Purification and characterization of chitinase produced by thermophilic fungi Thermomyces lanuginosus.

BACKGROUND: In the past few years, the production of shrimp shell waste from the seafood processing industries has confronted a significant surge. Furthermore, insignificant dumping of waste has dangerous effects on both nature and human well-being. This marine waste contains a huge quantity of chitin which has several applications in different fields. The chitinase enzyme can achieve degradation of chitin, and the chitin itself can be used as the substrate as well for production of chitinase. In the current study, the chitinase enzyme was produced by Thermomyces lanuginosus. The extracellular chitinase was purified from crude extract using ammonium sulfate precipitation followed by DEAE-cellulose ion-exchange chromatography and Sephadex G-100 gel filtration chromatography. The stability and activity of chitinase with different pH, temperature, different times for a reaction, in the presence of different metal ions, and different concentration of enzyme and substrate were analyzed. RESULT: The chitinase activity was found to be highest at pH 6.5, 50 °C, and 60 min after the reaction began. and the chitinase showed the highest activity and stability in the presence of ß-mercaptoethanol (ME). The SDS-PAGE of denatured purified chitinase showed a protein band of 18 kDa. CONCLUSION: The characterization study concludes that Cu2+, Hg2+, and EDTA have an inhibitory effect on chitinase activity, whereas ß-ME acts as an activator for chitinase activity. The utilization of chitin to produce chitinase and the degradation of chitin using that chitinase enzyme would be an opportunity for bioremediation of shrimp shell waste.

Optimization of process parameters for improved chitinase activity from Thermomyces sp. by using artificial neural network and genetic algorithm.

Chitinase is responsible for the breaking down of chitin to N-acetyl-glucosamine units linked through (1-4)-glycosidic bond. The chitinases find several applications in waste management and pest control. The high yield with characteristics thermal stability of chitinase is the key to their industrial application. Therefore, the present work focuses on parameter optimization for chitinase production using fungus Thermomyces lanuginosus MTCC 9331. Three different optimization approaches, namely, response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) were used. The parameters under study were incubation time, pH and inoculum size. The central composite design with RSM was used for the optimization of the process parameters. Further, results were validated with GA and ANN. A multilayer feed-forward algorithm was performed for ANN, i.e., Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The ANN predicted values gave higher chitinase activity, i.e., 102.24 U/L as compared to RSM-predicted values, i.e., 88.38 U/L. The predicted chitinase activity was also closer to the observed data at these levels. The validation study suggested that the highest activity of chitinase as predicted by ANN is in line with experimental analysis. The comparison of three different statistical approaches suggested that ANN gives better optimization results compared to the GA and RSM study.

Extraction and optimization of exopolysaccharide from Lactobacillus sp. using response surface methodology and artificial neural networks.

The microbial polysaccharides secreted and produced from various microbes into their extracellular environment is known as exopolysaccharide. These polysaccharides can be secreted from the microbes either in a soluble or insoluble form.Lactobacillus sp. is one of the organisms that have been found to produce exopolysaccharide. Exo-polysaccharides (EPS) have various applications such as drug delivery, antimicrobial activity, surgical implants and many more in different fields. Medium composition is one of the major aspects for the production of EPS from Lactobacillus sp., optimization of medium components can help to enhance the synthesis of EPS . In the present work, the production of exopolysaccharide with different medium composition was optimized by response surface methodology (RSM) followed by tested for fitting with artificial neural networks (ANN). Three algorithms of ANN were compared to investigate the highest yeild of EPS. The highest yeild of EPS production in RSM was achieved by the medium composition that consists of (g/L) dextrose 15, sodium dihydrogen phosphate 3, potassium dihydrogen phosphate 2.5, triammonium citrate 1.5, and, magnesium sulfate 0.25. The output of 32 sets of RSM experiments were tested for fitting with ANN with three algorithms viz. Levenberg-Marquardt Algorithm (LMA), Bayesian Regularization Algorithm (BRA) and Scaled Conjugate Gradient Algorithm (SCGA) among them LMA found to have best fit with the experiments as compared to the SCGA and BRA.