Abstract & Details
Description
Award ID: 2449206
AI-designed Enzymes using Non-natural Cofactors for the Production of Bio-based Acrylates The project aims to transform the production of molecules known as acrylates with artificial intelligence (AI)-driven biotechnology. Acrylates are expensive components of paints, Plexiglas, and super-absorbent materials. By using engineered enzymes and innovative, low-cost chemicals, the process will convert affordable plant material into these high-value products instead. The team of experts from academia and industry will test this technology at pilot scales, demonstrating its potential to strengthen the U.S. bioeconomy. The project will also train a new workforce in protein design and synthetic biology. This project leverages artificial intelligence and advanced protein engineering to develop a scalable, cost-effective, and cell-free biocatalytic process for producing methylene butyrolactone (MBL), a bio-based acrylate monomer. The work begins with mining and redesigning natural enzymes to catalyze the conversion of itaconate to MBL using machine learning-guided frameworks and high-throughput Design-Build-Test-Learn cycles. Enzymes will be further engineered to utilize a non-natural cofactor, which provides enhanced stability, cost efficiency, and suitability for industrial conditions while overcoming traditional biochemical limitations. Subsequent phases involve strain engineering for enzyme production at titers exceeding 1 g/L in E. coli and demonstrating the complete process in 1-5L bioreactors. By coupling enzymes with chemical conversion processes, the project integrates enzyme performance, cofactor recycling, and chemical efficiency to achieve over 90% yield at industrial scales. Beyond enabling cost-competitive MBL production, this project overcomes key limitations of natural cofactor-dependent enzymes, advancing cell-free biocatalysis and its broader applications in synthetic pathways. The resulting platform will drive innovation across the bioeconomy, transforming industrial biotechnology through enhanced scalability and efficiency. 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.
NSF Program Director: Waleed Nasser
AI-designed Enzymes using Non-natural Cofactors for the Production of Bio-based Acrylates The project aims to transform the production of molecules known as acrylates with artificial intelligence (AI)-driven biotechnology. Acrylates are expensive components of paints, Plexiglas, and super-absorbent materials. By using engineered enzymes and innovative, low-cost chemicals, the process will convert affordable plant material into these high-value products instead. The team of experts from academia and industry will test this technology at pilot scales, demonstrating its potential to strengthen the U.S. bioeconomy. The project will also train a new workforce in protein design and synthetic biology. This project leverages artificial intelligence and advanced protein engineering to develop a scalable, cost-effective, and cell-free biocatalytic process for producing methylene butyrolactone (MBL), a bio-based acrylate monomer. The work begins with mining and redesigning natural enzymes to catalyze the conversion of itaconate to MBL using machine learning-guided frameworks and high-throughput Design-Build-Test-Learn cycles. Enzymes will be further engineered to utilize a non-natural cofactor, which provides enhanced stability, cost efficiency, and suitability for industrial conditions while overcoming traditional biochemical limitations. Subsequent phases involve strain engineering for enzyme production at titers exceeding 1 g/L in E. coli and demonstrating the complete process in 1-5L bioreactors. By coupling enzymes with chemical conversion processes, the project integrates enzyme performance, cofactor recycling, and chemical efficiency to achieve over 90% yield at industrial scales. Beyond enabling cost-competitive MBL production, this project overcomes key limitations of natural cofactor-dependent enzymes, advancing cell-free biocatalysis and its broader applications in synthetic pathways. The resulting platform will drive innovation across the bioeconomy, transforming industrial biotechnology through enhanced scalability and efficiency. 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.
NSF Program Director: Waleed Nasser
| Status | Active |
|---|---|
| Effective start/end date | 04/15/25 → 03/31/28 |
Lead and Sub-Awardee Organization(s)
Funding
- (USPRD) Use-Inspired Acceleration of Protein Design: $6,328,776.00
Active Fiscal Year
- FY2028
- FY2027
- FY2026
- FY2025
Start Fiscal Year
- FY2025
TIP Programs
- (USPRD) Use-Inspired Acceleration of Protein Design
Key Technology Areas
- Artificial Intelligence
- (confidence score: 94%)
- Biotechnology
- (confidence score: 100%)
Technology Foci
- Synthetic Biology
- (confidence score: 100%)
- Bio-manufacturing
- (confidence score: 99%)
- Biotechnology - Other than SynBio
- (confidence score: 100%)
- Genomics and bioinformatics
- (confidence score: 100%)
- Artificial Intelligence (Broad)
- (confidence score: 100%)
Congressional District at Award
- District n. 07 of Washington
Current Congressional District
- District n. 07 of Washington
United States
- Washington
Core Based Statistical Area (CBSA)
- Seattle-Tacoma-Bellevue, WA
County
- County: King, WA
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