Effect of active site mutation on pH activity and transglycosylation of Sulfolobus acidocaldarius β-glycosidase

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

The production, properties, and applications of thermostable steryl glucosidases

Extremophilic microorganisms are a rich source of enzymes, the enzymes which can serve as industrial catalysts that can withstand harsh processing conditions. An example is thermostable β-glucosidases that are addressing a challenging problem in the biodiesel industry: removing steryl glucosides (SGs) from biodiesel. Steryl glucosidases (SGases) must be tolerant to heat and solvents in order to function efficiently in biodiesel. The amphipathic nature of SGs also requires enzymes with an affinity for water/solvent interfaces in order to achieve efficient hydrolysis. Additionally, the development of an enzymatic process involving a commodity such as soybean biodiesel must be cost-effective, necessitating an efficient manufacturing process for SGases. This review summarizes the identification of microbial SGases and their applications, discusses biodiesel refining processes and the development of analytical methods for identifying and quantifying SGs in foods and biodiesel, and considers technologies for strain engineering and process optimization for the heterologous production of a SGase from Thermococcus litoralis. All of these technologies might be used for the production of other thermostable enzymes. Structural features of SGases and the feasibility of protein engineering for novel applications are explored.