Collaborative Research: Enabling Scalable Redox Reactions in Biomanufacturing

合作研究：在生物制造中实现可扩展的氧化还原反应

• 批准号: 2328146
• 负责人: Justin Siegel
• 金额: $ 36.09万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328146&HistoricalAwards=false
• 关键词: Collaborative; Research; Enabling; Scalable; Redox

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 SystemsThis 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.

生物制造，商品化学品，燃料和药品的生物合成代表了一个快速增长的行业，在美国的收入超过1500亿美元。为了继续扩大规模和经济生存能力，生物制造必须提高其碳和能源效率。但是，自然界中存在的生物合成逻辑通常不会以最大碳或能量效率运行。就是这样，因为需要释放碳，因为二氧化碳和能量必须浪费为热量，以提供强大的热力学驱动力。克服这一挑战的一种方法是引入不自然的热力学驱动力。该项目贡献了一套不自然的化学工具，以低降低势力降低等效物的形式部署强大的热力学驱动力。这些工具增强了生物系统的自然能力，并导致可再生资源转化为有价值的产品。通过综合研究和外展活动，该项目改善了生物制造，以更好地满足国家对能源，食品，商品和医学的需求，并同时为STEM的本科和研究生教育做出了贡献。该项目计划通过参加实践科学的动手经验来激励K-12学生从事STEM职业。当前的生物制造过程面临着一个基本挑战：自然界中存在的生物合成逻辑通常不会以最大碳或能源效率运行，因为需要释放碳，因为碳二氧化碳和能量需要浪费为热量以提供强大的热力学驱动力。为了克服这一挑战，引入了不自然的热力学驱动力。该提案开发了不自然的辅助因子，以部署强大的热力学驱动力。总体目的是将比NAD（P）更有效减少试剂引入不自然的氧化还原辅助因子中，并将其用于大肠杆菌代谢，并用它们为商品化学品的碳效率增强生物制造。这是通过工程关键酶通过跨越基因组挖掘，高通量酶发现的集成设计测试酶的工作流程来利用这些不自然的辅因子来实现的，该研究涵盖了基于机器学习酶的工程学解释，并统治了基于机器学习的蛋白质，并针对机器学习酶进行了针对机器学习的酶的指导，结构性和生物物理研究。更好地理解酶的结构可塑性如何由酶耐受性，超越了在进化过程中选择的能力，并为蛋白质开辟了新的设计空间。该奖项由分子和细胞生物科学和蜂窝和生物化学奖的系统和合成生物学计划共同资助。 NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。