Impact of process parameters on L-glutaminase production by marine Vibrio costicola in solid state fermentation using polystyrene as an inert support

Process parameters influencing e-glutaminase production by marine Vibrio costicola in solid state fermentation (SSF) using polystyrene as an inert support were optimised. Maximal enzyme yield (157 U/g dry substrate) was obtained at 2% (w/w) t:glutamine, 35°C and pH 7.0 after 24 h. Maltose and potassium dihydrogen phosphate at 1% (w/w) concentration enhanced enzyme yield by 23 and 18%, respectively, while nitrogen sources had an inhibitory effect. Leachate with high specific activity for glutaminase (4.2 U/mg protein) and low viscosity (0-966 Ns/m 2) was recovered from the polystyrene SSF system

Short Communication: Polystyrene-an inert carrier for L·glutaminase production by marine Vibrio costico/a under selld-state fermentation

Polystyrene beads, impregnated with mineral salts/glutamine medium as inert support, were used to produce L-glutaminase from Vibrio costicola by solid-state fermentation. Maximum enzyme yield, 88 U/g substrate, was after 36 h. Glucose at 10 g/kg enhanced the enzyme yield by 66%. The support system allowed glutaminase to be recovered with higher specific activity and lower viscosity than when a wheat-bran system was used

L-Glutaminase production by an immobilized marine Pseudomonas sp. BTMS -51

The present study is about the Pseudomonas sp. BTMS-51 isolated from the marine sediments of Cochin Coast. In the present study, it is concluded that marine bacteria are ideal candidates for immobilization using either Ca-alginate entrapment or physical adsorption on to synthetic inert supports and the process of immobilization does not negatively influence them. Thus, Ca-alginate entrapment of the bacteria was found to be well suited for reuse of the biomass and extended operational stability during continuous operation. Adherence of the bacterium to inertsupports was observed to be strong and it imparted minimal stress on the immobilized bacterium and allowed detachment and relocation on the supports which enabled the formation of a dynamic equilibrium maintaining a stable cell loading. This is particularly desirable in the industry for extended operational stability and maintenance of consistently higher outputs. Marine Pseudomonas sp. BTMS-51 is ideal for industrial production of extra cellular L-glutaminase and immobilization on to synthetic inert support such as polyurethane foam could be an efficient technique, employing packed bed reactor for continuous production of the enzyme. Temperature and glutamine concentration had significant effects on enzyme production by cells immobilized on polyurethane foam (PUF).

L-Glutaminase production by marine Beau6eria sp. under solid state fermentation

Extracellular L-glutaminase production by Beau6eria sp., isolated from marine sediment, was observed during solid state fermentation using polystyrene as an inert support. Maximal enzyme production (49.89 U:ml) occurred at pH 9.0, 27°C, in a seawater based medium supplemented with L-glutamine (0.25% w:v) as substrate and D-glucose (0.5% w:v) as additional carbon source, after 96 h of incubation. Enzyme production was growth associated. Results indicate scope for production of salt tolerant L-glutaminase using this marine fungus

Purification and Characterization of an Anti-cancer Enzyme Produced by Marine Vibrio Costicola Under A Novel Solid State Fermentation Process

L - Glutaminase, a therapeutically and industrially important enzyme, was produced from marine Vibrio costicola by a novel solid state fermentation process using polystyrene beads as inert support. The new fermentation system offered several advantages over the conventional systems, such as the yield of leachate with minimum viscosity and high specific activity for the target product besides facilitating the easy estimation of biomass. The enzyme thus produced was purified and characterised. It was active at physiological pH, showed high substrate specificity towards L - glutamine and had a Km value of 7.4 x 10-2 M. It also exhibited high salt and temperature tolerance indicating good scope for its industrial and therapeutic applications

Glucoamylase immobilized on montmorillonite: influence of nature of binding on surface properties of clay-support and activity of enzyme

Glucoamylase was immobilized on acid activated montmorillonite clay via two different procedures namely adsorption and covalent binding. The immobilized enzymes were characterized by XRD, NMR and N2 adsorption measurements and the activity of immobilized glucoamylase for starch hydrolysis was determined in a batch reactor. XRD shows intercalation of enzyme into the clay matrix during both immobilization procedures. Intercalation occurs via the side chains of the amino acid residues, the entire polypeptide backbone being situated at the periphery of the clay matrix. 27Al NMR studies revealed the different nature of interaction of enzyme with the support for both immobilization techniques. N2 adsorption measurements indicated a sharp drop in surface area and pore volume for the covalently bound glucoamylase that suggested severe pore blockage. Activity studies were performed in a batch reactor. The adsorbed and covalently bound glucoamylase retained 49% and 66% activity of the free enzyme respectively. They showed enhanced pH and thermal stabilities. The immobilized enzymes also followed Michaelis–Menten kinetics. Km was greater than the free enzyme that was attributed to an effect of immobilization. The immobilized preparations demonstrated increased reusability as well as storage stability.

Acid activated montmorillonite: an efficient immobilization support for improving reusability, storage stability and operational stability of enzymes

Three enzymes, α-amylase, glucoamylase and invertase, were immobilized on acid activated montmorillonite K 10 via two independent techniques, adsorption and covalent binding. The immobilized enzymes were characterized by XRD, N2 adsorption measurements and 27Al MAS-NMR spectroscopy. The XRD patterns showed that all enzymes were intercalated into the clay inter-layer space. The entire protein backbone was situated at the periphery of the clay matrix. Intercalation occurred through the side chains of the amino acid residues. A decrease in surface area and pore volume upon immobilization supported this observation. The extent of intercalation was greater for the covalently bound systems. NMR data showed that tetrahedral Al species were involved during enzyme adsorption whereas octahedral Al was involved during covalent binding. The immobilized enzymes demonstrated enhanced storage stability. While the free enzymes lost all activity within a period of 10 days, the immobilized forms retained appreciable activity even after 30 days of storage. Reusability also improved upon immobilization. Here again, covalently bound enzymes exhibited better characteristics than their adsorbed counterparts. The immobilized enzymes could be successfully used continuously in the packed bed reactor for about 96 hours without much loss in activity. Immobilized glucoamylase demonstrated the best results.

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576