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Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement

Recombination systems are widely used as bioengineering tools, but their sites have to be highly similar to a consensus sequence or to each other. To develop a recombination system free of these constraints, we turned toward attC sites from the bacterial integron system: single-stranded DNA hairpins...

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Autores principales: Nivina, Aleksandra, Grieb, Maj Svea, Loot, Céline, Bikard, David, Cury, Jean, Shehata, Laila, Bernardes, Juliana, Mazel, Didier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439510/
https://www.ncbi.nlm.nih.gov/pubmed/32832653
http://dx.doi.org/10.1126/sciadv.aay2922
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author Nivina, Aleksandra
Grieb, Maj Svea
Loot, Céline
Bikard, David
Cury, Jean
Shehata, Laila
Bernardes, Juliana
Mazel, Didier
author_facet Nivina, Aleksandra
Grieb, Maj Svea
Loot, Céline
Bikard, David
Cury, Jean
Shehata, Laila
Bernardes, Juliana
Mazel, Didier
author_sort Nivina, Aleksandra
collection PubMed
description Recombination systems are widely used as bioengineering tools, but their sites have to be highly similar to a consensus sequence or to each other. To develop a recombination system free of these constraints, we turned toward attC sites from the bacterial integron system: single-stranded DNA hairpins specifically recombined by the integrase. Here, we present an algorithm that generates synthetic attC sites with conserved structural features and minimal sequence-level constraints. We demonstrate that all generated sites are functional, their recombination efficiency can reach 60%, and they can be embedded into protein coding sequences. To improve recombination of less efficient sites, we applied large-scale mutagenesis and library enrichment coupled to next-generation sequencing and machine learning. Our results validated the efficiency of this approach and allowed us to refine synthetic attC design principles. They can be embedded into virtually any sequence and constitute a unique example of a structure-specific DNA recombination system.
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spelling pubmed-74395102020-08-20 Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement Nivina, Aleksandra Grieb, Maj Svea Loot, Céline Bikard, David Cury, Jean Shehata, Laila Bernardes, Juliana Mazel, Didier Sci Adv Research Articles Recombination systems are widely used as bioengineering tools, but their sites have to be highly similar to a consensus sequence or to each other. To develop a recombination system free of these constraints, we turned toward attC sites from the bacterial integron system: single-stranded DNA hairpins specifically recombined by the integrase. Here, we present an algorithm that generates synthetic attC sites with conserved structural features and minimal sequence-level constraints. We demonstrate that all generated sites are functional, their recombination efficiency can reach 60%, and they can be embedded into protein coding sequences. To improve recombination of less efficient sites, we applied large-scale mutagenesis and library enrichment coupled to next-generation sequencing and machine learning. Our results validated the efficiency of this approach and allowed us to refine synthetic attC design principles. They can be embedded into virtually any sequence and constitute a unique example of a structure-specific DNA recombination system. American Association for the Advancement of Science 2020-07-24 /pmc/articles/PMC7439510/ /pubmed/32832653 http://dx.doi.org/10.1126/sciadv.aay2922 Text en Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Nivina, Aleksandra
Grieb, Maj Svea
Loot, Céline
Bikard, David
Cury, Jean
Shehata, Laila
Bernardes, Juliana
Mazel, Didier
Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement
title Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement
title_full Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement
title_fullStr Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement
title_full_unstemmed Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement
title_short Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement
title_sort structure-specific dna recombination sites: design, validation, and machine learning–based refinement
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439510/
https://www.ncbi.nlm.nih.gov/pubmed/32832653
http://dx.doi.org/10.1126/sciadv.aay2922
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