Protein purification by using immobilized metal ion affinity (IMA) adsorbent - Fidelia Sawai, anak Michael Mulok (2009)
Effective separation and purification of proteins has been an important issue in the biomedical and pharmaceutical industries. A novel protein adsorption has been developed in biotechnology to achieve highly efficient and economical separation processes. Application in separation and purification processes often used the ability of zeolites and other molecular sieves to exclude molecules too large to enter the pores and admit smaller ones. In this study, two zeolites which are H-Y and H-Beta have been modified by adding a type of metal into each zeolite to enhance the performance of the zeolites. Three types of metals were used. They were nickel oxide, ferum oxide and zirconium oxide. The zeolite is used as an immobilized metal ion affinity stationary phase for protein purification. The adsorption of Bovine Serum Albumin (BSA) protein using modified zeolites was studied. The effect of pH on adsorption capacity was studied at three different pHs, namely 3, 5 and 8. It is found that the adsorption capacity is the highest at pH 5. Increase in pH higher than the pI leads to the decrease in the adsorption capacity. This is caused by electrostatics repulsion between protein and the surface of adsorbent. Bovine serum albumin concentration was analyzed by UV/VIS Spectrophotometer. It is obvious that as the concentration is higher, the adsorption of Bovine Serum Albumin (BSA) protein is also higher. This is because as the sample is more concentrated, it contained more protein so the adsorption will also be increased. It can be concluded that the most efficient zeolite is H-Beta combined with zirconium oxide. Ismail et al. (2005) has said that the molecular sieve H-Beta zeolite has been explored for its ability to adsorb proteins from aqueous solution in batch experiment. Zirconium oxide is the most efficient metal compared to nickel oxide and ferum oxide. The adsorption isotherms are confirmed to be ideal to the Langmuir model.