Machine Learning in Enzyme Engineering

TLDR

Enzyme engineering is essential for creating efficient biocatalysts, and machine learning is increasingly used to predict protein structure, stability, function, solubility, and substrate specificity. This perspective reviews the current state of databases and predictive methods in enzyme engineering, identifies limitations and challenges, and outlines future directions for developing efficient biocatalysts. The authors analyze training and validation datasets and methods, discuss recent experimental and theoretical advances that may address current challenges, and evaluate their potential impact on enzyme design.

Abstract

Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have been increasingly applied to find patterns in data that help predict protein structures, improve enzyme stability, solubility, and function, predict substrate specificity, and guide rational protein design. In this Perspective, we analyze the state of the art in databases and methods used for training and validating predictors in enzyme engineering. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges. We also present our view on possible future directions for developing the applications to the design of efficient biocatalysts.