Biosensor-Based Directed Evolution of Methanol Dehydrogenase from Lysinibacillus xylanilyticus

Area of Science:

• Biochemistry
• Enzyme Engineering
• Synthetic Biology

Background:

• Methanol dehydrogenase (Mdh) is vital for methylotrophy, but wild-type enzymes exhibit limitations in catalytic activity and methanol affinity.
• Engineering NAD-dependent Mdh is crucial for enhancing methanol conversion for industrial applications.

Purpose of the Study:

• To improve the catalytic efficiency and methanol affinity of Mdh from Lysinibacillus xylanilyticus (Lxmdh) through direct evolution.
• To identify specific mutations that enhance Lxmdh performance at low methanol concentrations.

Main Methods:

• A transcriptional factor-based biosensor system was developed for direct evolution of Lxmdh.
• A random mutant library of Lxmdh was constructed in Escherichia coli.
• Fluorescence-activated cell sorting (FACS) was used to screen for improved enzyme variants using formaldehyde-detectable biosensors.

Main Results:

• Mutant enzymes E396V, K318N, and K46E demonstrated significantly enhanced activity and methanol conversion efficiency.
• Mutant E396V showed a 79-fold improvement, K318N a 23-fold improvement, and K46E a 3-fold improvement compared to wild-type Lxmdh.
• These mutants exhibit superior performance, particularly at very low methanol concentrations.

Conclusions:

• Direct evolution using a biosensor is an effective strategy for engineering Mdh with enhanced methanol conversion capabilities.
• The identified Lxmdh mutants hold promise for advancing synthetic methylotrophy and biotechnological applications requiring efficient methanol utilization.