The fermentation optimization for alkaline protease production by BS-QR-052.

Introduction: Proteases exhibit a wide range of applications, and among them, alkaline proteases have become a prominent area of research due to their stability in highly alkaline environments. To optimize the production yield and activity of alkaline proteases, researchers are continuously exploring different fermentation conditions and culture medium components.

Methods: In this paper, the fermentation conditions of the alkaline protease (EC 3.4.21.14) production by BS-QR-052 were optimized, and the effect of different nutrition and fermentation conditions was investigated. Based on the single-variable experiments, the Plackett-Burman design was used to explore the significant factors, and then the optimized fermentation conditions, as well as the interaction between these factors, were evaluated by response surface methodology through the Box-Behnken design.

Results And Discussion: The results showed that 1.03% corn syrup powder, 0.05% MgSO, 8.02% inoculation volume, 1:1.22 vvm airflow rate, as well as 0.5% corn starch, 0.05% MnSO, 180 rpm agitation speed, 36°C fermentation temperature, 8.0 initial pH and 96 h incubation time were predicted to be the optimal fermentation conditions. The alkaline protease enzyme activity was estimated to be approximately 1787.91 U/mL, whereas subsequent experimental validation confirmed it reached 1780.03 U/mL, while that of 500 L scale-up fermentation reached 1798.33 U/mL. This study optimized the fermentation conditions for alkaline protease production by through systematic experimental design and data analysis, and the activity of the alkaline protease increased to 300.72% of its original level. The established model for predicting alkaline protease activity was validated, achieving significantly higher levels of enzymatic activity. The findings provide valuable references for further enhancing the yield and activity of alkaline protease, thereby holding substantial practical significance and economic benefits for industrial applications.