Inferring an ancestral alginate lyase for improved stability and high-level expression using fed-batch fermentation

Area of Science:

• Biotechnology
• Enzymology
• Microbial Engineering

Background:

• Alginate lyases are crucial biocatalysts with industrial potential.
• Their application is hindered by poor stability and low production yields.
• Heterologous expression and protein engineering are key strategies for improvement.

Purpose of the Study:

• To engineer enhanced alginate lyase variants with improved stability and activity.
• To develop an efficient industrial-scale production strategy for the engineered enzymes.
• To overcome limitations in current alginate lyase applications.

Main Methods:

• Heterologous expression of alginate lyase from *Flavobacterium* sp. in *Escherichia coli*.
• Protein engineering using ancestral sequence inference to create improved variants (AncAlyA1, AncAlyA2).
• Fed-batch fermentation optimization using glucose and yeast extract for large-scale enzyme production.

Main Results:

• Engineered variants AncAlyA1 and AncAlyA2 exhibited significantly increased catalytic activities (132.4% and 87.3% higher than wild-type).
• Both variants demonstrated superior thermal and pH stability, with AncAlyA2 retaining ~60% activity at 60°C and pH 4.0/10.0.
• Optimized fed-batch fermentation achieved high enzyme activities: 5164.8 U/mL for AncAlyA1 and 4220.1 U/mL for AncAlyA2.

Conclusions:

• Engineered alginate lyases possess enhanced enzymatic properties suitable for industrial use.
• An efficient fermentation strategy enables high-level production of these improved biocatalysts.
• This study presents a viable approach for the industrial-scale manufacturing of alginate lyase.