Statistical versus neural network-embedded swarm intelligence optimization of a metallo-neutral-protease production: activity kinetics and food industry applications

Abstract

An integrated approach involving response surface methodology (RSM) and artificial neural network-ant-colony hybrid optimization (ANN-ACO) was adopted to develop a bioprocess medium to increase the yield of Bacillus cereus neutral protease under submerged fermentation conditions. The ANN-ACO model was comparatively superior (predicted r 2 = 98.5%, mean squared error [MSE] = 0.0353) to RSM model (predicted r 2 = 86.4%, MSE = 23.85) in predictive capability arising from its low performance error. The hybrid model recommended a medium containing (gL−1) molasses 45.00, urea 9.81, casein 25.45, Ca2+ 1.23, Zn2+ 0.021, Mn2+ 0.020, and 4.45% (vv−1) inoculum, for a 6.75-fold increase in protease activity from a baseline of 76.63 UmL−1. Yield was further increased in a 5-L bioreactor to a final volumetric productivity of 3.472 mg(Lh)−1. The 10.0-fold purified 46.6-kDa-enzyme had maximum activity at pH 6.5, 45–55 °C, with Km of 6.92 mM, Vmax of 769.23 µmolmL−1 min−1, kcat of 28.49 s−1, and kcat/Km of 4.117 × 103 M−1 s−1, at 45 °C, pH 6.5. The enzyme was stabilized by Ca2+, activated by Zn2+ but inhibited by EDTA suggesting that it was a metallo-protease. The biomolecule significantly clarified orange and pineapple juices indicating its food industry application.