缩小聚酮合酶作为合成生物学平台的承诺与现实之间的差距。
Narrowing the gap between the promise and reality of polyketide synthases as a synthetic biology platform.
机构信息
Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94270, USA; Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
Department of Plant & Microbial Biology, University of California, Berkeley, CA 94270, USA; Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA; Synthetic Biology Engineering Research Center, 5885 Hollis Street, Emeryville, CA 94608, USA.
出版信息
Curr Opin Biotechnol. 2014 Dec;30:32-9. doi: 10.1016/j.copbio.2014.04.011. Epub 2014 May 6.
Engineering modular polyketide synthases (PKSs) has the potential to be an effective methodology to produce existing and novel chemicals. However, this potential has only just begun to be realized. We propose the adoption of an iterative design-build-test-learn paradigm to improve PKS engineering. We suggest methods to improve engineered PKS design by learning from laboratory-based selection; adoption of DNA design software and automation to build constructs and libraries more easily; tools for the expression of engineered proteins in a variety of heterologous hosts; and mass spectrometry-based high-throughput screening methods. Finally, lessons learned during iterations of the design-build-test-learn cycle can serve as a knowledge base for the development of a single retrosynthesis algorithm usable by both PKS experts and non-experts alike.
工程模块化聚酮合酶（PKS）有可能成为生产现有和新型化学物质的有效方法。然而，这种潜力才刚刚开始被意识到。我们建议采用迭代设计-构建-测试-学习的范例来改进 PKS 工程。我们提出了一些方法，通过从实验室选择中学习来改进工程 PKS 的设计；采用 DNA 设计软件和自动化技术，以便更轻松地构建构建体和文库；用于在各种异源宿主中表达工程化蛋白质的工具；以及基于质谱的高通量筛选方法。最后，在设计-构建-测试-学习循环的迭代过程中获得的经验教训可以作为知识库，用于开发一个可由 PKS 专家和非专家共同使用的单一反合成算法。
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