CAREER: Computational Modeling of Biological Nanomachines - Protein Unfolding and Translocation by Clp ATPases

蛋白质降解和聚集体的分解是维持重要细胞功能的关键质量控制机制。Clp大分子机器的强大环状组件通过使用蛋白质穿线机制介导这些作用，该机制包括通过中央通道对底物的识别、展开和易位。了解这些执行关键细胞功能的生物纳米机器的机制，是现代生物学研究的一个重要挑战。本CAREER项目的目标是通过计算机模拟，阐明蛋白质展开的动力学和热力学要求以及Clp纳米机器的底物转运的操作原理。这些研究将查明周期性力对底物蛋白展开和易位的影响，以及底物与负责这些作用的Clp之间相互作用的细节。该项目将促进对其他蛋白质降解机器和控制蛋白质展开和易位的基本原理的理解。这个CAREER项目将计算生物物理化学的跨学科研究与该领域本科生和研究生的教育和培训相结合。辛辛那提大学的教育基础设施将通过建立一门采用基于问题的学习和学生积极参与的方法教授的计算生物物理化学课程得到加强。PI将与中央州立大学和菲斯克大学合作，增加未被充分代表的少数民族在计算科学领域的参与。这些合作的范围是加强这些大学的物理化学课程，并引入分子建模课程。PI将为这些大学的学生提供研究经验的机会。

Protein degradation and disassembly of aggregates are critical quality control mechanisms for the maintenance of vital cellular functions. Powerful ring-shaped components of Clp macromolecular machines mediate these actions by using a protein threading mechanism, which consists of recognition, unfolding, and translocation of substrates through a central channel. Understanding the mechanism of these biological nanomachines, that carry out crucial cellular functions, represents an important challenge for modern biological studies. The objective of this CAREER project is to elucidate, using computer simulations, kinetic and thermodynamic requirements for protein unfolding and the operational principles of substrate translocation by the Clp nanomachines. These studies will pinpoint the effect of periodic forces on substrate protein unfolding and translocation and the details of interactions between substrates and Clp responsible for these actions. This project will advance understanding of other protein degradation machines and fundamental principles that govern protein unfolding and translocation.This CAREER project combines interdisciplinary research in computational biophysical chemistry with education and training of undergraduate and graduate students in this field. The educational infrastructure at the University of Cincinnati will be enhanced by establishing a computational biophysical chemistry course taught using problem-based learning and active student participation approaches. The PI will collaborate with Central State University and Fisk University to increase the participation of underrepresented minorities in computational sciences. The scope of these collaborations is to enhance the physical chemistry curriculum and to introduce molecular modeling courses at these universities. The PI will provide research experience opportunities for students at these universities.