High-Throughput Analysis and Evolution of Stereoselective Enzymes using Flow Cytometry

酶是已知的最有效的催化剂之一，它们负责几乎所有使生命成为可能的反应。酶催化化学转化的能力也可以用来为药物和其他高价值化学品的合成提供对环境无害的途径。然而，自然产生的酶必须经过磨练才能在这些非自然反应中发挥作用。增强酶功能的关键是开发快速，高保真的传感工具，以测量大量酶的功能。在美国国家科学基金会化学、生物工程、环境和运输系统部门的生物传感项目和化学测量与成像项目的支持下，埃默里大学的Jennifer Heemstra博士使用一个高速序列平台来测量数百万种不同的天然酶的功能，这些酶是由她的团队生成的。从而可以识别出最适合重要非自然反应的酶。该项目正在开发一个用户友好的平台，可以广泛应用于发现用于构建这些高价值化学品的新酶。结合这项研究，Heemstra博士开发并广泛传播多媒体资源，帮助为未来的科学、技术、工程和数学劳动力做好准备。具体来说，这些资源提供有关专业技能的信息和建议，包括口头和书面沟通，开拓新的研究思路，促进多样性和包容性，以及专业网络。这些资源对公众免费提供，并可供全国的学生和研究人员使用。埃默里大学的Jennifer Heemstra正在使用对映体DNA生物传感器来实现高通量对映不纯度测量，这是一种广泛适用于生物催化剂发现的方法。DNA生物传感器具有强大的能力，可以将特定小分子靶标的存在转化为剂量依赖的荧光输出。Heemstra博士利用由DNA的两种对映体组成的传感器来量化酶促反应产生的目标小分子的两种对映体的浓度。该分析与微流控技术相结合，通过流式细胞术进行筛选和分选。因此，可以快速筛选大量的酶变体库，并分离出具有所需立体选择性水平的酶变体库，从而加速发现用于合成药物和其他高价值化学品的新生物催化剂。本研究与本科课程相结合，学生将学习如何阅读和分析主要文献，制作原创研究计划，以及公共资源，以促进早期职业研究人员的专业发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Enzymes are among the most efficient catalysts known, and they are responsible for nearly all the reactions that make life possible. The capability of enzymes to catalyze chemical transformations can also be harnessed to provide environmentally benign routes for the synthesis of pharmaceuticals and other high-value chemicals. However, naturally occurring enzymes must be honed to function in these non-natural reactions. Key to augmenting enzyme capabilities is the development of fast, high-fidelity sensing tools that measure the function of enzymes in large numbers. With support from the Chemical Measurement and Imaging Program in the Division of Chemistry and partial co-funding from the Biosensing Program in the Division of Chemical, Bioengineering, Environmental and Transport Systems at NSF, Dr. Jennifer Heemstra at Emory University uses a high-speed sequential platform to measure the function of millions of different modified versions of naturally occurring enzymes generated by her team, thereby allowing for identification of those enzymes best suited to important non-natural reactions. This project is developing a user-friendly platform that can be widely applied to discover new enzymes for constructing these high-value chemicals. Integrated with this research, Dr. Heemstra develops and broadly disseminates multi-media resources that help to prepare the future workforce in science, technology, engineering, and mathematics. Specifically, these resources provide information and advice on professional skills, including oral and written communication, pioneering new research ideas, promoting diversity and inclusion, and professional networking. These resources are freely available to the general public and utilized by students and researchers nationwide.Dr. Jennifer Heemstra at Emory University is using enantiomeric DNA biosensors to enable high-throughput enantiopurity measurement as a broadly applicable approach to biocatalyst discovery. DNA biosensors are powerful in their ability to transduce the presence of a specific small-molecule target into a dose-dependent fluorescence output. Dr. Heemstra utilizes sensors comprised of each of the two enantiomers of DNA to quantify the concentrations of the two enantiomers of a target small molecule that are produced as a result of an enzymatic reaction. This analysis is integrated with droplet microfluidic technology to enable screening and sorting by flow cytometry. As a result, large libraries of enzyme variants can be rapidly screened, and those having the desired levels of stereoselectivity isolated, thereby accelerating the discovery of new biocatalysts for the synthesis of pharmaceuticals and other high-value chemicals. This research is integrated with an undergraduate curriculum in which students learn how to read and analyze the primary literature and craft original research proposals, as well as publicly available resources to promote the professional development of early-career researchers.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.