Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis

Area of Science:

• Biocatalysis and Enzyme Engineering
• Computational Biology and Bioinformatics
• Synthetic Biology

Background:

• Machine learning (ML) is emerging as a transformative technology in biocatalysis.
• ML models analyze complex biological data, including amino acid sequences, protein structures, and functional information.
• Integration with advances in DNA synthesis, sequencing, automation, and screening amplifies ML's impact.

Purpose of the Study:

• To review recent applications of ML in enzyme discovery, design, and engineering.
• To identify current challenges and emerging solutions in ML-driven biocatalysis.
• To discuss barriers to the widespread adoption of ML in the biocatalysis community.

Main Methods:

• Review of recent literature on ML applications in enzyme discovery, design, and engineering.
• Analysis of ML model capabilities in navigating protein fitness landscapes.
• Discussion of integration strategies with laboratory automation and high-throughput screening.

Main Results:

• ML facilitates navigation of complex fitness landscapes for enzyme optimization.
• ML aids in discovering novel enzymes from databases and designing biocatalysts de novo.
• ML significantly increases the speed and efficiency of enzyme development.

Conclusions:

• ML is a key technology poised to revolutionize enzyme development.
• Addressing adoption barriers and fostering interdisciplinary collaboration is crucial for maximizing ML's potential in biocatalysis.
• Best practices for collaboration are suggested to accelerate ML integration.