| Abstrakt: |
The trend of employing machine learning methods has been increasing to develop promising biocatalysts. Leveraging the experimental findings and simulation data, these methods facilitate enzyme engineering and even the design of new-to-nature enzymes. This review focuses on the application of machine learning methods in the engineering of polyethylene terephthalate (PET) hydrolases, enzymes that have the potential to help address plastic pollution. We introduce an overview of machine learning workflows, useful methods and tools for protein design and engineering, and discuss the recent progress of machine learning-aided PET hydrolase engineering and de novo design of PET hydrolases. Finally, as machine learning in enzyme engineering is still evolving, we foresee that advancements in computational power and quality data resources will considerably increase the use of data-driven approaches in enzyme engineering in the coming decades. [ABSTRACT FROM AUTHOR] |
Full text from SpringerLink