Application of the novel manta-ray foraging algorithm to optimize acidic peptidase production in solid-state fermentation using binary agro-industrial waste

Abstract

Peptidases, which constitute about 20% of the global enzyme market, have found applications in detergent, food and pharmaceutical industries, and could be produced on a large scale using low-cost agro-industrial waste. An acidophilic Bacillus cereus strain produced acidic peptidase on binary-agro-industrial waste comprising yam peels and fish processing waste at pH 4.5 with high catalytic activity. A five-variable central composite rotatable design of a response surface methodology was used to model bioprocess conditions for improved peptidase production in solid-state fermentation. Data generated was leveraged as the basis for applying the novel Manta-ray foraging optimization-linked feed-forward artificial neural network to predict bioprocess conditions optimally. Results obtained from the optimization experiments revealed a significant coefficient of determination of 0.9885 with low-performance error. The bioprocess predicted a peptidase activity of 1035.32 U/mL under optimized conditions set as 54.8 g/100 g yam peels, 23.85 g/100 g fish waste, 0.31 g/100 g CaCl2, 47.54% (v/w) moisture content, and pH 2. Peptidase activity was improved 5-fold, and was stable for 240 min between pH 2.5 and 3.5. Michaelis-Menten kinetics revealed a Km of 0.119 mM and a catalytic efficiency of 45462.19 mM−1 min−1. The bioprocess holds promise for sustainable enzyme-driven applications.