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Computational design of anti-CRISPR proteins with improved inhibition potency

Nature Chemical Biology volume 16, pages 725–730 (2020)Cite this article

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Abstract

Anti-CRISPR (Acr) proteins are powerful tools to control CRISPR–Cas technologies. However, the available Acr repertoire is limited to naturally occurring variants. Here, we applied structure-based design on AcrIIC1, a broad-spectrum CRISPR–Cas9 inhibitor, to improve its efficacy on different targets. We first show that inserting exogenous protein domains into a selected AcrIIC1 surface site dramatically enhances inhibition of Neisseria meningitidis (Nme)Cas9. Then, applying structure-guided design to the Cas9-binding surface, we converted AcrIIC1 into AcrIIC1X, a potent inhibitor of the Staphylococcus aureus (Sau)Cas9, an orthologue widely applied for in vivo genome editing. Finally, to demonstrate the utility of AcrIIC1X for genome engineering applications, we implemented a hepatocyte-specific SauCas9 ON-switch by placing AcrIIC1X expression under regulation of microRNA-122. Our work introduces designer Acrs as important biotechnological tools and provides an innovative strategy to safeguard CRISPR technologies.

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Fig. 1: Improving the efficacy of Acr proteins by protein engineering.
Fig. 2: Domain insertion into AcrIIC1 yields a highly potent NmeCas9 inhibitor.
Fig. 3: Enhanced potency of the AcrIIC1-mCherry chimeric inhibitor arises from multiple factors.
Fig. 4: Structure-guided design of AcrIIC1X, an Acr protein targeting SauCas9.
Fig. 5: Characterization of AcrIIC1X, a designer SauCas9 inhibitor.

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Data availability

Vectors encoding AcrIIC1X, AcrIIC1X* as well as the AcrIIC1-mCherry chimera will be made available via Addgene (plasmids nos. 128112–128114). Annotated vector sequences (GenBank files) are provided in the Supplementary Data 1. Additional data can be obtained from the corresponding authors on reasonable request.

Code availability

Code and data for the computational domain assembly and the design of the improved AcrIIC1 point mutants will be made available on GitHub (https://github.com/LPDI-EPFL/AcrIIC1X).

References

  1. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bondy-Denomy, J., Pawluk, A., Maxwell, K. L. & Davidson, A. R. Bacteriophage genes that inactivate the CRISPR/Cas bacterial immune system. Nature 493, 429–432 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Bondy-Denomy, J. et al. Multiple mechanisms for CRISPR–Cas inhibition by anti-CRISPR proteins. Nature 526, 136–139 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Pawluk, A., Bondy-Denomy, J., Cheung, V. H., Maxwell, K. L. & Davidson, A. R. A new group of phage anti-CRISPR genes inhibits the type I-E CRISPR-Cas system of Pseudomonas aeruginosa. mBio 5, e00896 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Basgall, E. M. et al. Gene drive inhibition by the anti-CRISPR proteins AcrIIA2 and AcrIIA4 in Saccharomyces cerevisiae. Microbiology 164, 464–474 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bubeck, F. et al. Engineered anti-CRISPR proteins for optogenetic control of CRISPR–Cas9. Nat. Methods 15, 924–927 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Hoffmann, M. D. et al. Cell-specific CRISPR–Cas9 activation by microRNA-dependent expression of anti-CRISPR proteins. Nucleic Acids Res. 47, e75 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nakamura, M. et al. Anti-CRISPR-mediated control of gene editing and synthetic circuits in eukaryotic cells. Nat. Commun. 10, 194 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Shin, J. et al. Disabling Cas9 by an anti-CRISPR DNA mimic. Sci. Adv. 3, e1701620 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Aschenbrenner, S. et al. Coupling Cas9 to artificial inhibitory domains enhances CRISPR-Cas9 target specificity. Sci. Adv. 6, eaay0187 (2019).

    Article  CAS  Google Scholar 

  13. Rauch, B. J. et al. Inhibition of CRISPR-Cas9 with bacteriophage proteins. Cell 168, 150–158.e10 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Pawluk, A. et al. Inactivation of CRISPR–Cas systems by anti-CRISPR proteins in diverse bacterial species. Nat. Microbiol 1, 16085 (2016).

