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

In addition to storing molecular oxygen, myoglobin catalyzes peroxidase-like reactions involving high valency iron(IV)-oxo species that support oxidation of a range of substrates at an open active site. Until now, it was unclear whether long-range electron transfer via hole-hopping could contribute to myoglobin’s catalytic cycle. 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 evaluate >4M double mutant variants. The predictions suggested a beneficial role for 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 hole-hopping pathways can be identified and engineered through deep mutational learning, with broad implications for biocatalyst and redox enzyme design.