A robust life-or-death selection platform for enzyme evolution

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.