Discovery of Electron Hole-hopping Redox Mutations in Myoglobin by Deep Mutational Learning

Abstract

In addition to storing molecular oxygen, myoglobin catalyzes peroxidase-like reactions involving high valency iron(IV)-oxo species that support one-electron oxidations on a range of substrates at an open active site. In select metalloenzymes, long-range electron transfer can be mediated by hole-hopping pathways composed of aromatic residues that act as relay stations for oxidative equivalents. However, it remains unclear how sequence variations could introduce or alter such catalytic mechanisms in myoglobin. Here we used enzyme proximity sequencing (EP-Seq) to measure the peroxidase activity levels of >6,000 human myoglobin variants. The resulting fitness landscape reveals how aromatic substitutions, in particular surface-exposed tryptophans, can enhance peroxidase activity. Using protein language models in tandem with feedforward neural networks, we trained an accurate fitness predictor on the experimental dataset, and applied it to screen >4M double mutant variants. The predictions suggested a beneficial role for electron-hole-hopping mutations in improving peroxidase activity. We experimentally tested 20 high scoring variants in a yeast display assay, all of which outperformed wild type myoglobin. Three selected variants were also tested in soluble format and similarly showed improved performance. A focused combinatorial library yielded a top double tryptophan variant (Q92W/F107W) with 4.9-fold higher catalytic efficiency than wild type. These results show that deep mutational learning can identify myoglobin variants with enhanced peroxidase activity that are consistent with the involvement of hole-hopping pathways, with broad implications for biocatalyst and redox enzyme design.