An Integrated Computational-Experimental Workflow for Fungal Peroxygenase Discovery and Enzyme-Substrate Prediction

Recent advances in enzyme informatics have produced increasingly powerful general models for the prediction of protein function prediction. However, their performance remains limited for underexplored enzyme families where experimental data are scarce and heterologous expression remains challenging. Here, we present a computational-experimental workflow for prioritizing, characterizing, and modeling fungal unspecific peroxygenases (UPOs) as a case study in enzyme discovery under data-limited conditions. The workflow integrates large-scale sequence mining, expression-aware candidate prioritization, systematic experimental screening, and machine-learning-based prediction of enzyme-substrate activity. From more than 3000 mined candidate sequences, 59 putative UPOs were shortlisted, expressed in Escherichia coli across two batches, and tested against colorimetric substrates, yielding 27 UPOs with consistent expression and activity. A subset of 15 novel UPOs and 4 well-characterized reference enzyme sequences was then screened against a chemically diverse panel of 27 substrates to generate an enzyme-substrate activity data set. Using this data set, we developed two complementary machine learning protocols based on gradient boosting: (i) a structure-informed model incorporating homology modeling, docking, and interaction-derived features (modeling-docking-ML) and (ii) a latent-representation model utilizing joint protein/chemical language model embeddings (protein/chemical-language-model-ML). In retrospective validation, the models achieved performance with a Matthews correlation coefficient (MCC) of up to 55% and accuracy up to 78%. In selected prospective testing on previously unseen batch 2 enzymes, the models achieved an MCC of up to 48% and accuracy up to 70%. Taken together, these results demonstrate that targeted experimental data generation, combined with complementary structure-informed and embedding-based models, can support prioritization and activity prediction within poorly characterized enzyme families while highlighting the continued challenge of generalizing to broader chemical substrate space.