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

有了这个奖项，化学部的生命过程化学计划正在资助犹他州州立大学的Joan Hevel博士和迈阿密大学的Orlando阿切韦多博士，以确定一类被称为蛋白质精氨酸甲基转移酶或PRMT的酶如何选择性地识别它们的靶点，以及它们的活性如何使用先进的计算和生物物理技术进行调节。 PRMT几乎影响细胞生物学的各个方面，包括细胞发育、修复和维护。这些酶通过在精确的时间将一个或多个特异性标记物添加到选择的蛋白质上来发挥作用。 令人惊讶的是，目前还不清楚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进行作用的。 这项研究将整合计算机模拟和生化实验，以确定赋予PRMT选择性结合的规则，并更好地了解PRMT的活性是如何调节的。这些研究的结果将提供新型抑制剂开发所需的基础信息，并使人们能够了解PRMT在细胞如何相互交流和应对不断变化的环境方面所发挥的复杂作用。在社会层面上，学生将接受计算和湿生物化学实验技术的交叉培训，这将为他们在科学和技术劳动力中的职业生涯做好准备。 该项目也被整合到一个推广计划，介绍西班牙裔美国高中生和美国原住民本科生的研究企业。蛋白质精氨酸甲基转移酶（PRMTs）是不可或缺的哺乳动物细胞功能，影响细胞生物学的几乎每一个方面。 令人惊讶的是，控制PRMT靶点识别的规则已经回避了该领域十多年，该领域才刚刚开始了解这种酶化学的复杂性。 本申请的第一个目的是阐明寡聚化和变构如何在PRMT 1甲基转移酶活性中发挥作用。 PRMT 1生理浓度的测定将用于校准生物物理学研究。 使用策略性设计的寡聚状态标准PRMT 1蛋白、定点突变体和PRMT 1的工程化假二聚体将与非变性PAGE（聚丙烯酰胺凝胶电泳）、动力学和计算研究一起使用，以探索寡聚化、变构和催化之间的关系。 该项目的第二个目标是建立一个框架，通过计算和动力学表征底物构象和N-末端PRMT结构域对PRMT 4底物选择的作用来理解PRMT靶点识别。 这些研究将深入了解精氨酸甲基化在细胞中是如何调节的。 开发一种机器学习方法，准确预测肽与蛋白质的结合速度比传统计算方法快几个数量级，将对科学模拟界产生更广泛的影响。这项研究的更广泛的影响包括在研究企业的学生的培训，特别关注西班牙裔美国高中生和美国本土大学生。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.