Mining Highly Active Oleate Hydratases by Structure Clustering, Sequence Clustering, and Ancestral Sequence Reconstruction

Abstract

Oleate hydratases (Ohys) catalyze the conversion of oleic acid (OA) to 10-(R)-hydroxystearic acid (10-HSA), a compound widely used in the chemical industry. However, the limited activity of Ohys has hindered their broader applications. To address this limitation, we propose a novel strategy for mining highly active Ohys through structure clustering, sequence clustering, and ancestral sequence reconstruction (SSA strategy). Structure clustering via AI-driven protein structure prediction followed by classification enhanced the ability to mine target Ohys. Ancestral enzyme reconstruction was carried out based on mining results from both structure and sequence clustering. This strategy significantly reduces the time and cost of the discovery process. Among the 1304 Ohys screened via SSA, 13 candidates were selected. Seven candidates demonstrated high activity. Ohy 64, identified through structure clustering, exhibited the highest activity. Ancestral enzymes that were reconstructed from structure clustering targets were 3 times more likely to exhibit high catalytic activity than those identified through sequence clustering. Four critical, hydrophobic residues were identified through structure and sequence comparisons between StOhy and targets mined by SSA. Site-directed mutagenesis revealed that these hydrophobic residues conferred varying levels of enzyme activity, confirming that increased hydrophobicity at these positions enhances cofactor FAD binding, thus improving enzyme catalytic efficiency.