选择(遗传算法)
生物
人口
定向进化
计算生物学
生化工程
计算机科学
人工智能
遗传学
工程类
基因
人口学
社会学
突变体
作者
Suzanne C. Jansen,Clemens Mayer
标识
DOI:10.1101/2023.10.09.561342
摘要
Abstract Life-or-death selections evaluate the fitness of individual organisms on a population level. In enzyme engineering, such growth selections allow the rapid and straightforward identification of highly efficient biocatalysts from extensive libraries. However, selection-based improvement of (industrially-relevant) biocatalysts is challenging, as they require highly dependable strategies that artificially link their activities to host survival. Here, we showcase a robust and scalable life-or-death selection platform centered around the complementation of non-canonical amino acid-dependent bacteria. Specifically, we demonstrate how serial passaging of populations featuring millions of carbamoylase variants autonomously selects biocatalysts with up to 90,000-fold higher initial rates. Notably, selection of replicate populations enriched diverse biocatalysts, which feature distinct amino-acid motifs that drastically boost carbamoylase activity. As beneficial substitutions also originated from unintended copying errors during library preparation or cell division, we anticipate that our life-or-death selection platform will be applicable to the continuous, autonomous evolution of diverse biocatalysts in the future.
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