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

TitleStructure-specific DNA recombination sites: Design, validation, and machine learning–based refinement
Publication TypeJournal Article
Year of Publication2020
AuthorsNivina, A, Grieb, MSvea, Loot, C, Bikard, D, Cury, J, Shehata, L, Bernardes, J, Mazel, D
JournalScience Advances
Volume6
Abstract

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.

URLhttps://advances.sciencemag.org/content/6/30/eaay2922
DOI10.1126/sciadv.aay2922

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