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长期生物催化剂性能:机理预测和连续非等温测试。

Long-Term Biocatalyst Performance: Mechanistic Prediction and Continuous Non-Isothermal Testing.

机构信息

School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA.

Petit Institute of Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, Georgia, 30332-0363, USA.

出版信息

ChemSusChem. 2022 May 6;15(9):e202102701. doi: 10.1002/cssc.202102701. Epub 2022 Apr 20.

DOI:10.1002/cssc.202102701
PMID:35441829
Abstract

The assessment of the operational stability of biocatalysts by conventional direct determination of the total turnover number (TTN), a useful indicator of lifetime biocatalyst productivity, via continuous isothermal experiments tends to be time-consuming, material-intensive, and prone to disturbances, especially in case of rather stable catalysts. In the present work, we present and validate two alternative methods for estimating the TTN of a biocatalyst for any desired operating temperature. The first method is a mechanistic approach, built upon mathematical derivation of enzyme deactivation models derived from first principles, in which TTN can be calculated from two straightforward isothermal biochemical batch measurements. The second method relies on a few non-isothermal, continuous-mode experiments in conjunction with mathematical modeling to determine the intrinsic deactivation parameters of the biocatalyst. We verify both methods on the test case of TEM-1 β-lactamase-catalyzed penicillin G (Pen G) hydrolysis. Both alternative methods provide estimates of TTN which are typically within a factor of two to five or less of the values measured directly via lengthy, costly, and error-prone conventional isothermal aging tests. Therefore, both the mechanistic approach and the non-isothermal continuous approach are extremely valuable tools to enable calculation of catalyst cost contribution in continuous processing and to eliminate underperforming candidates in search of the most stable biocatalyst.

摘要

通过连续等温实验来评估生物催化剂的操作稳定性,通常使用总转化数(TTN)作为衡量生物催化剂寿命生产力的有用指标,但这种方法耗时、耗材料且容易受到干扰,特别是对于相当稳定的催化剂。在本工作中,我们提出并验证了两种替代方法,用于估算任何所需操作温度下生物催化剂的 TTN。第一种方法是一种基于酶失活动力学模型的机理方法,该模型是从第一性原理推导出来的,其中 TTN 可以从两个简单的等温生化批处理测量中计算得出。第二种方法依赖于几个非等温连续模式实验,并结合数学建模来确定生物催化剂的固有失活动力学参数。我们在 TEM-1 β-内酰胺酶催化青霉素 G(Pen G)水解的实例中验证了这两种方法。这两种替代方法都提供了 TTN 的估计值,这些值通常与通过冗长、昂贵且容易出错的传统等温老化实验直接测量的值相差一个数量级或五个数量级以内。因此,无论是机理方法还是非等温连续方法,都是非常有价值的工具,可以用于计算连续处理中的催化剂成本贡献,并消除表现不佳的候选物,以寻找最稳定的生物催化剂。

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