    Article  CAS  PubMed  Google Scholar 

  15. Pawluk, A. et al. Naturally occurring off-switches for CRISPR-Cas9. Cell 167, 1829–1838 e9 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lee, J. et al. Potent Cas9 inhibition in bacterial and human cells by AcrIIC4 and AcrIIC5 anti-CRISPR proteins. mBio 9, e02321–18 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. Hynes, A. P. et al. Widespread anti-CRISPR proteins in virulent bacteriophages inhibit a range of Cas9 proteins. Nat. Commun. 9, 2919 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Watters, K. E., Fellmann, C., Bai, H. B., Ren, S. M. & Doudna, J. A. Systematic discovery of natural CRISPR–Cas12a inhibitors. Science 362, 236–239 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Marino, N. D. et al. Discovery of widespread type I and type V CRISPR–Cas inhibitors. Science 362, 240–242 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Garcia, B. et al. Anti-CRISPR AcrIIA5 potently inhibits all Cas9 homologs used for genome editing. Cell Rep. 29, 1739–1746 e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Harrington, L. B. et al. A broad-spectrum inhibitor of CRISPR-Cas9. Cell 170, 1224–1233.e15 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168–e168 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Sentmanat, M. F., Peters, S. T., Florian, C. P., Connelly, J. P. & Pruett-Miller, S. M. A survey of validation strategies for CRISPR-Cas9 editing. Sci. Rep. 8, 888 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Edraki, A. et al. A compact, high-accuracy Cas9 with a dinucleotide PAM for in vivo genome editing. Mol. Cell 73, 714–726 e4 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Ran, F. A. et al. In vivo genome editing using Staphylococcus aureus Cas9. Nature 520, 186–191 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Senís, E. et al. CRISPR/Cas9-mediated genome engineering: an adeno-associated viral (AAV) vector toolbox. Biotechnol. J. 9, 1402–1412 (2014).

    Article  PubMed  CAS  Google Scholar 

  27. Schmidt, F. & Grimm, D. CRISPR genome engineering and viral gene delivery: a case of mutual attraction. Biotechnol. J. 10, 258–272 (2015).

    Article  CAS  PubMed  Google Scholar 

  28. Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lee, J. et al. Tissue-restricted genome editing in vivo specified by microRNA-repressible anti-CRISPR proteins. RNA 25, 1421–1431 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hirosawa, M., Fujita, Y. & Saito, H. Cell-type-specific CRISPR activation with microRNA-responsive AcrllA4 switch. ACS Synth. Biol. 8, 1575–1582 (2019).

    Article  CAS  PubMed  Google Scholar 

  31. Osuna, B. A. et al. Listeria phages induce Cas9 degradation to protect lysogenic genomes. Preprint at bioRxiv https://doi.org/10.1101/787200 (2019).

  32. Sesterhenn, F. et al. De novo protein design enables precise induction of functional antibodies in vivo. Preprint at bioRxiv https://doi.org/10.1101/685867 (2019).

  33. Song, G. et al. AcrIIA5 inhibits a broad range of Cas9 orthologs by preventing DNA target cleavage. Cell Rep. 29, 2579–2589 e4 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Agudelo, D. et al. Versatile and robust genome editing with Streptococcus thermophilus CRISPR1-Cas9. Genome Res. 30, 107–117 (2018).

    Article  CAS  Google Scholar 

  35. Watters, K.E. et al. Potent CRISPR-Cas9 inhibitors from Staphylococcus genomes. Preprint at bioRxiv https://doi.org/10.1101/799403 (2019).

  36. Huang, P. S. et al. RosettaRemodel: a generalized framework for flexible backbone protein design. PLoS ONE 6, e24109 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Canutescu, A. A. & Dunbrack, R. L. Jr. Cyclic coordinate descent: a robotics algorithm for protein loop closure. Protein Sci. 12, 963–972 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Coutsias, E. A., Seok, C., Jacobson, M. P. & Dill, K. A. A kinematic view of loop closure. J. Comput. Chem. 25, 510–528 (2004).

    Article  CAS  PubMed  Google Scholar 

  39. Mandell, D. J., Coutsias, E. A. & Kortemme, T. Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling. Nat. Methods 6, 551–552 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Bonet, J., Harteveld, Z., Sesterhenn, F., Scheck, A. & Correia, B. E. rstoolbox—a Python library for large-scale analysis of computational protein design data and structural bioinformatics. BMC Bioinf. 20, 240 (2019).

    Article  Google Scholar 

  41. Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Fleishman, S. J. et al. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS ONE 6, e20161 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Engler, C. & Marillonnet, S. Combinatorial DNA assembly using Golden Gate cloning. Methods Mol. Biol. 1073, 141–156 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Amrani, N. et al. NmeCas9 is an intrinsically high-fidelity genome-editing platform. Genome Biol. 19, 214 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Herrmann, A. K. et al. A robust and all-inclusive pipeline for shuffling of adeno-associated viruses. ACS Synth. Biol. 8, 194–206 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Börner, K. et al. Robust RNAi enhancement via human Argonaute-2 overexpression from plasmids, viral vectors and cell lines. Nucleic Acids Res. 41, e199 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinf. 18, 529 (2017).

