Machine learning for enzyme engineering, selection and design

Area of Science:

• Biochemistry and Computational Biology
• Enzyme Engineering and Design

Background:

• Machine learning (ML) offers powerful computational solutions for complex biological tasks.
• Enzyme engineering, selection, and design are critical areas benefiting from advanced computational tools.

Purpose of the Study:

• To review and analyze machine learning applications in enzyme engineering.
• To compare ML tools for enzyme function and site identification.
• To detail ML methods for optimizing enzyme properties and design.

Main Methods:

• Comparative analysis of machine learning algorithms and datasets.
• Review of tools for enzyme function and active site prediction.
• Examination of ML approaches for optimizing enzyme environments and reactants.

Main Results:

• Machine learning effectively identifies enzyme functions and responsible sites.
• ML methods enhance optimization of enzyme experimental properties.
• Recent advances in enzyme systems and de novo enzyme design are highlighted.

Conclusions:

• Machine learning is a valuable tool for enzyme engineering and design.
• Understanding ML algorithms and data is crucial for future enzyme design.
• This review provides insights for researchers applying ML in enzymology.