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首页> 外文期刊>Science Advances >Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement
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Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement

机译:特异性DNA重组位点:设计,验证和机器学习的改进

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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.
机译:重组系统被广泛用作生物工程工具,但它们的位点必须与共有序列或彼此高度相似。为了开发自由这些约束的重组系统,我们从细菌整合系统中转向attc位点:由整体酶特异性重组的单链DNA发夹。这里,我们提出了一种算法,该算法产生具有保守的结构特征和最小序列级约束的合成atc站点。我们证明所有产生的位点都是功能性的,它们的重组效率可以达到60%,并且它们可以嵌入蛋白质编码序列中。为了改善效率较低的地点的重组,我们应用大规模诱变和图书馆富集,耦合到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,使我们能够改进综合ATTC设计原则。它们几乎可以嵌入任何序列并构成结构特异性DNA重组系统的独特典范。

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