Hoch Shlomo Yakir, Netzer Ravit, Weinstein Jonathan Yaacov, Krauss Lucas, Hakeny Karen, Fleishman Sarel Jacob
Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
Protein Sci. 2024 Oct;33(10):e5169. doi: 10.1002/pro.5169.
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 >10 variants with DNA costs <0.007$ per variant and dropping significantly with increased library complexity. >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.
金门组装（GGA）能够无缝地从DNA片段生成全长基因。原则上，GGA可用于设计用于蛋白质工程的组合突变文库，但创建准确、复杂且经济高效的文库一直具有挑战性。我们展示了GGAssembler，这是一种基于图论的方法，用于经济地设计DNA片段，这些片段可组装成一个编码任何所需多样性的组合文库。我们使用GGAssembler对包含超过10个变体的骆驼科抗体文库进行一锅法体外组装，每个变体的DNA成本低于0.007美元，并且随着文库复杂性的增加而显著下降。通过深度测序验证，组装产物中存在超过93%的所需变体，且超过99%的变体在预期数量级内得到体现。因此，GGAssembler工作流程是一种生成复杂变体文库的准确方法，它可以大幅降低成本并加速抗体、酶和其他蛋白质的发现与优化。可通过https://github.com/Fleishman-Lab/GGAssembler上的Google Colab笔记本访问该工作流程。
