinding. Proc. Natl Acad. Sci. 2017;114:5461-5466. doi:10.1073/pnas.1700557114

12. Doench JG, et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 2016;34:184-191. doi:10.1038/nbt.3437

13. Fu Y, et al. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat. Biotechnol. 2013;31:822-826. doi:10.1038/nbt.2623

14. Hsu PD, et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 2013;31:827-832. doi:10.1038/nbt.2647

15. Jones SK Jr, et al. Massively parallel kinetic profiling of natural and engineered CRISPR nucleases. Nat. Biotechnol. 2021;39:84-93. doi:10.1038/s41587-020-0646-5

16. Kim D, Luk K, Wolfe SA, Kim J-S. Evaluating and Enhancing Target Specificity of Gene-Editing Nucleases and Deaminases. Annu. Rev. Biochem. 2019;88:191-220. doi:10.1146/annurev-biochem-013118-111730

17. Pattanayak V, et al. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat. Biotechnol. 2013;31:839-843. doi:10.1038/nbt.2673

18. Tsai SQ, Joung JK. Defining and improving the genome-wide specificities of CRISPR-Cas9 nucleases. Nat. Rev. Genet. 2016;17:300-312. doi:10.1038/nrg.2016.28

19. Cullot G, et al. CRISPR-Cas9 genome editing induces megabase-scale chromosomal truncations. Nat. Commun. 2019;10:1-14. doi:10.1038/s41467-019-09006-2

20. Labun K, Montague TG, Gagnon JA, Thyme SB, Valen E. CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 2016;44:W272-W276. doi:10.1093/nar/gkw398

21. Heigwer F, Kerr G, Boutros M. E-CRISP: Fast CRISPR target site identification. Nat. Methods. 2014;11:122-123. doi:10.1038/nmeth.2812

22. Listgarten J, et al. Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nat. Biomed. Eng. 2018;2:38-47. doi:10.1038/s41551-017-0178-6

23. Chuai G, et al. DeepCRISPR: Optimized CRISPR guide RNA design by deep learning. Genome Biol. 2018;19:1-18. doi:10.1186/s13059-018-1459-4

24. Zhang D, Hurst T, Duan D, Chen S-J. Unified energetics analysis unravels SpCas9 cleavage activity for optimal gRNA design. Proc. Natl Acad. Sci. 2019;116:8693-8698. doi:10.1073/pnas.1820523116

25. Stemmer M, Thumberger T, Del Sol Keyer M, Wittbrodt J, Mateo JL. CCTop: An intuitive, flexible and reliable CRISPR/Cas9 target prediction tool. PLoS One. 2015;10:1-11. doi:10.1371/journal.pone.0124633

26. Tycko J, Myer VE, Hsu PD. Methods for Optimizing CRISPR-Cas9 Genome Editing Specificity. Mol. Cell. 2016;63:355-370. doi:10.1016/j.molcel.2016.07.004

27. Farasat I, Salis HM. A Biophysical Model of CRISPR/Cas9 Activity for Rational Design of Genome Editing and Gene Regulation. PLoS Comput. Biol. 2016;12:1-33. doi:10.1371/journal.pcbi.1004724

28. Alkan F, Wenzel A, Anthon C, Havgaard JH, Gorodkin J. CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters. Genome Biol. 2018;19:177. doi:10.1186/s13059-018-1534-x

29. Bisaria N, Jarmoskaite I, Herschlag D. Lessons from Enzyme Kinetics Reveal Specificity Principles for RNA-Guided Nucleases in RNA Interference and CRISPR-Based Genome Editing. Cell Syst. 2017;4:21-29. doi:10.1016/j.cels.2016.12.010

30. Klein M, Eslami-Mossallam B, Arroyo DG, Depken M. Hybridization Kinetics Explains CRISPR-Cas Off-Targeting Rules. Cell Rep. 2018;22:1413-1423. doi:10.1016/j.celrep.2018.01.045

