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

Area of Science:

• Molecular Biology
• Bioengineering
• Computational Biology

Background:

• Golden Gate assembly (GGA) facilitates gene synthesis from DNA fragments.
• Designing complex and cost-effective combinatorial mutation libraries for protein engineering remains a challenge.
• Accurate and economical methods are needed to generate diverse protein variant libraries.

Purpose of the Study:

• To present GGAssembler, a graph-theoretical method for designing DNA fragments for combinatorial libraries.
• To enable economical and accurate generation of complex protein variant libraries.
• To demonstrate the application of GGAssembler in creating camelid antibody libraries.

Main Methods:

• Developed GGAssembler, a graph-theoretical approach for designing DNA fragments.
• Utilized GGAssembler for one-pot in vitro assembly of camelid antibody libraries.
• Verified library complexity and accuracy using deep sequencing.

Main Results:

• Generated antibody libraries with over 10^5 variants.
• Achieved DNA costs below $0.007 per variant, decreasing with library complexity.
• >93% of desired variants were present in the assembly product.
• >99% of variants were represented within the expected order of magnitude.

Conclusions:

• GGAssembler provides an accurate and cost-effective workflow for generating complex protein variant libraries.
• The method can significantly reduce costs and accelerate the discovery and optimization of proteins like antibodies and enzymes.
• GGAssembler is accessible via a Google Colab notebook, promoting wider adoption.