Prediction of designer-recombinases for DNA editing with generative deep learning
Design of recombinases with new target sites is usually achieved through cycles of directed molecular evolution. Here the authors report Recombinase Generator, RecGen, an algorithm for generation of designer-recombinases; they perform experimental validation to show that this can predict recombinase...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-35614-6 |
| _version_ | 1828083273148923904 |
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| author | Lukas Theo Schmitt Maciej Paszkowski-Rogacz Florian Jug Frank Buchholz |
| author_facet | Lukas Theo Schmitt Maciej Paszkowski-Rogacz Florian Jug Frank Buchholz |
| author_sort | Lukas Theo Schmitt |
| collection | DOAJ |
| description | Design of recombinases with new target sites is usually achieved through cycles of directed molecular evolution. Here the authors report Recombinase Generator, RecGen, an algorithm for generation of designer-recombinases; they perform experimental validation to show that this can predict recombinase sequences. |
| first_indexed | 2024-04-11T04:06:28Z |
| format | Article |
| id | doaj.art-98c42521ad4941e8b782b57f29143de6 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-11T04:06:28Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-98c42521ad4941e8b782b57f29143de62023-01-01T12:22:50ZengNature PortfolioNature Communications2041-17232022-12-0113111210.1038/s41467-022-35614-6Prediction of designer-recombinases for DNA editing with generative deep learningLukas Theo Schmitt0Maciej Paszkowski-Rogacz1Florian Jug2Frank Buchholz3Medical Systems Biology, Medical Faculty, TU DresdenMedical Systems Biology, Medical Faculty, TU DresdenFondazione Human TechnopoleMedical Systems Biology, Medical Faculty, TU DresdenDesign of recombinases with new target sites is usually achieved through cycles of directed molecular evolution. Here the authors report Recombinase Generator, RecGen, an algorithm for generation of designer-recombinases; they perform experimental validation to show that this can predict recombinase sequences.https://doi.org/10.1038/s41467-022-35614-6 |
| spellingShingle | Lukas Theo Schmitt Maciej Paszkowski-Rogacz Florian Jug Frank Buchholz Prediction of designer-recombinases for DNA editing with generative deep learning Nature Communications |
| 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 |
| url | https://doi.org/10.1038/s41467-022-35614-6 |
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