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

Area of Science:

• Biochemistry
• Enzyme Engineering
• Computational Biology

Background:

• Oleate hydratases (Ohys) are crucial enzymes converting oleic acid to 10-(R)-hydroxystearic acid, a valuable industrial chemical.
• The limited catalytic activity of naturally occurring Ohys restricts their widespread industrial use.
• Discovering highly active Ohys is essential for advancing biocatalysis and chemical manufacturing.

Purpose of the Study:

• To develop and validate a novel strategy (SSA) for efficiently mining highly active oleate hydratases.
• To identify novel Ohy variants with enhanced catalytic efficiency for industrial applications.
• To elucidate the structural and sequence determinants of Ohy activity.

Main Methods:

• Employed a Structure-Sequence-Ancestral (SSA) strategy integrating AI-driven protein structure prediction, clustering, and ancestral sequence reconstruction.
• Screened 1304 Ohy sequences using the SSA strategy.
• Performed site-directed mutagenesis to investigate the role of key hydrophobic residues in enzyme activity.

Main Results:

• The SSA strategy successfully identified 13 candidate Ohys, with seven exhibiting high activity.
• Ohy 64, discovered via structure clustering, showed the highest catalytic activity.
• Ancestral enzymes derived from structure clustering were three times more likely to be highly active compared to those from sequence clustering.
• Identified four critical hydrophobic residues that enhance cofactor FAD binding and improve catalytic efficiency.

Conclusions:

• The SSA strategy offers a significantly faster and more cost-effective approach for discovering highly active enzymes.
• Structural insights combined with ancestral reconstruction are powerful tools for enzyme engineering.
• Targeted mutagenesis of identified hydrophobic residues can further optimize enzyme performance for industrial biocatalysis.