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Prediction of designer-recombinases for DNA editing with generative deep learning

Author

Listed:
  • Lukas Theo Schmitt

    (TU Dresden)

  • Maciej Paszkowski-Rogacz

    (TU Dresden)

  • Florian Jug

    (Fondazione Human Technopole
    Center for Systems Biology Dresden
    Max Planck Institute of Molecular Cell Biology and Genetics)

  • Frank Buchholz

    (TU Dresden)

Abstract

Site-specific tyrosine-type recombinases are effective tools for genome engineering, with the first engineered variants having demonstrated therapeutic potential. So far, adaptation to new DNA target site selectivity of designer-recombinases has been achieved mostly through iterative cycles of directed molecular evolution. While effective, directed molecular evolution methods are laborious and time consuming. Here we present RecGen (Recombinase Generator), an algorithm for the intelligent generation of designer-recombinases. We gather the sequence information of over one million Cre-like recombinase sequences evolved for 89 different target sites with which we train Conditional Variational Autoencoders for recombinase generation. Experimental validation demonstrates that the algorithm can predict recombinase sequences with activity on novel target-sites, indicating that RecGen is useful to accelerate the development of future designer-recombinases.

Suggested Citation

  • Lukas Theo Schmitt & Maciej Paszkowski-Rogacz & Florian Jug & Frank Buchholz, 2022. "Prediction of designer-recombinases for DNA editing with generative deep learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35614-6
    DOI: 10.1038/s41467-022-35614-6
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    References listed on IDEAS

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    1. Felix Lansing & Liliya Mukhametzyanova & Teresa Rojo-Romanos & Kentaro Iwasawa & Masaki Kimura & Maciej Paszkowski-Rogacz & Janet Karpinski & Tobias Grass & Jan Sonntag & Paul Martin Schneider & Ceren, 2022. "Correction of a Factor VIII genomic inversion with designer-recombinases," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
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