AI-Driven Enzyme Engineering: A Data-Efficient Framework for Designing Next-Generation Biocatalysts

Utkarsh Agrawal

Abstract: Enzyme engineering has traditionally relied on directed evolution, rational design, and semi-rational mutagenesis to improve catalytic activity, stability, selectivity, and substrate specificity. Although these approaches have produced important biocatalysts, they remain constrained by the enormous size of protein sequence space and the experimental cost of screening variants. Recent advances in artificial intelligence have introduced protein language models (PLMs), generative models, and machine-learning-assisted optimization as new approaches for navigating this sequence space. PLMs learn statistical representations of protein sequences from large evolutionary datasets and can be adapted for predicting protein properties and identifying potentially beneficial mutations. More recent approaches integrate evolutionary information with structural and biophysical knowledge, improving performance when experimental data are limited. This paper reviews the transition from conventional enzyme engineering toward AI-assisted protein engineering and proposes a data-efficient framework integrating PLMs, structure prediction, active learning, and experimental validation. The framework consists of sequence-space generation, computational filtering, structure- and function-aware ranking, targeted experimental validation, and iterative model updating. Particular attention is given to limited experimental data, epistasis, model interpretability, and the gap between predicted and experimentally observed enzyme performance. The paper argues that hybrid systems combining evolutionary information, protein structure, biophysical simulation, and experimental feedback are likely to be more reliable than any single model. Such systems could reduce laboratory screening requirements and accelerate enzyme discovery for sustainable chemical synthesis, pharmaceutical manufacturing, environmental biotechnology, and synthetic biology.

Keywords: Artificial Intelligence, Biocatalysis, Enzyme Engineering, Protein Language Models, Generative Protein Design, Active Learning, Protein Engineering, Machine Learning