Evolving artificial metalloenzymes via random mutagenesis

Random mutagenesis has the potential to optimize the efficiency and selectivity of protein catalysts without requiring detailed knowledge of protein structure; however, introducing synthetic metal cofactors complicates the expression and screening of enzyme libraries, and activity arising from free cofactor must be eliminated. Here we report an efficient platform to create and screen libraries of artificial metalloenzymes (ArMs) via random mutagenesis, which we use to evolve highly selective dirhodium cyclopropanases. Error-prone PCR and combinatorial codon mutagenesis enabled multiplexed analysis of random mutations, including at sites distal to the putative ArM active site that are difficult to identify using targeted mutagenesis approaches. Variants that exhibited significantly improved selectivity for each of the cyclopropane product enantiomers were identified, and higher activity than previously reported ArM cyclopropanases obtained via targeted mutagenesis was also observed. This improved selectivity carried over to other dirhodium-catalysed transformations, including N–H, S–H and Si–H insertion, demonstrating that ArMs evolved for one reaction can serve as starting points to evolve catalysts for others. Proteins have the potential to serve as powerful scaffolds that control the catalytic activity and selectivity of organometallic centres; however, new methods are needed to optimize artificial metalloenzymes. Now, an efficient approach for evolving the activity and selectivity of artificial metalloenzymes has been demonstrated using dirhodium cyclopropanases. This approach does not require structural or mechanistic data to guide mutagenesis.