Enzymes In Pharmaceutical Industries Research Articles

Site Saturation Mutagenesis Applications on Candida methylica Formate Dehydrogenase.

In NADH regeneration, Candida methylica formate dehydrogenase (cmFDH) is a highly significant enzyme in pharmaceutical industry. In this work, site saturation mutagenesis (SSM) which is a combination of both rational design and directed evolution approaches is applied to alter the coenzyme specificity of NAD+-dependent cmFDH from NAD+ to NADP+ and increase its thermostability. For this aim, two separate libraries are constructed for screening a change in coenzyme specificity and an increase in thermostability. To alter the coenzyme specificity, in the coenzyme binding domain, positions at 195, 196, and 197 are subjected to two rounds of SSM and screening which enabled the identification of two double mutants D195S/Q197T and D195S/Y196L. These mutants increase the overall catalytic efficiency of NAD+ to 5.6 × 104-fold and 5 × 104-fold value, respectively. To increase the thermostability of cmFDH, the conserved residue at position 1 in the catalytic domain of cmFDH is subjected to SSM. The thermodynamic and kinetic results suggest that 8 mutations on the first residue can be tolerated. Among all mutants, M1L has the best residual activity after incubation at 60°C with 17%. These studies emphasize that SSM is an efficient method for creating “smarter libraries” for improving the properties of cmFDH.

A tryptophan residue is identified in the substrate binding of penicillin G acylase from Kluyvera citrophila

Penicillin acylases are important enzymes in pharmaceutical industry for the production of semi-synthetic β-lactam antibiotics via the key intermediate 6-aminopenicillanic acid. The penicillin G acylase purified from Kluyvera citrophila ( KcPGA) on modification with tryptophan-specific reagents such as N-bromo succinamide (NBS) and 2-hydroxy 5-nitrobenzylbromide (HNBB) showed partial loss of activity and substrate protection. Various solute quenchers and substrate were used to probe the microenvironment of the putative reactive tryptophan through fluorescence quenching. Homology modeling of KcPGA structure has been carried out. Docking substrate on this modeled KcPGA structure identifies the tryptophan residue that is directly influenced by substrate binding. To confirm the biological significance of this particular tryptophan, we did a sequence comparison of PGAs from various organisms. The sequence alignment clustered the matches into two sets, those closer to (>40% identical) KcPGA and had the tryptophan of interest present in them formed the first set, while those less identical (<30%) to KcPGA and the particular tryptophan absent in them formed the second set. It is clear from the reported kinetic parameters of representative members of these two sets that the affinity for penicillin G (penG) of the former class is several times better. Thus, based on our studies we suggest that the tryptophan residue in the identified position is important for binding substrate penG by the acylases.

Experimental studies of network parameters and operational variables on recurrent backpropagation neural network adaptive control of penicillin acylase fermentation by Arthrobacter viscosus

This work is to investigate the on-line control of the fermentation by Arthrobacter viscosus. This species of bacteria can secrete penicillin acylase which is a key enzyme in pharmaceutical industry. The growth of more cells during the fermentation will obtain more enzyme. Both the enzyme activity and the cell growth are rather sensitive to the change of pH. Once the pH during a fermentation is not properly controlled, the decay of cells' activity will irreversibly occur. Two peristaltic pumps for supplying acidic and basic solutions, respectively, were connected for the regulation of pH. With superior ability in identification and prediction of dynamic time series, recurrent backpropagation network (RBPN), instead of conventional controllers, was used as the adaptive controller model for the fermentation with dynamic characteristics. Based on a 1-3-1 BPN, a corresponding 4-4-1 RBPN was determined. The deviation of the pH measured at current time from the set point of 7.0, denoted as Δ pH(t), was chosen as the input node of the network controller. The output node of this network controller was the predicted flow rate of the peristaltic pump for next control time interval. Such a model was operated by two phases. During the first phase, the network was set as the process model and trained by a fixed set of on-line acquired data. During the second phase, the network was stopped learning and switched to become a predictor, the predicted control action was hence obtained. The optimum sampling time was determined experimentally. To enhance the effective computation of this network, the number of training data was limited. A moving-window type of supplying training data to the network was applied for the on-line learning. The window size was also determined for each learning. With properly chosen network parameters as well as operation conditions, pH of the fermentation was thus well controlled by the RBPN controller.