Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR) sequences and CRISPR-associated (Cas) genes comprise CIRSPR-Cas effector complexes, which have revolutionized gene editing with their ability to target specific genomic loci using CRISPR RNA (crRNA) complementarity. Recognition of double-stranded DNA targets proceeds via DNA unwinding and base pairing between crRNA and the DNA target strand, forming an R-loop structure. Full R-loop extension is a prerequisite for subsequent DNA cleavage. However, the recognition of unintended sequences with multiple mismatches has limited therapeutic applications and is still poorly understood on a mechanistic level. Here we set up ultrafast DNA unwinding experiments on the basis of plasmonic DNA origami nanorotors to study R-loop formation by the Cascade effector complex in real time, close to base-pair resolution. We resolve a weak global downhill bias of the forming R-loop, followed by a steep uphill bias for the final base pairs. We also show that the energy landscape is modulated by base flips and mismatches. These findings suggest that Cascade-mediated R-loop formation occurs on short timescales in submillisecond single base-pair steps, but on longer timescales in six base-pair intermediate steps, in agreement with the structural periodicity of the crRNA–DNA hybrid.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Minimal datasets generated and/or analyzed during this study are available from https://doi.org/10.5281/zenodo.7825921. For figures including molecular structures the following PDB models were used: 4tvx, 5h9e. Source data are provided with this paper.
Code availability
The custom-made code used for the deconvolution of the recorded data as well as the code used to perform the Brownian dynamics simulations is available at https://doi.org/10.5281/zenodo.7849440.
References
Gasiunas, G., Barrangou, R., Horvath, P. & Siksnys, V. Cas9–crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria. Proc. Natl Acad. Sci. USA 109, E2579–E2586 (2012).
Brouns, S. J. J. et al. Small CRISPR RNAs guide antiviral defense in prokaryotes. Science 321, 960–964 (2008).
Jore, M. M. et al. Structural basis for CRISPR RNA-guided DNA recognition by Cascade. Nat. Struct. Mol. Biol. 18, 529–536 (2011).
Xiao, Y. et al. Structure basis for directional R-loop formation and substrate handover mechanisms in type I CRISPR–Cas system. Cell 170, 48–60.e11 (2017).
Szczelkun, M. D. et al. Direct observation of R-loop formation by single RNA-guided Cas9 and Cascade effector complexes. Proc. Natl Acad. Sci. USA 111, 9798–9803 (2014).
Wiedenheft, B. et al. Structures of the RNA-guided surveillance complex from a bacterial immune system. Nature 477, 486–489 (2011).
Rutkauskas, M. et al. Directional R-loop formation by the CRISPR–Cas surveillance complex Cascade provides efficient off-target site rejection. Cell Rep. 10, 1534–1543 (2015).
Klein, M., Eslami-Mossallam, B., Arroyo, D. G. & Depken, M. Hybridization kinetics explains CRISPR–Cas off-targeting rules. Cell Rep. 22, 1413–1423 (2018).
Irmisch, P., Ouldridge, T. E. & Seidel, R. Modeling DNA-strand displacement reactions in the presence of base-pair mismatches. J. Am. Chem. Soc. 142, 11451–11463 (2020).
Rutkauskas, M. et al. A quantitative model for the dynamics of target recognition and off-target rejection by the CRISPR-Cas Cascade complex. Nat. Commun. 13, 7460 (2022).
Pattanayak, V. et al. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat. Biotechnol. 31, 839–843 (2013).
Newton, M. D. et al. DNA stretching induces Cas9 off-target activity. Nat. Struct. Mol. Biol. 26, 185–192 (2019).
Rothemund, P. W. K. Folding DNA to create nanoscale shapes and patterns. Nature 440, 297–302 (2006).
Douglas, S. M. et al. Rapid prototyping of 3D DNA-origami shapes with caDNAno. Nucleic Acids Res. 37, 5001–5006 (2009).
