- title
- Prediction of designer-recombinases for DNA editing with generative deep learning
- Please use the following URL when quoting:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-918383
- Source
- Nature Communications
Erscheinungsjahr: 2022
Jahrgang: 13
E-ISSN: 2041-1723
Artikelnummer: 7966 - Abstract (EN)
- 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 designerrecombinases 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 designerrecombinases. We gather the sequence information of over one million Crelike recombinase sequences evolved for 89 different target sites with whichwe 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.
- otherVersion0
- Link zum Artikel der zuerst in der Zeitschrift „ Nature Communications” bei Springer erschienen ist.
DOI: 10.1038/s41467-022-35614-6 - references0000
- Ergänzendes Material ist unter folgendem Link zu finden.
Link: https://www.nature.com/articles/s41467-022-35614-6#Sec19 - Keywords (DE)
- Maschinelles Lernen, Molekulare Evolution, Entwurf von Proteinen
- Keywords (EN)
- Machine learning, Molecular evolution, Protein design
- Classification (DDC)
- 500
- Publishing house
- Nature Publishing Group, London
- Project sponsoring
- European Commission (EC)
H2020 | RIA
Unlocking Precision Gene Therapy
(UPGRADE)
ID: 825825 - European Commission (EC)
H2020 | ERC | ERC-ADG
Designer recombinases for efficient and safe genome surgery
(GENSURGE)
ID: 742133 - version
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-918383
- Qucosa date of publication
- 04.06.2024
- Document type
- article
- Document language
- English
- licence
CC BY 4.0