Cooperativity Driven Communication through Noncovalent Networks in Biomimetic Systems

在化学系大分子、超分子和纳米化学项目的支持下，北卡罗来纳大学教堂山分校的Marcey Waters教授将开发仿生模型系统，模拟调节蛋白质功能的复杂行为。许多蛋白质就像分子机器一样，通过分子开关对刺激作出反应。这项研究旨在揭示创造新的刺激反应分子所必需的分子特征，这些分子模仿了蛋白质中的行为。这项工作的发现有可能为生物医学相关蛋白质行为提供更好的见解，但也为如何创造新的仿生“智能”材料提供指导。这项工作的更广泛影响包括为参与该项目的学生提供广泛的跨学科培训，以及开发新项目，将从事独立研究的化学专业学生与年轻学生联系起来，为如何从事研究提供同伴榜样和指导，目的是增加来自代表性不足群体的学生从事化学和化学生物学研究的人数。蛋白质是复杂的分子，除了结合和催化外，还具有广泛的行为，包括在变构、构象信号和信号转导中涉及长距离结构重排的刺激反应。这些行为是通过非共价网络的通信完成的，但这种通信的分子机制尚不清楚。本研究旨在开发具有响应性非共价网络的预测模型系统，以建立通过非共价网络实现通信的分子要求，包括正合作和负合作的作用。这些模型系统有可能弥合极简模型和复杂蛋白质之间的差距，以获得对这些响应系统的分子水平的了解。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 Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Marcey Waters of the University of North Carolina at Chapel Hill will develop biomimetic model systems that mimic the complex behavior that regulates protein function. Many proteins function as molecular machines that respond to stimuli with molecular on-off switches. This research seeks to uncover the molecular features that are necessary to create new stimulus-responsive molecules that mimic the behavior seen in proteins. The findings from this work have the potential to provide better insight into biomedically relevant protein behavior, but to also provide guidelines for how to create new biomimetic "smart" materials. The broader impacts of this work include broad interdisciplinary training for the students working on the project, as well as the development of new program that connects chemistry majors doing independent research with younger students to provide peer role models and guidance on how to pursue research, with the goal of increasing the number of students from underrepresented groups that engage in research in the chemistry and chemical biology.Proteins are complex molecules that display a wide range of behaviors beyond binding and catalysis, including stimulus response involving long-distance structural rearrangement as in allostery, conformational signaling, and signal transduction. These behaviors are accomplished via communication through a noncovalent network, but the molecular mechanisms of such communication are poorly understood. This research aims to develop predictive model systems with a responsive noncovalent network to establish the molecular requirements for achieving communication through a noncovalent network, including the role of both positive and negative cooperativity. These model systems have the potential to bridge the gap between minimalist models and complex proteins to gain molecular level insight into these responsive systems.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.