Lebel, P., Basu, A., Oberstrass, F. C., Tretter, E. M. & Bryant, Z. Gold rotor bead tracking for high-speed measurements of DNA twist, torque and extension. Nat. Methods 11, 456–462 (2014).
Huhle, A. et al. Camera-based three-dimensional real-time particle tracking at kHz rates and ångström accuracy. Nat. Commun. 6, 1–8 (2015).
Kosuri, P., Altheimer, B. D., Dai, M., Yin, P. & Zhuang, X. Rotation tracking of genome-processing enzymes using DNA origami rotors. Nature 572, 136–140 (2019).
Ivanov, I. E. et al. Cas9 interrogates DNA in discrete steps modulated by mismatches and supercoiling. Proc. Natl Acad. Sci. USA 117, 5853–5860 (2020).
Marko, J. F. & Siggia, E. D. Statistical mechanics of supercoiled DNA. Phys. Rev. E 52, 2912–2938 (1995).
Brutzer, H., Luzzietti, N., Klaue, D. & Seidel, R. Energetics at the DNA supercoiling transition. Biophys. J. 98, 1267–1276 (2010).
Allemand, J. F., Bensimon, D., Lavery, R. & Croquette, V. Stretched and overwound DNA forms a Pauling-like structure with exposed bases. Proc. Natl Acad. Sci. USA 95, 14152–14157 (1998).
Sinkunas, T. et al. In vitro reconstitution of Cascade-mediated CRISPR immunity in Streptococcus thermophilus. EMBO J. 32, 385–394 (2013).
Songailiene, I. et al. Decision-making in Cascade complexes harboring crRNAs of altered length. Cell Rep. 28, 3157–3166.e4 (2019).
Mulepati, S., Heroux, A. & Bailey, S. Crystal structure of a CRISPR RNA-guided surveillance complex bound to a ssDNA target. Science 345, 1479–1484 (2014).
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. 4, 21–29 (2017).
Jung, C. et al. Massively parallel biophysical analysis of CRISPR–Cas complexes on next generation sequencing chips. Cell 170, 35–47.e13 (2017).
Hayes, R. P. et al. Structural basis for promiscuous PAM recognition in type IE Cascade from E. coli. Nature 530, 499–503 (2016).
Kim, D. et al. Digenome-seq: genome-wide profiling of CRISPR–Cas9 off-target effects in human cells. Nat. Methods 12, 237–243 (2015).
Eslami-Mossallam, B. et al. A kinetic model predicts SpCas9 activity, improves off-target classification, and reveals the physical basis of targeting fidelity. Nat. Commun. 13, 1367 (2022).
Singh, D. et al. Mechanisms of improved specificity of engineered cas9s revealed by single-molecule FRET analysis. Nat. Struct. Mol. Biol. 25, 347–354 (2018).
Barrangou, R. & Doudna, J. A. Applications of CRISPR technologies in research and beyond. Nat. Biotechnol. 34, 933–941 (2016).
Jackson, R. N. et al. Crystal structure of the CRISPR RNA-guided surveillance complex from Escherichia coli. Science 345, 1473–1479 (2014).
Sinkunas, T. et al. Cas3 is a single-stranded DNA nuclease and ATP-dependent helicase in the CRISPR/Cas immune system. EMBO J. 30, 1335–1342 (2011).
Kauert, D. J., Kurth, T., Liedl, T. & Seidel, R. Direct mechanical measurements reveal the material properties of three-dimensional DNA origami. Nano Lett. 11, 5558–5563 (2011).
Lin, C., Perrault, S. D., Kwak, M., Graf, F. & Shih, W. M. Purification of DNA-origami nanostructures by rate-zonal centrifugation. Nucleic Acids Res. 41, e40 (2012).
Gür, F. N., Schwarz, F. W., Ye, J., Diez, S. & Schmidt, T. L. Toward self-assembled plasmonic devices: high-yield arrangement of gold nanoparticles on DNA origami templates. ACS Nano 10, 5374–5382 (2016).
