Optimization and partial purification of chitinase produced by Micromonospora echinospora C33 using statistical and neural network approaches

Chitinases are enzymes with important applications in the production of pharmaceutically valuable chitooligosaccharides, bioconversion of chitin-rich waste, and biocontrol of phytopathogenic fungi. Despite extensive studies on bacterial chitinases, the chitinolytic potential of Micromonospora echinospora remains largely unexplored. This study aimed to optimize chitinase production by M. echinospora C33, isolated from semi-arid soil in Laghouat, Algeria, through statistical experimental designs and computational modeling. A preliminary Plackett-Burman design identified three significant factors affecting enzyme production: colloidal chitin, peptone, and galactose. These parameters were further optimized using a Box-Behnken design and response surface methodology (RSM), resulting in an optimal medium composition of 10% (v/v) colloidal chitin, 15 g/L peptone, and 5 g/L galactose. This formulation predicted a maximum chitinase activity of 0.878 U/mL, which was experimentally validated with a yield of 0.828 U/mL, corresponding to a 23.42% increase compared to the basal medium. Furthermore, the Artificial Neural Network (ANN) model accurately predicted chitinase production, corroborating experimental results. Partial purification of the enzyme achieved a purification factor of 4.72, a recovery of 27.41%, and a specific activity of 3 U/mg. The partial characterization of this enzyme reveals an interesting combination of an alkaline pH optimum (pH 8.0) and moderate temperature optimum (30 °C), making it a valuable bio-catalytic candidate for eco-friendly agricultural biocontrol and chitin waste management under ambient soil conditions. These findings underscore the potential of M. echinospora C33 as a promising source of chitinase for industrial applications, providing a solid foundation for further microbial enzyme research and production optimization.