Rational design of deep eutectic solvents for the stabilization of dehydrogenases: an artificial neural network prediction approach

• Published in: Frontiers in chemistry Vol. 12; p. 1436049
• Main Authors: Radović, Mia, Jurinjak Tušek, Ana, Reiter, Tamara, Kroutil, Wolfgang, Cvjetko Bubalo, Marina, Radojčić Redovniković, Ivana
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 01.08.2024
• Subjects: Chemistry; COSMO-RS; deep eutectic solvents; dehydrogenases; enzymatic stability; neural networks; rational design; rational design; dehydrogenases; COSMO-RS; deep eutectic solvents; enzymatic stability; neural networks
• ISSN: 2296-2646; 2296-2646
• DOI: 10.3389/fchem.2024.1436049

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.