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Prediction of designer-recombinases for DNA editing with generative deep learning
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 direc...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794738/ https://www.ncbi.nlm.nih.gov/pubmed/36575171 http://dx.doi.org/10.1038/s41467-022-35614-6 |
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author | Schmitt, Lukas Theo Paszkowski-Rogacz, Maciej Jug, Florian Buchholz, Frank |
author_facet | Schmitt, Lukas Theo Paszkowski-Rogacz, Maciej Jug, Florian Buchholz, Frank |
author_sort | Schmitt, Lukas Theo |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9794738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97947382022-12-29 Prediction of designer-recombinases for DNA editing with generative deep learning Schmitt, Lukas Theo Paszkowski-Rogacz, Maciej Jug, Florian Buchholz, Frank Nat Commun Article 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. Nature Publishing Group UK 2022-12-27 /pmc/articles/PMC9794738/ /pubmed/36575171 http://dx.doi.org/10.1038/s41467-022-35614-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Schmitt, Lukas Theo Paszkowski-Rogacz, Maciej Jug, Florian Buchholz, Frank Prediction of designer-recombinases for DNA editing with generative deep learning |
title | Prediction of designer-recombinases for DNA editing with generative deep learning |
title_full | Prediction of designer-recombinases for DNA editing with generative deep learning |
title_fullStr | Prediction of designer-recombinases for DNA editing with generative deep learning |
title_full_unstemmed | Prediction of designer-recombinases for DNA editing with generative deep learning |
title_short | Prediction of designer-recombinases for DNA editing with generative deep learning |
title_sort | prediction of designer-recombinases for dna editing with generative deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794738/ https://www.ncbi.nlm.nih.gov/pubmed/36575171 http://dx.doi.org/10.1038/s41467-022-35614-6 |
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