Perrault, S. D. & Chan, W. C. W. Synthesis and surface modification of highly monodispersed, spherical gold nanoparticles of 50–200 nm. J. Am. Chem. Soc. 131, 17042–17043 (2009).
Swoboda, M. et al. Enzymatic oxygen scavenging for photostability without pH drop in single-molecule experiments. ACS Nano 6, 6364–6369 (2012).
Halíř, R. & Flusser, J. Numerically stable direct least squares fitting of ellipses. In Proc. 6th International Conference in Central Europe on Computer Graphics and Visualization (Skala, V. ed) 125–132 (University of West Bohemia, 1998).
Woodside, M. T. & Block, S. M. Reconstructing folding energy landscapes by single-molecule force spectroscopy. Annu. Rev. Biophys. 43, 19–39 (2014).
Zadeh, J. N. et al. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170–173 (2010).
Weizenmann, N. et al. Chemical ligation of an entire DNA origami nanostructure. Nanoscale 13, 17556–17565 (2021).
Howe, D. A., Allan, D. U. & Barnes, J. A. Properties of signal sources and measurement methods. In 35th Annual Frequency Control Symposium 669–716 (IEEE, 1981).
Bryant, Z. et al. Structural transitions and elasticity from torque measurements on DNA. Nature 424, 338–341 (2003).
Wang, A. H.-J. et al. Molecular structure of a left-handed double helical DNA fragment at atomic resolution. Nature 282, 680–686 (1979).
Kriegel, F. et al. Probing the salt dependence of the torsional stiffness of DNA by multiplexed magnetic torque tweezers. Nucleic Acids Res. 45, 5920–5929 (2017).
Acknowledgements
This work was supported by grants from the European Research Council (GA 724863) and the Deutsche Forschungsgemeinschaft (SPP2141, SE 1646/8-1) to R.S. J.M.M. was supported by a postdoctoral fellowship of the Alexander von Humboldt Foundation. We thank J. Ye and U. Kemper for providing TEM images of the nanorotor as well as D. Poppitz for the training and support of the TEM imaging. We further thank P. Irmisch and F. Welzel for critical reading of the manuscript.
Author information
Authors and Affiliations
Contributions
R.S. and D.J.K. designed the study. I.S., T.S. and V.S. provided the purified Cascade. D.J.K. and A.W. designed the nanorotor. M.R. designed the DNA targets. D.J.K., J.M.-M. and A.W. carried out the experiments. D.J.K. analyzed the data. D.J.K., J.M.-M. and M.R. interpreted the results. D.J.K and R.S. and wrote the manuscript. All authors provided comments to the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Structural & Molecular Biology thanks Ailong Ke and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Beth Moorefield, Carolina Perdigoto and Dimitris Typas were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Scheme and TEM images of the nanorotor.
a, Three-dimensional scheme of the nanorotor including the origami rotor arm and a 50 nm AuNP. Blue cylinders represent the individual dsDNA helices. Insets at the top and bottom show enlarged perspectives as well as schematic views of the dsDNA ligation interfaces at each end of the stem, where two complementary ssDNA staple overhangs of neighbouring helices (orange) and a secondary interface oligo form a sticky end for ligation. Other helix ends carry six nucleotide ssDNA staple overhangs (purple) to prevent aggregation between rotor arm structures due to blunt end DNA stacking. The insets in the middle show detailed and schematic views of the attachment of the AuNP to the end of the rotor arm, in which the connecting DNA strands hybridize in a zipper-like configuration. b, TEM overview image of several DNA origami rotor arms and selected images of individual structures (top). c, TEM overview image of DNA origami rotor arms with attached 50 nm AuNPs and selected images of individual structures (top). The nanorotor binds often to protrusions on the AuNP. All TEM images are scaled to the same magnification. The length of the scale bars is 100 nm with 20 nm for the length of each black and white segment. TEM imaging was performed on 3 independent preparations.
