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结构特异性DNA重组位点:设计、验证及基于机器学习的优化

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

作者信息

Nivina Aleksandra, Grieb Maj Svea, Loot Céline, Bikard David, Cury Jean, Shehata Laila, Bernardes Juliana, Mazel Didier

机构信息

Unité Plasticité du Génome Bactérien, Institut Pasteur, 75724 Paris, France.

CNRS UMR 3525, 75724 Paris, France.

出版信息

Sci Adv. 2020 Jul 24;6(30):eaay2922. doi: 10.1126/sciadv.aay2922. eCollection 2020 Jul.

DOI:10.1126/sciadv.aay2922
PMID:32832653
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7439510/
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 sites from the bacterial integron system: single-stranded DNA hairpins specifically recombined by the integrase. Here, we present an algorithm that generates synthetic 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 design principles. They can be embedded into virtually any sequence and constitute a unique example of a structure-specific DNA recombination system.

摘要

重组系统被广泛用作生物工程工具,但其位点必须与共有序列或彼此高度相似。为了开发一种不受这些限制的重组系统,我们转向了细菌整合子系统的位点:由整合酶特异性重组的单链DNA发夹。在这里,我们提出了一种算法,该算法可以生成具有保守结构特征且序列水平约束最小的合成位点。我们证明所有生成的位点都是有功能的,它们的重组效率可以达到60%,并且可以嵌入到蛋白质编码序列中。为了提高效率较低位点的重组,我们应用了大规模诱变和文库富集,并结合下一代测序和机器学习。我们的结果验证了这种方法的效率,并使我们能够完善合成位点的设计原则。它们几乎可以嵌入到任何序列中,构成了结构特异性DNA重组系统的独特示例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/2ca679cf9ab8/aay2922-F6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/1db7250508a8/aay2922-F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/79ff3be15265/aay2922-F2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/d4394d612c3e/aay2922-F3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/5913cf2d8167/aay2922-F4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/9b2686105e9f/aay2922-F5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/2ca679cf9ab8/aay2922-F6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/1db7250508a8/aay2922-F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/79ff3be15265/aay2922-F2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/d4394d612c3e/aay2922-F3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/5913cf2d8167/aay2922-F4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/9b2686105e9f/aay2922-F5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c601/7439510/2ca679cf9ab8/aay2922-F6.jpg

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