Prediction of designer-recombinases for DNA editing with generative deep learning
Open accessPublished version of a preprint
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.
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- Authors
- Schmitt, L. T., Paszkowski-Rogacz, M., Jug, F., & Buchholz, F.
- Affiliations
- Technische Universität Dresden (DE)
- Human Technopole (IT)
- Corresponding author
- Buchholz, Frank
- Venue
- Nature Communications
- Published
- 2022-12-27
- Volume
- 13
- Issue
- 1
- Pages
- 7966-7966
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9794738
- OpenAlex
- W4313251027
- Cited by
- 45
- References
- 8
- Citations by year
- 2026: 13 · 2025: 5 · 2024: 13 · 2023: 12 · 2022: 2
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Qucosa (Saxon State and University Library Dresden) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- European Commission (742133, H2020, ERC 742133, 825825)
- Bundesministerium für Bildung und Forschung (031B0633)
- Technische Universität Dresden
- Horizon 2020 Framework Programme (H2020 UPGRADE 825825, 825825)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Keywords
- site-specific recombinases, DNA editing, genome editing, conditional variational autoencoder, generative deep learning
- MeSH terms
- Deep Learning, DNA, Directed Molecular Evolution, Recombinases
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