Collaborative Research: Mechanism and Target Recognition of Protein Arginine Methyltransferases (PRMTs)

有了这个奖项，化学部门的生命过程化学项目资助了犹他州立大学的Joan Hevel博士和迈阿密大学的Orlando Acevedo博士，以确定一类被称为蛋白质精氨酸甲基转移酶（PRMTs）的酶是如何选择性地识别它们的目标的，以及它们的活性是如何使用先进的计算和生物物理技术进行调节的。PRMTs几乎影响细胞生物学的每一个方面，包括细胞发育、修复和维持。这些酶的功能是在精确的时间将一个或多个特定的标记添加到选定的蛋白质上。令人惊讶的是，我们还不知道这些酶是如何从细胞中大量的其他蛋白质中挑选出特定的蛋白质来起作用的。本研究将结合计算机模拟和生化实验来确定PRMTs选择性结合的规则，并更好地了解PRMTs的活性是如何被调节的。这些研究的结果将为开发新的抑制剂提供必要的基础信息，并允许理解PRMTs在细胞如何相互沟通和响应不断变化的环境中所起的复杂作用。在社会层面上，学生将接受计算生物化学和湿生物化学实验技术的交叉训练，为他们在科学和技术劳动力的职业生涯做好准备。这个项目也被整合到一个外展计划中，向拉美裔美国高中生和美国原住民本科生介绍研究企业。蛋白精氨酸甲基转移酶（PRMTs）是哺乳动物细胞功能不可或缺的一部分，影响着细胞生物学的几乎每一个方面。令人惊讶的是，管理PRMT目标识别的规则已经回避了十多年，该领域才刚刚开始了解这种酶化学的复杂性。本应用程序的第一个目标是阐明寡聚化和变构如何在PRMT1甲基转移酶活性中发挥作用。测定PRMT1的生理浓度将用于校准生物物理研究。使用战略性设计的低聚状态标准PRMT1蛋白，位点定向突变体和PRMT1的工程伪二聚体将与天然PAGE（聚丙烯酰胺凝胶电泳），动力学和计算研究一起使用，以探索低聚，变构和催化之间的关系。该项目的第二个目标是通过计算和动力学表征底物构象和n端PRMT结构域对PRMT4底物选择的作用，建立一个理解PRMT目标识别的框架。这些研究将深入了解精氨酸甲基化是如何在细胞中受到调节的。一种机器学习方法的开发，能够比传统的计算方法更快地准确预测肽与蛋白质的结合，这将对科学模拟界产生更广泛的影响。本研究的更广泛影响包括对研究企业学生的培训，特别关注西班牙裔美国高中生和美国原住民本科生。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 this award, the Chemistry of Life Processes Program in the Chemistry Division is funding Dr. Joan Hevel at the Utah State University and Dr. Orlando Acevedo at the University of Miami to determine how a class of enzymes known as protein arginine methyl transferases or PRMTs selectively recognize their targets and how their activity is regulated using advanced computational and biophysical techniques. PRMTs impact nearly every aspect of cellular biology, including cell development, repair, and maintenance. These enzymes function by adding one or more specific markers onto select proteins at precise times. Surprisingly, it is not understood how these enzymes pick out the specific proteins to act on from a vast number of other proteins in the cell. This study will integrate both computer simulations and biochemical experiments to identify the rules that confer selective binding to PRMTs and to better understand how the activity of PRMTs is regulated. The results of these studies will provide foundational information that is required for novel inhibitor development, as well as allow for an understanding of the sophisticated role that PRMTs play in how cells communicate with each other and respond to an ever-changing environment. On a societal level, students will receive cross-training in both computational and wet biochemistry experimental techniques that will prepare them for careers in the science and technology workforce. This project also is integrated into an outreach program to introduce Hispanic-American high school students and Native American undergraduate students to the research enterprise.Protein arginine methyltransferases (PRMTs) are integral to mammalian cell function, impacting nearly every aspect of cellular biology. Surprisingly, the rules that govern PRMT target recognition have evaded the field for more than a decade and the field is only beginning to understand the complexities of this enzymatic chemistry. The first objective of this application is to clarify how oligomerization and allostery play a role in PRMT1 methyltransferase activity. Determination of physiological concentrations of PRMT1 will be used to calibrate biophysical studies. Use of strategically designed oligomeric state standard PRMT1 proteins, site-directed mutants, and an engineered pseudo-dimer of PRMT1 will be used with native PAGE (polyacrylamide gel electrophoresis), kinetics, and computational studies to explore the relationship between oligomerization, allostery, and catalysis. The second objective of this project is to establish a framework for understanding PRMT target recognition by computationally and kinetically characterizing the roles that substrate conformation and N-terminal PRMT domains have on PRMT4 substrate selection. The studies will provide insight into how arginine methylation is regulated in the cell. The development of a machine learning method that accurately predicts the binding of peptides to proteins orders of magnitude faster than traditional computational methods will have a large broader impact for the scientific simulation community. Broader impacts of the study include the training of students in the research enterprise, with special attention given to Hispanic-American high school students and native American undergraduates.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.