Multi level statistical optimization of l-asparaginase from Bacillus subtilis VUVD001

A new extracellular glutaminase and urease-free L-asparaginase from Meyerozyma guilliermondii

L-Asparaginase (L-ASNase) is a potent chemotherapeutic drug employed to treat leukemia and lymphoma. Currently, L-ASNases for therapeutic use are obtained from Escherichia coli and Dickeya chrysanthemi (Erwinia chrysanthemi). Despite their therapeutic potential, enzymes from bacteria are subject to inducing immune responses, resulting in a higher number of side effects. Eukaryote producers, such as fungi, may provide therapeutic alternatives through enzymes that induce relatively less toxicity and immune responses. Additional expected benefits from yeast-derived enzymes include higher activity and stability in physiological conditions. This work describes the new potential therapeutic candidate L-ASNase from the yeast Meyerozyma guilliermondii. A statistical approach (full factorial central composite design) was used to optimize L-ASNase production, considering L-asparagine and glucose concentration, pH of the medium, and cultivation time as independent factors. In addition, the crude enzymes were biochemically characterized, in terms of temperature and optimal pH, thermostability, pH stability, and associated glutaminase or urease activities. Our results showed that enzyme production increased after supplementing a pH 4.0 medium with 1.0% L-asparagine and 0.5% glucose during 75 h of cultivation. Under these optimized conditions, L-ASNase production reached 26.01 U mL-1, which is suitable for scale-up studies. The produced L-ASNase exhibits maximal activity at 37 °C and pH 7.0 and is highly stable under physiological conditions. In addition, M. guilliermondii L-ASNase has no associated glutaminase or urease activities, demonstrating its potential as a promising antineoplastic agent.

Computational modeling of culture media for enhanced production of fibrinolytic enzyme from marine bacterium Fictibacillus sp. strain SKA27 and in vitro evaluation of fibrinolytic activity

The present study reports the optimized production and purification of an extremely active fibrinolytic enzyme from newly isolated marine bacterium Fictibacillus sp. strain SKA27, with a specific activity of 125,107.85 U/mg and an apparent molecular weight of 28 kDa on SDS-PAGE. Wheat bran extract used for submerged production proved to be highly beneficial and enhanced fibrinolytic enzyme production when combined with yeast extract and CaCl₂. Optimization of culture media by response surface methodology (RSM) resulted in high root mean square error (RMSE), which led to the training of a back propagation multilayer artificial neural network (ANN) with 3-5-1 topology for better prediction quality. The prediction and optimization capabilities of regression and ANN were critically examined and ANN displayed higher proficiency with R ² of 0.99 and RMSE of 2.0 compared to 0.98 R ² and 48.9 RMSE of the regression model. An adept ANN linked genetic algorithm (GA) optimized the medium components to achieve 1.8-fold higher enzyme production (4175.41 U/mL). Further, a new and improved in vitro qualitative analysis displayed high specificity of purified enzyme to fibrin.