Extended Data Fig. 2 Design and construction of the nanorotor DNA construct.
a, Scheme of the construct components including four dsDNA fragments: a digoxigenin-labelled surface attachment handle (1), the Cascade target sequence (2), a long spacer to lift the magnetic bead out of the evanescent field (4) and a biotin-modified bead attachment handle (5), as well as the origami rotor arm structure that carries attachment sites (yellow) for the gold nanoparticle (3). b, Analysis by agarose gel electrophoresis of the individual components of the DNA nanorotor construct (lanes 1–5, numbered according to a) as well as the ligation products from all components before (lane F) and after PEG purification (lane P). Lane M contains a DNA size marker whose dsDNA fragment length is given on the left. In lane F, a yellow arrow marks the band containing the full nanorotor DNA construct. Additional dominant ligation products at 1.0–1.5 kbp are caused by the self-ligation of both handles due to the use of an excess of handles with symmetric ligation overhangs. Low molecular weight products (<1.5 kbp) were removed in the subsequent PEG precipitation step. Remaining side-products were removed upon magnetic bead binding and washing prior to the tweezers experiments. Gel electrophoresis was performed as described previously42 and performed for 3 independent preparations.
Extended Data Fig. 3 Tracking the motion of nanorotor AuNPs and determination of the angular position.
a, Stack of consecutive images of an individual 50 nm AuNP attached to a DNA nanorotor construct recorded at 3947 fps. The image size is 31 px × 31 px corresponding to a field of view of 1.28 µm × 1.28 µm. b, Localization of the AuNP. To determine the position of the AuNP centre, the intensity distribution of a 13 px × 13 px area (pink square) around the pixel with maximum intensity was fitted with a two-dimensional Gaussian function (pink circles, indicating Gaussian intensities of 6000, 5000, 4000, 3000, 2000, 1000 from inside to outside). Measurements on AuNPs stuck to the glass substrate yielded a resolution of 2.7 nm. c, Corrections for magnetic bead fluctuations and drift of the microscope. In parallel to the tracking of the AuNP, we record the positions of the attached magnetic bead and a surface-attached non-magnetic reference bead. The position tracking of the reference bead is used to remove the thermal drift of the microscope. Additionally, thermal fluctuations cause lateral displacements x of the magnetic bead from the equilibrium position. In turn this results in lateral displacements xb of the nanorotor rotation centre. To correct for this, we determined xb from xb=h⁄L⋅x, where L is the distance of the magnetic bead centre from the DNA surface attachment (obtained from bead tracking) and h is the height of the AuNP above the surface for which 118 nm was taken. d, Tracked positions of a nanorotor bound AuNP in the XY plane. Imperfections of the magnet configuration can cause a tilt of the pulling direction with respect to the optical axis, providing an elliptic distortion of the AuNP positions due to the tilt of the rotation plane. A fit of the data with an ellipse is shown in orange. To correct for the tilt of the rotational plane, the short ellipse axis was stretched to the value of the long axis to yield a circular trajectory (shown in red). This correction was applied to all data points in order to obtain correct angular positions of the nanorotor.
Extended Data Fig. 4 Power Spectral Density and Allan Deviation of nanorotors.
