Optimization of sequential purification of beta-glucosidase from tricoderma reesei in aqueous two-phase system
Otto H. York Department of Chemical Engineering
Doctor of Philosophy
Loney, Norman W.
Knox, Dana E.
Malhotra, Sanjay V.
Aqueous two-phase system
Artificial neural networks
A novel sequential technique was developed for the purification of a valuable enzyme, beta-glucosidase, from microorganism Tricoderma reesei. The fungus T. reesei produces cellulose degrading enzymes, called cellulases: beta-glucosidase, endo-glucanase and exo-glucanase and low molecular weight proteins. For specific applications, the enzyme must be separated from other contaminants. The sequential technique, that included affinity precipitation with chitosan followed by separation with an aqueous two-phase system (ATPS), was implemented for the purification of beta-glucosidase from the culture filtrate of T. reesei.
The cultivation medium (nutrient) was optimized for the production of betaglucosidase from T. reesei cell culture. Treatment of the crude extract of T. reesei with chitosan resulted in the precipitation of endo and exo-glucanases. During this separation step, beta-glucosidase activity was completely recovered in the supematant. The enzyme was further purified from other proteins by partitioning in aqueous two-phase systems. Preliminary investigation with pure beta-glucosidase showed that the ATPS composed of PEG 4000, Potassium Phosphate salt and water is the best system for extracting the enzyme. The influences of system conditions, such as system pH and temperature, on the partition coefficients of beta-glucosidase and total proteins were evaluated in order to determine the most favorable condition for the purification of the enzyme from the culture filtrate. For the range of pH (6.0-7.5) and temperature (25-55 0C) studied, a positive correlation was obtained between these two variables and the partition coefficients.
The development of reliable tools, that can predict equilibrium phase compositions and the partitioning behavior of the system components, is critical for protein purification in ATPS. Artificial Neural-Network models (ANN) offered a remarkable performance to predict equilibrium phase compositions and beta-glucosidase partition coefficients. In addition, the pilot plant study with the culture filtrate was carried out in a continuous two-stage counter-current aqueous two-phase extractor system. The pilot plant experiments demonstrated the feasibility of the continuous counter current extraction process of ATPS for large-scale purification of beta-glucosidase.
njit-etd2005-066 (120 pages ~ 4,500 KB pdf)
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Created September 6, 2005