Controlling Protein Post-translational Modification by Separating Affinity and Catalysis in Designer Enzymes

在化学学部生命过程化学（CLP）项目的支持下，北卡罗来纳大学教堂山分校的Albert A. Bowers博士将研究制造人工酶的方法。酶是一种蛋白质，是所有细胞生命中代谢转化和过程的关键驱动因素。酶也日益成为各种工业过程中重要的“绿色”工具，包括药品、材料、燃料和食品的制造和制造。尽管在计算蛋白质设计方面取得了重大进展，但创造有用、高效的人工酶仍然是一个重大挑战。这是由于需要设计能够同时1)保持底物（或结合）和2)修饰底物（或催化）的蛋白质。提出的实验将测试一种潜在的可推广的酶设计策略，分离底物结合和催化肽基底物。这项工作采用了尖端技术，将为研究生在高通量实验、dna编码技术、计算蛋白质设计、机器学习和人工智能方面提供丰富的培训基础。这些方法还将构成一项重点推广计划的基础，将生物技术的新兴概念介绍给更广泛的公众。最终，如果成功，这项研究有可能为酶功能的设计开辟新的途径，并使生物催化合成具有新特性的肽和生物材料的应用成为可能。本研究项目旨在定量表征由分离的底物招募和修饰域组成的设计肽翻译后修饰（PTM）酶的效率增益。这构成了一种潜在的可推广的方法来创造人工PTM酶。Rosetta设计套件将用于设计fyn-SH3底物招募结构域和两种不同催化结构域之间的界面，以便在动力学分析中与原始（或未设计的）融合物以及母体，野生型催化亚基进行比较。该策略的通用性和灵活性将通过一种创新的、基于mRNA显示的底物显示分析方法来测量，并将结果与先进的机器学习（ML）方法进行分析和集成。这些模型酶将进一步用于评估设计策略对小的、设计的生物合成途径的加性效应。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。

With the support of the Chemistry of Life Processes (CLP) program in the Chemistry Division, Dr. Albert A. Bowers from the University of North Carolina at Chapel Hill will investigate methods for making artificial enzymes. Enzymes are proteins that act as the key drivers of metabolic transformations and processes in all cell-based life. Enzymes are also increasingly important ‘green’ tools for a variety of industrial processes, including the make and manufacture of pharmaceuticals, materials, fuels, and foods. Despite significant advances in computational protein design, the creation of useful, efficient, artificial enzymes remains a major challenge. This is due to the need to fashion proteins that are capable of simultaneously 1) holding a substrate (or binding) and 2) modifying the substrate (or catalysis). The proposed experiments will test a potentially generalizable strategy for enzyme design that separates substrate binding and catalysis for peptide-based substrates. This work employs cutting edge technologies and will provide rich training grounds for graduate students in high-throughput experimentation, DNA-encoded technology, computational protein design, and machine learning and artificial intelligence. These methods will also form the foundations of a focused outreach program to bring emerging concepts in biotechnology to the broader public. Ultimately, if successful, this research has the potential to open up new avenues for the design of enzyme function and enable the biocatalytic synthesis of peptides with new properties and biomaterials applications.This research project seeks to quantitatively characterize efficiency gains in designer peptide post-translational modification (PTM) enzymes comprised of separate substrate recruitment and modification domains. This constitutes a potentially generalizable approach to creating artificial PTM enzymes. Rosetta design suite will be used to fashion designer interfaces between the fyn-SH3 substrate recruitment domain and two different catalytic domains for comparison against naive (or undesigned) fusions as well as the parent, wild-type catalytic subunit in kinetic assays. Generalizability and flexibility of the strategy will be measured by an innovative, mRNA display-based substrate display assay approach and results analyzed and integrated with advanced machine learning (ML) methods. These model enzymes will further be employed to assess the additive effect of the design strategy on small, designer biosynthetic pathways.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.