Use of a Golden Gate plasmid set enabling scarless MoClo-compatible transcription unit assembly

Authors: Stijn T. de Vries, Laura Kley, Daniel Schindler

arXiv: 2402.03410v1 - DOI (q-bio.OT)
26 pages, 5 figures
License: CC BY 4.0

Abstract: Golden Gate cloning has become a powerful and widely used DNA assembly method. Its modular nature and the reusability of standardized parts allow rapid construction of transcription units and multi-gene constructs. Importantly, its modular structure makes it compatible with laboratory automation, allowing for systematic and highly complex DNA assembly. Golden Gate cloning relies on Type IIS enzymes that cleave an adjacent undefined sequence motif at a defined distance from the directed enzyme recognition motif. This feature has been used to define hierarchical Golden Gate assembly standards with defined overhangs ("fusion sites") for defined part libraries. The simplest Golden Gate standard would consist of three part libraries, namely promoter, coding and terminator sequences, respectively. Each library would have defined fusion sites, allowing a hierarchical Golden Gate assembly to generate transcription units. Typically, Type IIS enzymes are used, which generate four nucleotide overhangs. This results in small scar sequences in hierarchical DNA assemblies, which can affect the functionality of transcription units. However, there are enzymes that generate three nucleotide overhangs, such as SapI. Here we provide a step-by-step protocol on how to use SapI to assemble transcription units using the start and stop codon for scarless transcription unit assembly. The protocol also provides guidance on how to perform multi-gene Golden Gate assemblies with the resulting transcription units using the Modular Cloning standard. The transcription units expressing fluorophores are used as an example.

Submitted to arXiv on 05 Feb. 2024

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.