a, Power spectral density (PSD) of a nanorotor (black) that was torsionally constrained on the bottom side (next to the Cascade target) in absence of Cascade. The plateau seen at low frequencies demonstrated that the angular nanorotor fluctuations were considerably confined. This confinement was released when dynamic R-loops formed in presence of Cascade (see Fig. 1d, Fig. 2a). The PSD was fitted with a Lorentzian that was further corrected for aliasing and signal integration function (green, see Supplementary Discussion 1). Best-fit parameters for the shown molecule were \({k}_{\mathrm{rot}}=7.5\pm 0.1\,\text{pN nm}/\text{rad},\,{{f}}_{\mathrm{cut}}=158\pm 1\,\text{Hz}\)). b, Allan deviation (red solid line), calculated using overlapping intervals43 and root mean square (RMS) noise after filtering with a sliding average (RMS, blue solid line) of the nanorotor trajectory used in a (in rad and in bp). Dashed lines of the same colour represent theory predictions for the best-fit parameters of the PSD (see Supplementary Discussion 2). The response time of the nanorotor was τ=γ/κ=1.0 ms (purple dashed line). Horizontal dashed lines indicate the noise level at which DNA twist changes of 3 and 1 bp become resolvable assuming a SNR of 3. Intersections of this lines with measured RMS noise provide the corresponding temporal resolution to detect these twist changes of ~4.5 ms for 3 bp and ~54 ms for 1 bp.
Extended Data Fig. 5 Direct torque measurements during DNA twisting using the nanorotor.
a, Experimental setup for torque measurements during DNA twisting. Torque measurements require the nanorotor to be torsionally constrained with respect to both attachments, that is at the surface and at the bead. Twisting the DNA by magnet rotations changes the torque in the molecule, which in turn displaces the nanorotor. Calibrating the torsional stiffness ktot of the nanorotor system (see Extended Data Fig. 4), which is dominated by the linker on the bottom, allows to obtain the torque from the angular displacement of the nanorotor (see Supplementary Discussion 4). b, DNA length and torque during DNA twisting at different forces. Around zero turns, the DNA length remains approximately constant, while the torque is increasing proportionally with the applied turns. From the slope of this linear part, a torsional persistence length of dsDNA of ptor =102 nm was obtained, which is in good agreement with previous reports44. Once a critical positive torque was reached, the DNA buckles, which is seen as a sudden length jump. This is followed by a linear DNA length decrease with applied turns in which the plectonemic writhe structure (see sketch in torque plot) is further expanding. In the plectonemic regime, the torque remains approximately constant. Both the buckling point as well as the torque plateau are force dependent. For the measurement at 5.7 pN, no length decrease is observed, as the DNA undergoes a transition to p-DNA21. When the negative torque falls below −9 pN nm, another torque plateau is reached, where additionally applied negative turns are absorbed by force-independent local melting or z-DNA formation45. These local structural transitions appear to be dynamically changing throughout the molecule. They are typically found in the long DNA spacer above the nanorotor but can transiently appear as well in the dsDNA part below the nanorotor (see transient spikes towards negative nanorotor angles). Measurements are performed at a resolution of 1 pN nm within 38 ms which allowed to measure the torque continuously while twisting, instead of averaging measurements at set twist values46.
Extended Data Fig. 6 Trajectories, energy landscapes and inter-well transition rates for R-loop formation on the different T6 target sequences.
Shown are results measured on a single molecule with a, T6, b, T6-flipG, c, T6-flipT and d, T6-M17 target sequence. For each target are shown: a DNA untwisting trajectory in presence of Cascade (left, light colours represent raw data at 3947 fps, dark colours the data after 100-point adjacent averaging), the sequence of the particular target (with PAM bases in orange, the flipped base positions in purple and mismatches with the crRNA underlined in red), a histogram of the angular positions obtained from all trajectories of the given molecule (middle, top) as well as the corresponding free energy landscapes for R-loop formation (middle, bottom). In the latter, the apparent (black line), the deconvolved (line and scatter points) and the reconvolved (coloured line) free energy landscapes are shown. The apparent energy landscape in absence of Cascade (from which the deconvolution Kernel was obtained) is shown as a purple line. Inter-well transition rates from measured and simulated DNA untwisting trajectories are shown on the right. In the simulations, the R-loop length can be monitored directly from which correct transition rates of the simulations were calculated. Transition rates obtained by the three different ways show a good overall agreement. This validates the determination of realistic inter-well transition rate from experimental trajectories.
