Evaluation of Driving Forces for Protein Partition in PEG-Salt Aqueous Two-Phase Systems and Optimization by Design of Experiments

Anna Glyk, D. Solle, T. Scheper, S. Beutel
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引用次数: 4

Abstract

In the present study, the partitioning of four model proteins (bovine serum albumin, ovalbumin, α-chymotrypsin and lysozyme) was investigated in polyethylene glycol (PEG)-salt aqueous two-phase systems (ATPS) addressing the effects of PEG molecular weight (MW) and concentration, phase-forming salt type (potassium phosphate/sodium citrate (KPP/NaCit)) and concentration, sodium chloride (NaCl) concentration and pH, as well as MW, isoelectric point, charge and hydrophobicity of the proteins. It was found that protein partitioning was influenced by different coexisting effects, such as hydrophobic and electrostatic interactions, related to the system parameters and physicochemical/surface properties of proteins. Moreover, a rigorous analysis and optimization of the significant forces for protein partitioning in PEG-salt ATPS was performed by design of experiments (DoE). Initially, KPP/NaCit, NaCl and PEG concentrations were considered as statistically significant factors by a fractional factorial design. Subsequently, the optimal values of the significant factors were determined by a central composite face-centered design coupled with response surface methodology. Furthermore, linear and quadratic models were obtained in the experimental designs, respectively, and evaluated by statistical regression analysis and analysis of variance. Finally, a validation of the quadratic model confirmed a good correlation between predicted and experimental results, thus verifying the validity of the model.
聚乙二醇-盐两水相体系中蛋白质分配驱动力的评价及实验设计优化
在本研究中,研究了四种模型蛋白(牛血清白蛋白、卵清蛋白、α-凝乳胰蛋白酶和溶菌酶)在聚乙二醇(PEG)-盐双水相体系(ATPS)中的分配,研究了PEG分子量(MW)和浓度、成相盐类型(磷酸钾/柠檬酸钠(KPP/NaCit))和浓度、氯化钠(NaCl)浓度和pH,以及蛋白质的分子量、等电点、电荷和疏水性的影响。研究发现,蛋白质的分配受到不同共存效应的影响,如疏水和静电相互作用,这与系统参数和蛋白质的物理化学/表面性质有关。此外,通过实验设计(DoE)对PEG-salt ATPS中蛋白质分配的重要力进行了严格的分析和优化。最初,通过分数因子设计,KPP/NaCit、NaCl和PEG浓度被认为是具有统计学意义的因素。然后,采用中心复合面心设计结合响应面法确定显著因子的最优值。在实验设计中分别建立线性模型和二次模型,并通过统计回归分析和方差分析进行评价。最后,对二次模型进行了验证,证实了预测结果与实验结果具有良好的相关性,从而验证了模型的有效性。
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