Nature offers an immense variety of enzymes that we can employ to construct metabolic pathways for the production of compounds of interest. Nonetheless, to realize the full potential of synthetic biology, we ultimately need to move beyond the natural repertoire of enzymes and reactions. Artificial metalloenzymes (ArMs), which harness the catalytic potential of transition metals to catalyze new-to-nature reactions, offer exciting opportunities in this regard. However, most ArMs reported to date are only moderately active and barely function in living cells. To change this, we have established a whole-cell screening platform for ArMs that relies on periplasmic catalysis in Escherichia coli
. Combined with lab automation and machine learning, this allows us to rapidly and efficiently optimize ArMs towards increased activity in vivo
. Using this platform and a concise library of 400 double mutants, we recently identified improved ArMs for five bioorthogonal reactions, which are catalyzed by enzymes that harbor gold and ruthenium cofactors. The obtained scaffolds represent valuable starting points for larger, machine learning-guided engineering campaigns, which are currently under way to push the boundaries of ArM catalysis in vivo
Our ultimate goal is to enable new-to-nature transition metal catalysis in cells in order to construct novel metabolic pathways. To achieve this, we further engineer E. coli chassis that address key challenges in the field, featuring improved uptake of artificial cofactors and mutual compatibility between cell physiology and bioorthogonal catalysis. To this end, we combine microfluidics and fluorescence-activated cell sorting to facilitate genome-wide knockout and overexpression studies with the goal of identifying and engineering targets that influence ArM activity in vivo.
We expect that these combined efforts to systematically engineer both ArMs and host organisms will enable various new applications in fields such as biocatalysis, metabolic engineering and xenobiology.
Vornholt, Jeschek, ChemBioChem, 2020
Vornholt et al., Science Advances, 2021