Extended Data Fig. 7 Deconvolution of an apparent free energy landscape of R-loop formation exploiting the measured R-loop dynamics.
a, Apparent (black line) and deconvolved (red lines) free energy landscape of R-loop formation obtained for the T6-flipG target. Deconvolved energy landscapes were calculated for different entropy scaling factors b (see legend). The blue dashed line is a linear approximation of the apparent energy landscape between the local maximum at ~7 bp and the minimum at 26 bp. Increasing entropy scaling factors decrease the height of the free energy barriers between the local energy wells. To obtain a deconvolved free energy landscape that supports the measured dynamics of the R-loop formation as well, transition probability plots were used (see b). b, Transition maps revealing the dynamics of R-loop formation. Each transition map shows the probability distribution describing the length change of R-loops (vertical axis) of a given apparent length at time t (horizontal axis) after various time delays Δ\(t\) (2, 5, 10 and 25 ms). Shown are maps for the measurement in a (upper row) as well as for the Brownian dynamics simulations using the deconvolved energy landscapes obtained for different energy scaling values b and single base pair stepping rates kstep. While all b values well describe the intra-segment diffusion at 2 ms, significant differences are seen for the inter-segment dynamics at larger timescales, for which b=10 and kstep=7000 s−1 provide the parameter fit that best describes the measured data. c, Total residue (RMS) between simulated and measured transition plots (for all time differences) shown for different parameter sets of b and kstep. The RMS for a given parameter set was calculated from the sum of the RMS values at Δt = 2, 5, 10 and 25 ms). Coloured circles correspond to the 3 parameter sets highlighted/chosen in a and c. Across all targets we obtained a single base pair stepping rate of \({k}_{\mathrm{step}}=6000\pm 2000\,{s}^{-1}\) (s.d.).
Extended Data Fig. 8 Height of intersegment and mismatch barriers in the energy landscape.
a, (Top) Free energy barriers for intersegment transitions for the T6, T6-flipG and T6-flipT targets (see legend). (Bottom) The height of the transition barriers of a particular free energy landscape was determined from its difference to a barrier-free reference obtained by connecting the minima of adjacent free energy wells (dashed lines). The resulting barriers were approximately centred on the flipped base positions (dotted grey lines). Shaded areas correspond to s.e.m. The landscapes are shifted along the energy axis for better comparison. b, Free energy penalty introduced by a single C:A mismatch at position 17 between the target strand and crRNA. Shown are the apparent energy landscapes of the T6 (black) and the T6-M17 (green) targets as well as the difference between both apparent (purple) and both deconvolved (orange) energy landscapes. The difference between the two landscapes reveals a pronounced mismatch penalty, that is an offset of the energy values for lengths larger 17 bp, of 4.7 ± 0.4 kBT (see difference between the blue dashed lines). The landscapes are shifted along the energy axis for better comparison.
Extended Data Fig. 9 Mean-squared-displacement (MSD) of R-loop length fluctuations over time in comparison to simulations.
Mean squared displacement of the bare nanorotor (black) as well as of the R-loop length fluctuations as a function of time \(\left\langle {\left(\varphi \left(t\right)-\varphi \left(0\right)\right)}^{2}\right\rangle\) for starting positions φ(0) of 15, 21 and 26 bp corresponding to the 5-bp segments 3, 4 and 5, respectively. Shown are MSDs from the measurement on a molecule with a T6 target (solid lines) and Brownian dynamics simulation using the best-fit parameters: b=20 and kstep=7000, for its R-loop dynamics (dashed lines). The measured mean escape times (dotted vertical lines) from the wells 3, 4 and 5 were 31, 8.9 and 74 ms respectively. The good agreement between measurement and simulation for the MSD plots provides strong support for the obtained energy landscape and the stepping rate kstep. In particular, the agreement for short times in between the nanorotor response time (grey vertical line) and the corresponding escape times from the wells strongly supports that the intrawell R-loop dynamics is governed by a random walk-like process of 1 bp step size.
