A BIDISPERSE MODEL TO STUDY THE HYDROLYSIS OF MALTOSE USING GLUCOAMYLASE IMMOBILIZED IN SILICA AND WRAPPED IN PECTIN GEL

In this work, a bidisperse model is built to represent the hydrolysis of maltose using immobilized glucoamylase. The experimental set is a mixed-batch reactor, maintained at 30ºC, with pectin gel spherical particles that contain enzyme immobilized in macroporous silica. The possibility of substrate adsorption on the pectin gel is also studied because this phenomenon may result in smaller values of diffusivity. Equilibrium assays are then performed for different substrates (maltose, lactose and glucose) at different temperatures and pHs. These assays show that adsorption on the pectin gel is not important for the three dextrins analysed. The bidisperse model presents a good fit with the experimental data, when using previously-estimated kinetic and mass transfer parameters (Gonçalves et al., 1997). This result shows that the methodology used (wrapping the silica in pectin gel) is appropriate for experimental studies with silica, since it allows a higher degree of agitation without causing shearin

A simplified kinetic model for the side reactions occurring during the enzymatic synthesis of ampicillin

This work presents a kinetic study of the side reactions of the ampicillin enzymatic synthesis, from phenylglycine methyl ester and 6-aminopenicillanic acid using penicillin G acylase immobilized on agarose. A Michaelis-Menten model with competitive inhibition was fitted to initial rates of ester and antibiotic hydrolysis, at pH 6.5 and 25ºC. Inherent kinetic parameters were estimated for low enzymatic loads, to assure that diffusional resistance was not important. It was observed that ampicillin inhibits the hydrolysis of PGME, but the inhibitory effect of the ester on ampicillin hydrolysis was almost negligible. The obtained parameters were: k cat1= 0.025 mM/UI min, Km1 = 155.4mM, K AE = 16.18mM, k cat2= 4.67x10-3 mM/UI min, Km2 = 11.47, K EA = 0.68 mM. Parameter values are in the range reported in the literature, except for Km1, which is much higher. The large confidence interval for this parameter denotes that the model presents low sensitivity with respect to it

Synergy of ion exchange and covalent Reaction: immobilization of penicillin G acylase on herofunctional amino-vinyl sulfone agarose

Agarose-vinyl sulfone (VS) beads have proven to be a good support to immobilize several enzymes. However, some enzymes are hardly immobilized on it. This is the case of penicillin G acylase (PGA) from Escherichia coli, which is immobilized very slowly on this support (less than 10% in 24 h). This enzyme is also not significantly adsorbed in aminated MANAE-agarose beads, an anionic exchanger. In this study, MANAE-agarose beads were modified with divinyl sulfone (DVS) to produce MANAE-vinyl sulfone (VS) agarose beads. When PGA was immobilized on this support, the enzyme was fully immobilized in less than 1.5 h. PGA cannot be released from the support by incubation at high ionic strength, suggesting that the enzyme was rapidly immobilized in a covalent fashion. Considering that the amount of reactive VS groups was only marginally increased, the results indicated some cooperative effect between the anion exchange on the amine groups of the support, probably as the first step of the process, and the covalent attachment of the previously adsorbed PGA molecules. The covalent reaction of the previously adsorbed enzyme molecules proceeds much more efficiently than that of the free enzyme, due to the proximity of the reactive groups of the support and the enzyme. Finally, the steps of immobilization, incubation, and blocking with different agents were studied to determine the effects on final activity/stability. The stability of PGA immobilized on this new catalyst was improved with respect to the VS-agarose prepared at low ionic strengthDepto. de Ingeniería Química y de MaterialesFac. de Ciencias QuímicasTRUEpu

Stabilization of penicillin G acylase by immobilization on glutaraldehyde-activated chitosan

The objective of this work was to study enzyme immobilization on chitosan activated with glutaraldehyde, aiming to produce a cheap biocatalyst. Two different immobilization strategies were studied: one-point and multipoint covalent attachment to the solid matrix. The multipoint covalent attachment derivative had an 82% immobilization yield. It was 4.9-fold more stable than the free enzyme at 50°C and 4.5-fold more stable than soluble enzyme at pH 10.0. The one-point derivative had an 85% immobilization yield. It was 2.7-fold more stable than the free enzyme at 50°C and 3.8-fold more stable than soluble PGA at pH 10.0. Results indicated that chitosan can be loaded with PGA above 330 IU/g. Intraparticle diffusive effects, however, limited hydrolysis of penicillin G catalyzed by those derivatives at 37°C and 25°C. Operational stability assays were performed and the multipoint derivative exhibited a half-life of 40 hours

Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling

Neural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with Statistica<FONT FACE=Symbol>Ò</FONT> were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1) noncompetitive inhibition by substrate and competitive by product and 2) uncompetitive inhibition by substrate and competitive by product; however, these models’ correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior