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

TL;DR

Scientists improved an enzyme called alginate lyase by engineering ancestral versions and optimizing fermentation, leading to higher activity and stability for industrial use.

Contribution

The study introduces ancestral sequence inference and fed-batch fermentation to enhance alginate lyase stability and production efficiency.

Findings

Two ancestral variants of alginate lyase showed 132.4% and 87.3% higher catalytic activity than the wild-type.

AncAlyA2 retained 60% activity under extreme thermal and pH conditions.

Fed-batch fermentation achieved 5164.8 U/mL and 4220.1 U/mL enzyme activity for the two variants.

Abstract

Alginate lyases are valuable biocatalysts, but their industrial application is often constrained by limited stability and low production efficiency. In this study, an alginate lyase from Flavobacterium sp. was heterologously expressed in different host systems, among which Escherichia coli exhibited the highest enzymatic activity. Ancestral sequence inferring was subsequently applied to engineer improved variants, which were expressed and purified in E. coli. Two variants, AncAlyA1 and AncAlyA2, showed significantly enhanced catalytic activities, with increases of 132.4 and 87.3%, respectively, compared with the wild-type. Both variants also exhibited markedly improved thermal and pH stability. Notably, AncAlyA2 retained approximately 60% of its activity after incubation under extreme conditions, including 60 °C and pH 4.0 or 10.0. In addition, a fed-batch fermentation strategy based on combined glucose and yeast extract feeding was developed. Under optimized conditions, enzyme activities of 5164.8 U/mL and 4220.1 U/mL were achieved for AncAlyA1 and AncAlyA2, respectively. This study provides a promising strategy for industrial-scale production of alginate lyase.

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