Large scale active-learning-guided exploration for in vitro protein production optimization
Large scale active-learning-guided exploration for in vitro protein production optimization
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DOI：
10.1038/s41467-020-15798-5
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发表时间：
2020-04-20
影响因子：
16.6
通讯作者：
Faulon, Jean-Loup
中科院分区：
文献类型：
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作者：
Borkowski, Olivier;Koch, Mathilde;Faulon, Jean-Loup
基于裂解物的无细胞系统已成为研究基因表达的主要平台，但批次间的差异使蛋白质产量难以预测。在这里，我们描述了一种主动学习方法，以探索类似于4，000，000无细胞缓冲液组合物的组合空间，最大限度地提高蛋白质产量并确定无细胞生产率中涉及的关键参数。我们还提供了一种一步法，无论裂解物质量如何，都可以使用最少的实验工作来实现蛋白质生产的高质量预测。
Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of similar to 4,000,000 cell-free buffer compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.