可溶性预测模型在包涵体复性中的应用

目的 研究包涵体复性后可溶性的预测方法,区分复性类型,提高复性效率.方法 利用可溶性预测模型对43个疾病相关重组蛋白的表达进行预测,并采用双变性-复性方法对表达形成的包涵体进行复性.结果 预测有可溶性表达倾向的14个蛋白表达的包涵体.复性后均收率高、可溶性好.预测为高不溶性表达倾向的29个蛋白.表达的包涵体复性后高收率与低收率并存,对这两种收率包涵体特性的统计学分析显示理论等电点间存在显著性差异(P<0.05).结论 可溶性预测模型叮以应用于区分包涌体复性类型,预测复性后蛋白可溶性,为提高复性效率提供了新方法.同时提示蛋白的理论等电点在模型应用中有重要作用.

Abstract：

Objective To establish a prediction method for the refolding of inclusion bodies and classify refolding types of different inclusion bodies directly from their primary structure to improve the efficiency of high throughput refolding process. Methods Forty-three recombinant proteins performing important biological functions were expressed in E. coli. The probability of forming inclusion bodies of these proteins was predicted using Harrison's two parameter prediction model based on the proteins' amino acid composition. Subsequently, the proteins from the inclusion bodies were refolded using a double denaturation method that involved washing and denaturation in GdnHCl solution followed by denaturation in Urea solution and refolding through dilution. Results All the proteins were detected in the form of inclusion bodies using SDS-PAGE method. The proteins were divided into two types according to the results of both solubility prediction and refolding experiments. Fourteen proteins were predicted to have the dependency of soluble expression. The refolding yields of these inclusion bodies were up to 70%. Twenty-nine proteins were predicted to have the high dependency of insoluble expression, and their refolding yields could be higher than 70% and lower than 60%. Comparison of the characteristics between the proteins with high and low refolding yields showed that the theoretical pI was significantly different (P<0.05). Conclusions Harrison's two parameter prediction model has the value for potential application in classification of the inclusion bodies and prediction of solubility of proteins refolded from different inclusion bodies. This a novel method enhances the efficiency of high throughput refolding of inclusion bodies, and suggests that the theoretical pI of the proteins is an important parameter in the prediction of refolding yields.

Application of a prediction model in inclusion body refolding

Objective To establish a prediction method for the refolding of inclusion bodies and classify refolding types of different inclusion bodies directly from their primary structure to improve the efficiency of high throughput refolding process. Methods Forty-three recombinant proteins performing important biological functions were expressed in E. coli. The probability of forming inclusion bodies of these proteins was predicted using Harrison's two parameter prediction model based on the proteins' amino acid composition. Subsequently, the proteins from the inclusion bodies were refolded using a double denaturation method that involved washing and denaturation in GdnHCl solution followed by denaturation in Urea solution and refolding through dilution. Results All the proteins were detected in the form of inclusion bodies using SDS-PAGE method. The proteins were divided into two types according to the results of both solubility prediction and refolding experiments. Fourteen proteins were predicted to have the dependency of soluble expression. The refolding yields of these inclusion bodies were up to 70%. Twenty-nine proteins were predicted to have the high dependency of insoluble expression, and their refolding yields could be higher than 70% and lower than 60%. Comparison of the characteristics between the proteins with high and low refolding yields showed that the theoretical pI was significantly different (P<0.05). Conclusions Harrison's two parameter prediction model has the value for potential application in classification of the inclusion bodies and prediction of solubility of proteins refolded from different inclusion bodies. This a novel method enhances the efficiency of high throughput refolding of inclusion bodies, and suggests that the theoretical pI of the proteins is an important parameter in the prediction of refolding yields.