采用细胞破碎,热变性除杂蛋白,有机溶剂沉淀,DEAE-Sepharose Fast Flow柱层析等方法从酵母中纯化酵母蔗糖酶,用5种化学修饰剂PMSF、SUAN、NBS、DTT、EDTA及几种金属离子对其进行化学修饰。结果表明:酶分子中的丝氨酸残基和金属离子与酶...采用细胞破碎,热变性除杂蛋白,有机溶剂沉淀,DEAE-Sepharose Fast Flow柱层析等方法从酵母中纯化酵母蔗糖酶,用5种化学修饰剂PMSF、SUAN、NBS、DTT、EDTA及几种金属离子对其进行化学修饰。结果表明:酶分子中的丝氨酸残基和金属离子与酶活力无关,赖氨酸残基和半胱氨酸残基对酶活力有一定贡献,但不位于酶的活性中心;而色氨酸残基是酶活性中心的必需基团。展开更多
A hybrid GMDH neural network model has been developed in order to predict the partition coefficients of invertase from Baker's yeast. ATPS experiments were carried out changing the molar average mass of PEG(1500–...A hybrid GMDH neural network model has been developed in order to predict the partition coefficients of invertase from Baker's yeast. ATPS experiments were carried out changing the molar average mass of PEG(1500–6000 Da), p H(4.0–7.0), percentage of PEG(10.0–20.0 w/w), percentage of MgSO_4(8.0–16.0 w/w), percentage of the cell homogenate(10.0–20.0 w/w) and the percentage of MnSO_4(0–5.0 w/w) added as cosolute. The network evaluation was carried out comparing the partition coefficients obtained from the hybrid GMDH neural network with the experimental data using different statistical metrics. The hybrid GMDH neural network model showed better fitting(AARD = 32.752%) as well as good generalization capacity of the partition coefficients of the ATPS than the original GMDH network approach and a BPANN model. Therefore hybrid GMDH neural network model appears as a powerful tool for predicting partition coefficients during downstream processing of biomolecules.展开更多
文摘采用细胞破碎,热变性除杂蛋白,有机溶剂沉淀,DEAE-Sepharose Fast Flow柱层析等方法从酵母中纯化酵母蔗糖酶,用5种化学修饰剂PMSF、SUAN、NBS、DTT、EDTA及几种金属离子对其进行化学修饰。结果表明:酶分子中的丝氨酸残基和金属离子与酶活力无关,赖氨酸残基和半胱氨酸残基对酶活力有一定贡献,但不位于酶的活性中心;而色氨酸残基是酶活性中心的必需基团。
基金CAPES and Brazilian National Council of Research (CNPq) (Grant 407684/2013-1) for the financial support
文摘A hybrid GMDH neural network model has been developed in order to predict the partition coefficients of invertase from Baker's yeast. ATPS experiments were carried out changing the molar average mass of PEG(1500–6000 Da), p H(4.0–7.0), percentage of PEG(10.0–20.0 w/w), percentage of MgSO_4(8.0–16.0 w/w), percentage of the cell homogenate(10.0–20.0 w/w) and the percentage of MnSO_4(0–5.0 w/w) added as cosolute. The network evaluation was carried out comparing the partition coefficients obtained from the hybrid GMDH neural network with the experimental data using different statistical metrics. The hybrid GMDH neural network model showed better fitting(AARD = 32.752%) as well as good generalization capacity of the partition coefficients of the ATPS than the original GMDH network approach and a BPANN model. Therefore hybrid GMDH neural network model appears as a powerful tool for predicting partition coefficients during downstream processing of biomolecules.