31. Jung C, et al. Massively Parallel Biophysical Analysis of CRISPR-Cas Complexes on Next Generation Sequencing Chips. Cell. 2017;170:35-47.e13. doi:10.1016/j.cell.2017.05.044

32. O’Geen H, et al. A genome-wide analysis of Cas9 binding specificity using ChIP-seq and targeted sequence capture. Nucleic Acids Res. 2015;43:3389-3404. doi:10.1093/nar/gkv137

33. Kuscu C, Arslan S, Singh R, Thorpe J, Adli M. Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease. Nat. Biotechnol. 2014;32:677-683. doi:10.1038/nbt.2916

34. Wu X, et al. Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells. Nat. Biotechnol. 2014;32:670-676. doi:10.1038/nbt.2889

35. Cameron P, et al. Mapping the genomic landscape of CRISPR-Cas9 cleavage. Nat. Methods. 2017;14:600-606. doi:10.1038/nmeth.4284

36. Tsai SQ, et al. CIRCLE-seq: A highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets. Nat. Methods. 2017;14:607-614. doi:10.1038/nmeth.4278

37. Kim D, et al. Digenome-seq: Genome-wide profiling of CRISPR-Cas9 off-target effects in human cells. Nat. Methods. 2015;12:237-243. doi:10.1038/nmeth.3284

38. Tsai SQ, et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat. Biotechnol. 2015;33:187-198. doi:10.1038/nbt.3117

39. Frock RL, et al. Genome-wide detection of DNA double-stranded breaks induced by engineered nucleases. Nat. Biotechnol. 2015;33:179-188. doi:10.1038/nbt.3101

40. Yan WX, et al. BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks. Nat. Commun. 2017;8:1-9. doi:10.1038/ncomms15058

41. Slaymaker IM, et al. Rationally engineered Cas9 nucleases with improved specificity. Science. 2016;351:84-88. doi:10.1126/science.aad5227

42. Ivanov IE, et al. Cas9 interrogates DNA in discrete steps modulated by mismatches and supercoiling. Proc. Natl Acad. Sci. U.S.A. 2020;117:5853-5860. doi:10.1073/pnas.1913445117

43. Haeussler M, et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016;17:1-12. doi:10.1186/s13059-016-1012-2

44. Anders C, Niewoehner O, Duerst A, Jinek M. Structural basis of PAM-dependent target DNA recognition by the Cas9 endonuclease. Nature. 2014;513:569-573. doi:10.1038/nature13579

45. Jiang F, Zhou K, Gressel S, Doudna JA. A Cas9 guide RNA complex preorganized for target DNA recognition. Science. 2015;348:1477-1482. doi:10.1126/science.aab1452

46. Josephs EA, et al. Structure and specificity of the RNA-guided endonuclease Cas9 during DNA interrogation, target binding and cleavage. Nucleic Acids Res. 2015;43:8924-8941. doi:10.1093/nar/gkv892

47. Rutkauskas M, et al. Directional R-loop formation by the CRISPR-Cas surveillance complex Cascade provides efficient off-target site rejection. Cell Rep. 2015;10:1534-1543. doi:10.1016/j.celrep.2015.01.067

48. Szczelkun MD, et al. Direct observation of R-loop formation by single RNA-guided Cas9 and Cascade effector complexes. Proc. Natl Acad. Sci. 2014;111:9798-9803. doi:10.1073/pnas.1402597111

49. Xiao Y, et al. Structure Basis for Directional R-loop Formation and Substrate Handover Mechanisms in Type I CRISPR-Cas System. Cell. 2017;170:48-60.e11. doi:10.1016/j.cell.2017.06.012

50. Sternberg SH, LaFrance B, Kaplan M, Doudna JA. Conformational control of DNA target cleavage by CRISPR-Cas9. Nature. 2015;527:110-113. doi:10.1038/nature15544

