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A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins

Tobias Stadelmann, Daniel Heid, Michael Jendrusch, Jan Mathony, Stéphane Rosset, Bruno E. Correia, Dominik Niopek
doi: https://doi.org/10.1101/2021.08.21.457204
Tobias Stadelmann
1Center for Synthetic Biology, Technical University of Darmstadt, Darmstadt, 64287, Germany
2Department of Biology, Technical University of Darmstadt, Darmstadt, 64287, Germany
3Black Forest Life Sciences Laboratory, FRO eV, Ohlsbach, 77797, Germany
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Daniel Heid
3Black Forest Life Sciences Laboratory, FRO eV, Ohlsbach, 77797, Germany
4Synthetic Biology Group, BioQuant Center of Heidelberg University, 69120 Heidelberg, Germany
5European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany
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Michael Jendrusch
4Synthetic Biology Group, BioQuant Center of Heidelberg University, 69120 Heidelberg, Germany
5European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany
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Jan Mathony
1Center for Synthetic Biology, Technical University of Darmstadt, Darmstadt, 64287, Germany
2Department of Biology, Technical University of Darmstadt, Darmstadt, 64287, Germany
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Stéphane Rosset
6Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland
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Bruno E. Correia
6Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland
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Dominik Niopek
1Center for Synthetic Biology, Technical University of Darmstadt, Darmstadt, 64287, Germany
2Department of Biology, Technical University of Darmstadt, Darmstadt, 64287, Germany
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  • For correspondence: dominik.niopek@tu-darmstadt.de
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ABSTRACT

Deep mutational scanning is a powerful method to explore the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, would facilitate their in-depth characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in Escherichia coli combining synthetic gene circuits based on CRISPR interference with flow cytometry-coupled sequencing and mathematical modeling. Using this pipeline, we created and characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of Streptococcus pyogenes Cas9. The resulting mutational fitness landscapes revealed that both Acrs possess a considerable mutational tolerance as well as an intrinsic redundancy with respect to Cas9 inhibitory features, suggesting evolutionary pressure towards high plasticity and robustness. Finally, to demonstrate that our pipeline can inform the optimization and fine-tuning of Acrs for genome editing applications, we cross-validated a subset of AcrIIA4 mutants via gene editing assays in mammalian cells and in vitro affinity measurements. Together, our work establishes deep mutational scanning as powerful method for anti-CRISPR protein characterization and optimization.

Competing Interest Statement

D.N. is inventor on several patent applications related to the use and engineering of anti-CRISPR proteins.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted August 22, 2021.
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A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins
Tobias Stadelmann, Daniel Heid, Michael Jendrusch, Jan Mathony, Stéphane Rosset, Bruno E. Correia, Dominik Niopek
bioRxiv 2021.08.21.457204; doi: https://doi.org/10.1101/2021.08.21.457204
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A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins
Tobias Stadelmann, Daniel Heid, Michael Jendrusch, Jan Mathony, Stéphane Rosset, Bruno E. Correia, Dominik Niopek
bioRxiv 2021.08.21.457204; doi: https://doi.org/10.1101/2021.08.21.457204

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