ABSTRACT

Biomanufacturing, the biosynthesis of commodity chemicals, fuels, and medicines, represents a fast-growing industry with over $150 billion in revenue in the US. To continue to grow in scale and economic viability, biomanufacturing must increase its carbon and energy efficiency. However, biosynthetic logics that exist in Nature often do not operate at maximal carbon or energy efficiency. This is the case because release of carbon is required as carbon dioxide and energy has to be wasted as heat to afford a robust thermodynamic driving force. One way to overcome this challenge is to introduce unnatural thermodynamic driving forces. This project contributes a suite of unnatural, chemical tools to deploy stronger-than-Nature thermodynamic driving forces in the form of low reduction-potential reducing equivalents. These tools augment the natural capability of biological systems and lead to the conversion of renewable resources into valuable products. Through the integrated research and outreach activities, the project improves biomanufacturing to better meet the Nation's needs for energy, food, commodities, and medicine and concomitantly contributes to undergraduate and graduate education in STEM. The project plans activities to motivate K-12 students to pursue a career in STEM by participating in hands-on experiences in practical science.

Current biomanufacturing processes face a fundamental challenge: biosynthetic logics that exist in Nature often do not operate at maximal carbon or energy efficiency, because carbon needs to be released as carbon dioxide and energy needs to be wasted as heat to afford a robust thermodynamic driving force. To overcome this challenge, unnatural thermodynamic driving forces are introduced. This proposal develops unnatural cofactors to deploy stronger-than-Nature thermodynamic driving forces. The overall objectives are to introduce unnatural redox cofactors that are more potent reducing reagents than NAD(P) into Escherichia coli metabolism and use them to power carbon-efficient biomanufacturing of commodity chemicals. This is achieved by engineering key enzymes to utilize these unnatural cofactors through an integrated Design-Build-Test-Learn workflow spanning genome mining, high-throughput enzyme discovery with directed evolution, structural and biophysical study of the engineered enzymes, as well as machine learning-based data interpretation to distill general design principles that govern protein-cofactor interactions. A better overall understanding of how structural plasticity of the cofactors is tolerated by enzymes, advances capability beyond what Nature selected for during evolution and opens new design space for proteins.

This award is co-funded by the Systems and Synthetic Biology program in the Division of Molecular and Cellular Biosciences and the Cellular and Biochemical Engineering program in the Division of Chemical, Bioengineering, Environmental and Transport Systems

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.

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