Machine learning-assisted enzyme engineering

Directed evolution and rational design are powerful strategies in protein engineering to tailor enzyme properties to meet the demands in academia and industry. Traditional approaches for enzyme engineering and directed evolution are often experimentally driven, in particular when the protein structure–function relationship is not available. Though they have been successfully applied to engineer many enzymes, these methods are still facing significant challenges due to the tremendous size of the protein sequence space and the combinatorial problem. It can be ascertained that current experimental techniques and computational techniques might never be able to sample through the entire protein sequence space and benefit from nature's full potential for the generation of better enzymes. With advancements in next generation sequencing, high throughput screening methods, the growth of protein databases and artificial intelligence, especially machine learning (ML), data-driven enzyme engineering is emerging as a promising solution to these challenges. To date, ML-assisted approaches have efficiently and accurately determined the quantitative structure-property/activity relationship for the prediction of diverse enzyme properties. In addition, enzyme engineering can be accelerated much faster than ever through the combination of experimental library generation and ML-based prediction. In this chapter, we review the recent progresses in ML-assisted enzyme engineering and highlight several successful examples (e.g., to enhance activity, enantioselectivity, or thermostability). Herein we explain enzyme engineering strategies that combine random or (semi-)rational approaches with ML methods and allow an effective reengineering of enzymes to improve targeted properties. We further discuss the main challenges to solve in order to realize the full potential of ML methods in enzyme engineering. Finally, we describe the current limitations of ML-assisted enzyme engineering, and our perspective on future opportunities in this growing field.