GGAssembler: Precise and economical design and synthesis of combinatorial mutation libraries

Golden Gate assembly (GGA) can seamlessly generate full‐length genes from DNA fragments. In principle, GGA could be used to design combinatorial mutation libraries for protein engineering, but creating accurate, complex, and cost‐effective libraries has been challenging. We present GGAssembler, a gr...

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Bibliographic Details
Published in:Protein science 2024-10, Vol.33 (10), p.e5169-n/a
Main Authors: Hoch, Shlomo Yakir, Netzer, Ravit, Weinstein, Jonathan Yaacov, Krauss, Lucas, Hakeny, Karen, Fleishman, Sarel Jacob
Format: Article
Language:English
Subjects:
Antibodies
Antibody libraries
Assembly
biotechnology
Combinatorial analysis
combinatorial mutation libraries
Complexity
Deoxyribonucleic acid
Design
DNA
DNA - chemistry
DNA - genetics
DNA biosynthesis
DNA library synthesis
Fragments
Gene Library
Gene sequencing
Golden Gate assembly
Graphical representations
In vitro methods and tests
Mutation
Peptide Library
Protein engineering
Protein Engineering - economics
Protein Engineering - methods
Proteins
Workflow
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Summary:Golden Gate assembly (GGA) can seamlessly generate full‐length genes from DNA fragments. In principle, GGA could be used to design combinatorial mutation libraries for protein engineering, but creating accurate, complex, and cost‐effective libraries has been challenging. We present GGAssembler, a graph‐theoretical method for economical design of DNA fragments that assemble a combinatorial library that encodes any desired diversity. We used GGAssembler for one‐pot in vitro assembly of camelid antibody libraries comprising >105 variants with DNA costs 93% of the desired variants were present in the assembly product and >99% were represented within the expected order of magnitude as verified by deep sequencing. The GGAssembler workflow is, therefore, an accurate approach for generating complex variant libraries that may drastically reduce costs and accelerate discovery and optimization of antibodies, enzymes and other proteins. The workflow is accessible through a Google Colab notebook at https://github.com/Fleishman-Lab/GGAssembler.
ISSN:0961-8368
1469-896X
1469-896X
DOI:10.1002/pro.5169