Extended Data Fig. 10 R-loop free energy landscapes before and after locking for the T6, T2 and T1 targets.
a-d, (left) R-loop length trajectories including a locking event measured on targets with (a) six, (b) two, (c) one and (d) zero terminal mismatches (T6, T2, T1 and WT targets). The targets sequence is shown below (Colours as in Extended Data Fig. 6). Data was taken at 3947 fps (light colours) and smoothed with a 100-point sliding average (dark colours). The extensive R-loop length fluctuations before locking became suddenly quenched upon irreversible locking indicating a tightly constrained R-loop length (grey shaded area). (Right) Histograms of the measured R-loop lengths as well as apparent (solid lines) and deconvolved (lines and circles) energy landscapes before (black, red, blue and green colours) and after locking (purple colour). For the T2, T1 and WT targets, a pronounced shift of the predominant R-loop length from 27 to 30 bp (T2, T1 target) or 32 bp (WT target) is observed upon locking. For the T6 target, the predominant R-loop length of ~26 bp remains unchanged. e, Deconvolved free energy landscapes of the locked state for the T6, T2 and T1 targets (solid lines). We added a mismatch penalty of 2.26 kBT to the T2 and T1 landscapes for each additional mismatch that the T6 target comprises (dashed lines) to reconstruct the T6 energy landscape. The landscapes are shifted along the energy axis for better comparison. f, g, Scheme of the observed free energy landscapes of R-loop formation including the unbound and the locked state (blue and grey shaded areas, respectively) without (f) and with (g) an internal mismatch (before position 27). For a fully matching target the terminal barrier is slightly lower than the initial PAM binding barrier such that full R-loop formation and locking occur at a higher probability than R-loop collapse. The local penalty induced by an internal mismatch also lifts the terminal barrier such that R-loop collapse becomes favoured over full R-loop formation and locking, typically resulting in rejection of the target. Such energetics of R-loop formation is indicative of a kinetic target discrimination mechanism25. Thus, similar heights of the initial and terminal barriers on a fully matching target ensure highly efficient R-loop formation and high specificity at the same time.
Supplementary information
Supplementary Information
Supplementary Videos 1 and 2, Figs. 1 and 2, Discussions 1–8, Tables 1–4 and References.
Supplementary Video 1
Video showing circular movement of a nanorotor-attached AuNP recorded at 3,947 f.p.s. and sampled down to 40 f.p.s.
Supplementary Video 2
Video showing movement of a nanorotor-attached AuNP recorded at 3,947 f.p.s. slowed down by a factor of 100.
Source data
Source Data Fig. 1
Data points of shown graphs.
Source Data Fig. 2
Data points of shown graphs.
Source Data Fig. 3
Data points of shown graphs.
Source Data Fig. 4
Data points of shown graphs.
Source Data Extended Data Fig. 1
Raw TEM images of shown views.
Source Data Extended Data Fig. 2
Raw gel image.
Source Data Extended Data Fig. 3
Data points of images shown in b and d.
Source Data Extended Data Fig. 4
Data points of shown graphs.
Source Data Extended Data Fig. 5
Data points of shown graphs.
Source Data Extended Data Fig. 6
Data points of shown graphs.
Source Data Extended Data Fig. 7
Data points of shown graphs.
Source Data Extended Data Fig. 8
Data points of shown graphs.
Source Data Extended Data Fig. 9
Data points of shown graphs.
Source Data Extended Data Fig. 10
Data points of shown graphs.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kauert, D.J., Madariaga-Marcos, J., Rutkauskas, M. et al. The energy landscape for R-loop formation by the CRISPR–Cas Cascade complex. Nat Struct Mol Biol 30, 1040–1047 (2023). https://doi.org/10.1038/s41594-023-01019-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41594-023-01019-2
This article is cited by
-
Short-range translocation by a restriction enzyme motor triggers diffusion along DNA
Nature Chemical Biology (2024)