51. Dagdas YS, Chen JS, Sternberg SH, Doudna JA, Yildiz A. A conformational checkpoint between DNA binding and cleavage by CRISPR-Cas9. Sci. Adv. 2017;3:1-9. doi:10.1126/sciadv.aao0027

52. Sung K, Park J, Kim Y, Lee NK, Kim SK. Target Specificity of Cas9 Nuclease via DNA Rearrangement Regulated by the REC2 Domain. J. Am. Chem. Soc. 2018;140:7778-7781. doi:10.1021/jacs.8b03102

53. Yang M, et al. The Conformational Dynamics of Cas9 Governing DNA Cleavage Are Revealed by Single-Molecule FRET. Cell Rep. 2018;22:372-382. doi:10.1016/j.celrep.2017.12.048

54. Chen JS, et al. Enhanced proofreading governs CRISPR-Cas9 targeting accuracy. Nature. 2017;550:407-410. doi:10.1038/nature24268

55. Irmisch P, Ouldridge TE, Seidel R. Modeling DNA-Strand Displacement Reactions in the Presence of Base-Pair Mismatches. J. Am. Chem. Soc. 2020;142:11451-11463. doi:10.1021/jacs.0c03105

56. Srinivas N, et al. On the biophysics and kinetics of toehold-mediated DNA strand displacement. Nucleic Acids Res. 2013;41:10641-10658. doi:10.1093/nar/gkt801

57. Sulc P, et al. Modelling toehold-mediated RNA strand displacement. Biophys. J. 2015;108:1238-1247. doi:10.1016/j.bpj.2015.01.023

58. Broadwater DWB, Kim HD. The Effect of Basepair Mismatch on DNA Strand Displacement. Biophys. J. 2016;110:1476-1484. doi:10.1016/j.bpj.2016.02.027

59. Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science. 1983;220:671-680. doi:10.1126/science.220.4598.671

60. Jiang F, et al. Structures of a CRISPR-Cas9 R-loop complex primed for DNA cleavage. Science. 2016;351:867-871. doi:10.1126/science.aad8282

61. Jinek M, et al. Structures of Cas9 endonucleases reveal RNA-mediated conformational activation. Science. 2014;343:1247997. doi:10.1126/science.1247997

62. Kim D, et al. Genome-wide target specificities of CRISPR-Cas9 nucleases revealed by multiplex Digenome-seq. Genome Res. 2016;26:406-415. doi:10.1101/gr.199588.115

63. Dahlman JE, et al. Orthogonal gene knockout and activation with a catalytically active Cas9 nuclease. Nat. Biotechnol. 2015;33:1159-1161. doi:10.1038/nbt.3390

64. Chen JS, et al. CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science. 2018;360:436-439. doi:10.1126/science.aar6245

65. Gootenberg JS, et al. Nucleic acid detection with CRISPR-Cas13a/C2c2. Science. 2017;356:438-442. doi:10.1126/science.aam9321

66. Gootenberg JS, et al. Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6. Science. 2018;360:439-444. doi:10.1126/science.aaq0179

67. Harrington LB, et al. Programmed DNA destruction by miniature CRISPR-Cas14 enzymes. Science. 2018;362:839-842. doi:10.1126/science.aav4294

68. Kim D, et al. Genome-wide analysis reveals specificities of Cpf1 endonucleases in human cells. Nat. Biotechnol. 2016;34:863-868. doi:10.1038/nbt.3609

69. Kleinstiver BP, et al. Genome-wide specificities of CRISPR-Cas Cpf1 nucleases in human cells. Nat. Biotechnol. 2016;34:869-874. doi:10.1038/nbt.3620

70. Kleinstiver BP, et al. High-fidelity CRISPR-Cas9 nucleases with no detectable genome-wide off-target effects. Nature. 2016;529:490-495. doi:10.1038/nature16526

71. Amrani N, et al. NmeCas9 is an intrinsically high-fidelity genome-editing platform. Genome Biol. 2018;19:214. doi:10.1186/s13059-018-1591-1

For the complete references list, please see the full text on PubMed Central.