Response Surface Methodology-Genetic Algorithm Based Medium Optimization, Purification, and Characterization of...

Area of Science:

• Biotechnology
• Enzyme Engineering
• Microbial Fermentation

Background:

• Extracellular cholesterol oxidase (COD) is an enzyme with significant commercial interest.
• Streptomyces rimosus MTCC 10792 is a microbial source for COD production.
• Optimizing fermentation media is crucial for enhancing enzyme yield.

Purpose of the Study:

• To optimize critical medium components for increased cholesterol oxidase (COD) production from Streptomyces rimosus MTCC 10792.
• To apply statistical tools and artificial intelligence for medium optimization.
• To purify and characterize the optimized COD enzyme.

Main Methods:

• Medium component screening using Plackett-Burman design.
• Statistical modeling with Response Surface Methodology (RSM).
• Optimization using a combination of RSM and Genetic Algorithm (GA).
• Enzyme purification and characterization (molecular mass, stability, kinetic parameters).

Main Results:

• Yeast extract, dextrose, starch, and ammonium carbonate identified as significant factors.
• RSM-GA optimization predicted optimal concentrations leading to a 3.6-fold increase in COD production (5.41 U/ml).
• Purified COD exhibited a specific activity of 12.37 U/mg, molecular mass of 54 kDa, and stability at pH 7.0 and 40°C.
• Kinetic parameters: K_m = 0.043 mM, V_max = 2.21 μmol/min/mg.

Conclusions:

• The RSM-GA approach effectively optimized medium components for enhanced COD production.
• This study presents the first report of RSM-GA based optimization, purification, and characterization of COD from S. rimosus from eastern India.
• The findings provide a foundation for large-scale industrial production of cholesterol oxidase.