Machine learning for enzyme catalytic activity: current progress and future horizons.

Area of Science:

• Biotechnology and Biochemistry
• Computational Biology

Background:

• Enzyme catalysis offers sustainable and efficient industrial solutions.
• Optimizing enzyme catalytic activity is crucial but challenging.
• Machine learning (ML) models are increasingly used for enzyme property prediction.

Purpose of the Study:

• To review recent advancements in ML models for predicting enzyme catalytic activity.
• To analyze different modeling approaches and identify effective strategies.
• To highlight limitations and suggest future enhancements for predictive enzyme models.

Main Methods:

• Literature review of machine learning applications in enzyme catalysis.
• Analysis of predictive modeling strategies, including feature engineering and model architectures.
• Identification of common challenges such as dataset imbalance.

Main Results:

• Attention mechanisms, incorporating product information and temperature as features, and transfer learning are effective modeling strategies.
• Dataset imbalance remains a significant limitation in current models.
• Predictive models show promise for advancing enzyme and metabolic engineering.

Conclusions:

• Accurate ML predictors can significantly enhance enzyme engineering and biocatalysis optimization.
• Addressing limitations like dataset imbalance is key for future model development.
• The integration of advanced ML techniques will drive innovation in industrial enzymology.