Enzyme activity engineering based on sequence co-evolution analysis.

Area of Science:

• Biotechnology
• Enzyme Engineering
• Computational Biology

Background:

• Conventional enzyme engineering methods often require high-throughput screening or detailed structural information, limiting their broad applicability.
• Natural selection fine-tunes enzyme activity through accumulated mutations during evolution, offering an alternative strategy for enzyme optimization.

Purpose of the Study:

• To develop a novel strategy, sequence co-evolutionary analysis to control the efficiency of enzyme reactions (SCANEER), for predicting and directing mutations to enhance enzyme activity.
• To test the hypothesis that evolutionary history encodes amino acid pairs crucial for enzyme function, while avoiding loss-of-function mutations.

Main Methods:

• SCANEER analyzes evolutionary patterns in protein sequences to identify mutations that improve enzyme activity.
• The method was applied to predict enzyme activities for beta-lactamase and aminoglycoside 3'-phosphotransferase.
• Experimental validation was performed on three industrially relevant enzymes: cis-aconitate decarboxylase, α-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase.

Main Results:

• SCANEER accurately predicted enzyme activities for the tested enzymes.
• Experimental validation confirmed SCANEER's ability to enhance the activity of cis-aconitate decarboxylase, α-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase.
• Beneficial mutations were identified at positions distant from known active sites, suggesting novel mechanisms for activity enhancement.

Conclusions:

• SCANEER provides an effective computational approach to guide enzyme engineering for improved activity.
• The findings highlight the utility of evolutionary information in predicting functional mutations for enzyme optimization.
• A user-friendly webserver for SCANEER is available to facilitate enzyme engineering efforts.