    Article  Google Scholar 

  49. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Synthetic Biology (IPMB, Heidelberg University), Virus-Host Interactions (Heidelberg University Clinics) and the Protein Design and Immunoengineering groups (EPFL, Lausanne) for helpful discussions; K. Börner (Heidelberg University Clinics, Heidelberg, Germany) and K. Rippe (DKFZ, Heidelberg, Germany) for providing cell lines; and K. Niopek for critical reading of the manuscript. We thank the EPFL’s Scientific IT and Application Support Center for their support on the computational infrastructure. We thank the Protein Production and Structure Core facility at EPFL for their support on the protein biophysical characterization experiments. This study was funded by the Helmholtz association, the German Research Foundation (DFG) and the Federal Ministry of Education and Research (BMBF) (R.E.). D.G. is grateful for funding from the German Center for Infection Research (DZIF, grant no. TTU-HIV 04.803 and grant no. TTU-HIV 04.815) and the Cystic Fibrosis Foundation Therapeutics (CFFT, grant no. GRIMM15XX0). C. Schmelas, D.G. and R.E. acknowledge funding from the Transregional Collaborative Research Center TRR179 (DFG, Projektnummer 272983813). C. Schmelas and D.G. acknowledge additional funding by the Cluster of Excellence CellNetworks (DFG, grant no. EXC81). B.E.C. is a grantee from the European Research Council (starting grant no. 716058), the Swiss National Science Foundation and the Biltema Foundation. Part of the computational simulations were performed at the CSCS Swiss National Supercomputing Centre through a grant obtained by B.E.C. Z.H. is supported by a grant from the Swiss National Science Foundation. Z.H. and S.R. are supported by a grant from the National Center of Competence in Research in Chemical Biology. Y.W. is supported by the Natural Science Foundation of China (grant nos. 31930065, 31725008, 31630015, 31571335 and 31700662).

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Author notes

  1. These authors contributed equally: Jan Mathony, Zander Harteveld, Carolin Schmelas.

Authors and Affiliations

  1. Synthetic Biology Group, BioQuant Center, University of Heidelberg, Heidelberg, Germany

    Jan Mathony, Julius Upmeier zu Belzen, Sabine Aschenbrenner, Mareike D. Hoffmann, Christina Stengl & Dominik Niopek

  2. Digital Health Center, Berlin Institute of Health (BIH) and Charité, Berlin, Germany

    Jan Mathony, Julius Upmeier zu Belzen, Sabine Aschenbrenner & Roland Eils

  3. Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Zander Harteveld, Andreas Scheck, Sandrine Georgeon, Stéphane Rosset & Bruno E. Correia

  4. Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland

    Zander Harteveld, Andreas Scheck, Sandrine Georgeon, Stéphane Rosset & Bruno E. Correia

  5. Department of Infectious Diseases, Virology, University Hospital Heidelberg, Heidelberg, Germany

    Carolin Schmelas & Dirk Grimm

  6. BioQuant Center and Cluster of Excellence CellNetworks at Heidelberg University, Heidelberg, Germany

    Carolin Schmelas & Dirk Grimm

  7. Health Data Science Unit, University Hospital Heidelberg, Heidelberg, Germany

    Julius Upmeier zu Belzen, Roland Eils & Dominik Niopek

  8. Department of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Sabine Aschenbrenner & Mareike D. Hoffmann

  9. National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Wei Sun & Yanli Wang

  10. Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Wei Sun & Yanli Wang

  11. German Center for Infection Research (DZIF) and German Center for Cardiovascular Research (DZHK), Partner Site Heidelberg, Heidelberg, Germany

    Dirk Grimm

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Contributions

D.N. conceived the initial idea and refined it together with J.U.z.B. and B.E.C.. J.M., C. Schmelas, C. Stengl, S.A. and D.N. designed and performed experiments. S.A. and M.D.H. performed AAV production. S.R. and S.G. purified the Acrs and performed protein-biochemical characterization. Z.H., J.U.z.B., A.S. and B.E.C. performed in silico structural analysis and modeling. W.S. and Y.W. designed and performed in vitro DNA binding and cleavage assays. D.G. provided expertise on AAVs and SauCas9. R.E. supported the computational and experimental work. B.E.C. and D.N. jointly directed the work. B.E.C. and D.N. wrote the manuscript with support from all authors.

Corresponding authors

Correspondence to Bruno E. Correia or Dominik Niopek.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–18 and Tables 1–3.

Reporting Summary

Supplementary Data 1

Annotated vector sequences (GenBank files).

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Mathony, J., Harteveld, Z., Schmelas, C. et al. Computational design of anti-CRISPR proteins with improved inhibition potency. Nat Chem Biol 16, 725–730 (2020). https://doi.org/10.1038/s41589-020-0518-9

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