A Mechanistic Rationale Approach Revealed the Unexpected Chemoselectivity of an Artificial Ru-Dependent Oxidase: A Dual Experimental/Theoretical Approach

A Co(TAML)-based artificial metalloenzyme for asymmetric radical-type oxygen atom transfer catalysis

We show that the incorporation of a biotinylated Co(TAML) cofactor within streptavidin enables asymmetric radical-type oxygen atom transfer catalysis with improved activity and enantioselectivity.

Bis(4-carboxylpyrazol-1-yl)acetic acid: a scorpionate ligand for complexes with improved water solubility

Bis(4-carboxylpyrazol-1-yl)acetic acid (H₃bcpza) (2), obtained in a one-pot synthesis, contains carboxyl groups that differ in their pK_a values. The ligand exhibits heteroscorpionate κ³-N,N,O-coordination whereupon the peripheral carboxyl groups are not involved in metal binding. The corresponding carbonyl complexes [Mn(H₂bcpza)(CO)₃] (4), [Re(H₂bcpza)(CO)₃] (5) and [Ru(H₂bcpza)Cl(CO)₂] (6a/6b) are partially soluble in water but readily soluble in PBS buffer.

Systematic engineering of artificial metalloenzymes for new-to-nature reactions

Artificial metalloenzymes (ArMs) catalyzing new-to-nature reactions could play an important role in transitioning toward a sustainable economy. While ArMs have been created for various transformations, attempts at their genetic optimization have been case specific and resulted mostly in modest improvements. To realize their full potential, methods to rapidly discover active ArM variants for ideally any reaction of interest are required. Here, we introduce a reaction-independent, automation-compatible platform, which relies on periplasmic compartmentalization in Escherichia coli to rapidly and reliably engineer ArMs based on the biotin-streptavidin technology. We systematically assess 400 ArM mutants for five bioorthogonal transformations involving different metals, reaction mechanisms, and reactants, which include novel ArMs for gold-catalyzed hydroamination and hydroarylation. Activity enhancements up to 15-fold highlight the potential of the systematic approach. Furthermore, we suggest smart screening strategies and build machine learning models that accurately predict ArM activity from sequence, which has crucial implications for future ArM development.