Ideas Lab: USPRD: De Novo Design and Evolution of Enzymes for Biomass Upcycling to Surfactants and Fuels

De Novo Design and Evolution of Enzymes for Biomass Upcycling to Surfactants and Fuels

This project aims to transform abundant plant materials into high-value products like fuels, oils, and soaps. By using artificial intelligence (AI) and machine learning, the research focuses on enhancing the activity of key enzymes that are needed to convert waste biomass, such as plant materials, into commodity chemicals through protein design. This project introduces a novel strategy to meet supply chain goals through biomanufacturing.

Collaborations with industry partners will promote real-world use of these technologies in the U.S. bioeconomy. Additionally, the project emphasizes outreach and workforce development, providing opportunities to train the next generation of synthetic biologists.

The research employs advanced computational and experimental approaches to design and optimize enzymes for biomass upcycling. AI-guided tools, including machine learning models and molecular dynamics simulations, will be used to enhance enzyme activity, stability, and substrate specificity. The project targets enzyme classes to convert fatty acids into hydrocarbons for fuels, lubricants, and surfactants.

Directed evolution will further refine these enzymes for industrial scalability. The research integrates enzymology, biocatalysis, and computational chemistry, leveraging molecular dynamics, density functional theory, and spectroscopic analysis to establish foundational principles of de novo enzyme design. Collaboration with industrial partners ensures that these AI-enabled biocatalysts are implemented in scalable production systems.

This interdisciplinary effort not only advances biomanufacturing capabilities but also sets a precedent for applying computational enzyme design to other challenges in chemistry.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.