Statistically designed bioprocess for enhanced production of alkaline protease in bacillus cereus HP_RZ17

Abstract

Alkaline protease is one of the bulk enzymes having wide commercial demand for various applications. It is commercially produced by a submerged fermentation process employing various bacteria, Bacillus sp. being the most widely used species. Statistical optimization of the process for the production of alkaline proteases from rhizospheric bacteria and its application in the biocontrol of plant pathogens has not been explored fully and needs to be studied for the development of efficient bioprocess. We report the enhanced production of alkaline protease in the minimal salt medium (MSM) optimized using statistical approaches such as Plackett Burman Design (PBD) and Response Surface Methodology (RSM). In the first step; PBD, among the total eight variables, three variables namely, yeast extract (p<0.05), fructose (p<0.05) and pH (p<0.05) influenced the production of alkaline protease by Bacillus cereus HP_RZ17. These three variables were further analyzed in the second step i.e. Central Composite Design (CCD) of RSM. The optimum yield of alkaline protease by B. cereus HP_RZ17 (130.72 UmL-1) was obtained under the optimal conditions such as yeast extract (0.899% w/v), fructose (0.873% w/v), and pH (11.25) of production media. The statistically optimized values of variables used for the scale-up of the process at 5 L capacity bioreactor enhanced the alkaline protease yield (132.48 UmL-1) by 1.09 fold visà-vis un-optimized protocol (121.96 UmL-1) in B. cereus HP_RZ17.