Search

Crystallization has recently emerged as a suitable process for the manufacture of biocatalysts in the form of cross-linked enzyme crystals (CLECs) or for the recovery of proteins from fermentation broths. In both instances it is essential to define conditions which control crystal size and habit, and that yield a reliable recovery of the active protein. Experiments to define the crystallization conditions usually depend on a factorial design (either incomplete or sparse matrix) or reverse screening techniques. In this work, we describe a simple procedure that allows the effect of three factors, for example protein concentration, precipitant concentration and pH, to be varied simultaneously and smoothly over a wide range. The results are mapped onto a simple triangular diagram where a 'window of crystallization' is immediately apparent, and that conveniently describes variations either in the crystal features, such as their yield, size, and habit, or in the recovery of biological activity. The approach is illustrated with two enzymes, yeast alcohol dehydrogenase (ADH I) and Candida rugosa lipase. For ADH the formation of two crystal habits (rod and hexagonal) could be controlled as a function of pH (6.5-10) and temperature (4-25 degrees C). At pH 7, in 10 to 16% w/v polyethylene glycol (PEG) 4000, only rod-shaped crystals formed whereas at pH 8, in 10 to 14% w/v PEG, only hexagonal crystals existed. For both enzymes, catalyst recovery was greatest at high crystallization agent concentrations and low protein concentration. For ADH, the greatest activity recovery was 87% whereas for the lipase crystals, by using 45% v/v 2-methyl-2,4-pentanediol (MPD) as the crystallization agent, a crystal recovery of 250 crystals per µl was obtained. For the lipase system, the use of crystal seeding was also shown to increase the crystal recovery by up to a factor of four. From the crystallization windows, the original conditions based on literature precedent (35% v/v MPD, 1 mM CaCl(2), 1.8 mg protein/ml) were altered (47.5% v/v MPD, 2 mM CaCl(2), 3 mg protein/ml). This led to an improved recovery of the lipase under conditions that scale reliably from 0.5 ml to 500 ml with no change in size, shape or recovery of the crystals themselves. Finally, these crystals were crosslinked with 5% v/v glutaraldehyde and mass and activity balances were calculated for the entire process of CLEC production. Up to 35% of the lipase activity present in the crude solid was finally recovered in the lipase CLECs after propan-2-ol fractionation, crystallization, and crosslinking.

Cross-linked enzyme crystals (CLECs®) are a novel form of immobilised biocatalyst designed for application in large-scale biotransformation processes. In this work we review the production and characterisation of CLECs® prepared from three enzymes (yeast alcohol dehydrogenase I (YADHI), Candida rugosa lipase and α-chymotrypsin) over a range of crystallisation and cross-linking conditions. Optimisation and control of the crystallisation process, with respect to crystal form and enzyme activity yield, was facilitated by the use of triangular crystallisation diagrams which allowed three parameters (e.g. protein concentration, precipitant concentration and pH) to be varied simultaneously. These diagrams showed regions, or 'crystallisation windows', in which particular crystal forms or optimal activity recoveries (up to 87%) could be obtained. They also identified conditions for reproducible scale-up of the lipase crystallisation from 0.5 to 500 mL scale.In order to evaluate the suitability of a particular bat...

This article describes the calibration of a spectroscopic scanning instrument for the measurement of selected contaminants in a complex biological process stream. Its use is for the monitoring of a process in which contaminants are to be removed selectively by flocculation from yeast cell homogenate. The main contaminants are cell debris, protein, and RNA. A low-cost instrument has been developed for sensitivity in the region of the NIR spectrum (from 1900 to 2500 nm) where preliminary work found NIR signatures from cell debris, protein, and RNA. Calibration models have been derived using a multivariate method for concentrations of these contaminants, such as would be found after the flocculation process. Two strategies were compared for calibrating the NIR instrument. In one case, samples were prepared by adding materials representative of the contaminants to clarified yeast homogenate so the contaminant levels were well known but outside the range of interest. In the other case, where samples were like those from the process stream after flocculation and floc removal, there was uncertainty of analysis of contaminant level, but the calibration was in the range of interest. Calibration using process stream samples gave results close to those derived from traditional assays. When the calibration models were used to predict the contaminant concentrations in previously unseen samples, the correlation coefficients between measurements and predictions were above 90% in all cases but one. The prediction errors were similar to the errors in the traditional assays.