Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function

Abstract

Directed evolution has emerged as a powerful tool for engineering new biocatalysts. However, introducing new catalytic residues can be destabilizing, and it is generally beneficial to start with a stable enzyme parent. Here we show that the deep learning tool ProteinMPNN can be used to redesign an Fe(II)/alphaKG superfamily enzyme for greater stability, solubility, and expression while retaining both native activity and an industrially-relevant non-native function. We performed site-saturation mutagenesis with both the wild type and stabilized design variant and screened for activity increases in a non-native C-H hydroxylation reaction. We observed substantially larger increases in non-native activity for variants obtained from the stabilized scaffold compared to those from the wild-type enzyme. Deep learning tools like ProteinMPNN are user-friendly and widely-accessible, and relatively straightforward structural criteria were sufficient to obtain stabilized variants while preserving catalytic function. Our work suggests that stabilization by computational sequence redesign could be routinely implemented as a first step in directed evolution campaigns for novel biocatalysts.