Machine learning in predictive biocatalysis: A comparative review of methods and applications

Area of Science:

• Biotechnology and Biochemistry
• Computational Biology
• Enzyme Engineering

Background:

• Machine learning (ML) has revolutionized predictive biocatalysis.
• Current ML approaches enable enzyme function prediction, discovery, and reaction modeling.

Purpose of the Study:

• To provide a comparative analysis of ML methodologies in predictive biocatalysis.
• To highlight the synergy between computational tools and biochemical data.
• To discuss advancements in enzyme classification, reaction annotation, and kinetic parameter prediction.

Main Methods:

• Review of various ML approaches including deep neural networks, convolutional networks, graph-based architectures, and transformers.
• Analysis of data integration, representation, and featurization techniques.
• Examination of validation methods for ML models in biocatalysis.

Main Results:

• ML accelerates enzyme discovery and the development of sustainable biocatalytic processes.
• Different ML architectures offer unique strengths and limitations for specific biocatalysis tasks.
• Integration of large-scale data and robust validation are crucial for success.

Conclusions:

• ML is pivotal for connecting computational insights with enzyme engineering.
• Future applications span synthetic biology, metabolic engineering, and green biocatalysis.
• Continued advancements in ML will drive innovation in biocatalysis.