Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels.

Biotechnology has revolutionized the development of sustainable energy sources by harnessing biomass as a feedstock for energy production. However, challenges such as recalcitrant feedstocks and inefficient metabolic pathways hinder the large-scale integration of renewable energy systems. Enzyme engineering has emerged as a powerful tool to address these challenges by enhancing enzyme activity, specificity, and stability. Generative machine learning (ML) models have shown great promise in accelerating protein design, allowing for the generation of novel protein sequences with desired properties by navigating vast spaces. This review paper aims to summarize the state of the art in generative models for protein design and how they can be applied to bioenergy applications, including the underlying architectures and training strategies. Additionally, it highlights the importance of high-quality datasets for training and evaluating generative models, organizes available datasets for generative protein design, and discusses the potential of applying generative models to strain design for bioenergy production. • Challenges in renewable energy production require novel bioengineering approaches. • Generative models for novel enzymes present a big advancement in enzyme engineering. • We discuss how VAEs, GANs, and transformers enable improved enzyme production. • The potential of generative model integration into strain design is emphasized. • The role of high-quality, diverse datasets in training & evaluation is highlighted. [ABSTRACT FROM AUTHOR]