Radović Mia, Jurinjak Tušek Ana, Reiter Tamara, Kroutil Wolfgang, Cvjetko Bubalo Marina, Radojčić Redovniković Ivana
Faculty of Food technology and Biotechnology, University of Zagreb, Zagreb, Croatia.
Institute of Chemistry, University of Graz, Field of Excellence BioHealth, BioTechMed Graz, Graz, Austria.
Front Chem. 2024 Aug 1;12:1436049. doi: 10.3389/fchem.2024.1436049. eCollection 2024.
Stabilized enzymes are crucial for the industrial application of biocatalysis due to their enhanced operational stability, which leads to prolonged enzyme activity, cost-efficiency and consequently scalability of biocatalytic processes. Over the past decade, numerous studies have demonstrated that deep eutectic solvents (DES) are excellent enzyme stabilizers. However, the search for an optimal DES has primarily relied on trial-and-error methods, lacking systematic exploration of DES structure-activity relationships. Therefore, this study aims to rationally design DES to stabilize various dehydrogenases through extensive experimental screening, followed by the development of a straightforward and reliable mathematical model to predict the efficacy of DES in enzyme stabilization. A total of 28 DES were tested for their ability to stabilize three dehydrogenases at 30°C: ()-alcohol dehydrogenase from (ADH-A), ()-alcohol dehydrogenase from (Lk-ADH) and glucose dehydrogenase from (GDH). The residual activity of these enzymes in the presence of DES was quantified using first-order kinetic models. The screening revealed that DES based on polyols serve as promising stabilizing environments for the three tested dehydrogenases, particularly for the enzymes Lk-ADH and GDH, which are intrinsically unstable in aqueous environments. In glycerol-based DES, increases in enzyme half-life of up to 175-fold for Lk-ADH and 60-fold for GDH were observed compared to reference buffers. Furthermore, to establish the relationship between the enzyme inactivation rate constants and DES descriptors generated by the Conductor-like Screening Model for Real Solvents, artificial neural network models were developed. The models for ADH-A and GDH showed high efficiency and reliability (R > 0.75) for screening of the enzyme inactivation rate constants based on DES descriptors. In conclusion, these results highlight the significant potential of the integrated experimental and approach for the rational design of DES tailored to stabilize enzymes.
由于其增强的操作稳定性，稳定化酶对于生物催化的工业应用至关重要，这导致酶活性延长、成本效益提高，从而使生物催化过程具有可扩展性。在过去十年中，大量研究表明，深共熔溶剂（DES）是出色的酶稳定剂。然而，寻找最佳DES主要依赖于试错法，缺乏对DES构效关系的系统探索。因此，本研究旨在通过广泛的实验筛选合理设计DES以稳定各种脱氢酶，随后开发一个简单可靠的数学模型来预测DES在酶稳定化中的效果。总共测试了28种DES在30°C下稳定三种脱氢酶的能力：来自[具体来源1]的（）-醇脱氢酶（ADH-A）、来自[具体来源2]的（）-醇脱氢酶（Lk-ADH）和来自[具体来源3]的葡萄糖脱氢酶（GDH）。使用一级动力学模型对这些酶在DES存在下的残余活性进行了定量。筛选结果表明，基于多元醇的DES对三种测试脱氢酶而言是有前景的稳定化环境，特别是对于在水性环境中本质上不稳定的Lk-ADH和GDH酶。与参考缓冲液相比，在基于甘油的DES中，观察到Lk-ADH的酶半衰期增加高达175倍，GDH的酶半衰期增加60倍。此外，为了建立酶失活速率常数与由真实溶剂类导体筛选模型生成的DES描述符之间的关系，开发了人工神经网络模型。ADH-A和GDH的模型在基于DES描述符筛选酶失活速率常数方面显示出高效率和可靠性（R>0.75）。总之，这些结果突出了综合实验和[具体方法]方法在合理设计用于稳定酶的DES方面的巨大潜力。
