Metabolic growth-coupling strategies for in vivo enzyme selection systems

Abstract

Whole-cell biocatalysis facilitates the production of a wide range of industrially and pharmaceutically relevant molecules from sustainable feedstocks such as plastic wastes, carbon dioxide, lignocellulose, or plant-based sugar sources. The identification and use of efficient enzymes in the applied biocatalyst is key to establishing economically feasible production processes. The generation and selection of favorable enzyme variants in adaptive laboratory evolution experiments using growth as a selection criterion is facilitated by tightly coupling enzyme catalytic activity to microbial metabolic activity. Here, we present a computational workflow to design strains that have a severe, growth-limiting metabolic chokepoint through a shared class of enzymes. The resulting chassis cell, termed enzyme selection system (ESS), is a platform for growth-coupling any enzyme from the respective enzyme class, thus offering cross-pathway application for enzyme engineering purposes. By applying the constraint-based modeling workflow, a publicly accessible database of 25,505 potential and experimentally tractable ESS designs was built for Escherichia coli and a broad range of production pathways with biotechnological relevance. A model-based analysis of the generated design database reveals a general design principle that the target enzyme activity is linked to overall microbial metabolic activity, not just the synthesis of one biomass precursor. It can be observed that the stronger the predicted coupling between target enzyme and metabolic activity, the lower the maximum growth rate and therefore the viability of an ESS. Consequently, growth-coupling strategies with only suboptimal coupling strengths, as are included in the ESS design database, may be of interest for practical applications of ESSs in order to circumvent overly restrictive growth defects. In summary, the computed design database, which is accessible via https://biosustain.github.io/ESS-Designs/, and its analysis provide a foundation for the generation of valuable in vivo ESSs for enzyme optimization purposes and a range of biotechnological applications in general.