Indigo Formation as a Predictor of Non-Native Aromatic Hydroxylation in Cytochrome P450 BM3

Screening cytochrome P450 BM3 enzyme for specific non-native reactivity is challenging. This enzyme is natively promiscuous in hydroxylation and epoxidation reactions and directed evolution has broadened its scope of reactivity, making it valuable in synthetic schemes. To accelerate discovery of further targets of reactivity, this study investigates indigo formation as a cost-effective predictive marker for hydroxylation of non-native aromatic compounds. Following site-saturation mutagenesis at 42 active-site positions, we identified 142 previously unreported indigo-producing variants that were point-substituted at one of 23 positions, substantially increasing the database of indigo-producing variants. From this pool, 81 well-expressed indigo-positive and 46 indigo-negative variants were screened for hydroxylation of 17 aromatic compounds that are not native substrates of P450 BM3 using the colorimetric 4-aminoantipyrine assay; 10 of the compounds were found to be good substrates for hydroxylation by many variants. Although MD simulations identified no property clearly distinguishing indigo-positive from indigo-negative variants, we found indigo-positive variants to exhibit significantly higher activity and substrate promiscuity, on average. For instance, relative to wild-type P450 BM3, 93% of indigo-positive variants displayed ≥ 7-fold increased hydroxylation of 1-Br-naphthalene compared to only 9% of indigo-negative variants. Increased substrate promiscuity is illustrated by Spearman correlations of reactivity, reaching ≥ 0.7 for 12 substrate pairs. Furthermore, the 10 variants ranked most promiscuous and most active for aromatic hydroxylation are all indigo-positive. We thus found that screening for indigo formation by visualizing E. coli colonies allowed reliable identification of all the top point-substituted variants, with a high hit-rate of increased substrate promiscuity and activity increased up to 167-fold relative to the native enzyme. This efficient screen will serve as a powerful primary tool to accelerate the expansion of the functional repertoire of P450 BM3 variants. Our findings are presented in machine-readable format to aid ulterior machine learning, providing a foundation for computational advances to predict further substrate-variant combinations.