Nonequilibrium free energy transduction in biomolecular machines

生物分子机器中的非平衡自由能量转导

• 批准号: RGPIN-2020-04950
• 负责人: Sivak, David
• 金额: $ 4.44万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740307
• 关键词: Nonequilibrium; free; energy; transduction; biomolecular

Background I develop fundamental statistical physics to understand how living things address their operational molecular-scale imperatives: performing productive functions (including efficient energy transduction) rapidly while driven by strong mechanical and chemical forces, using nanoscale soft-matter objects. My long-term goal is to elucidate the physical limits-given these daunting biophysical challenges-on the efficiency, precision, speed, robustness, and tunability of molecular energy-transduction machinery; the trade-offs among these desirable characteristics; the designs of molecular machines that achieve these limits; and the extent to which naturally evolved systems conform to these predictions. Short-Term Objectives In the next five years, my research will focus on three Aims, two that use theory to understand the physics of biomolecular machines and a third that applies our theoretical framework to collaborative laboratory experiments: Uncover design principles for mechanical transduction of chemical free energy. We will develop basic theoretical frameworks and perform numerical simulations of model machines to understand nonequilibrium energy flows in intricately coupled systems, thereby uncovering design principles for energy transduction by proteins, the complex mechanical objects that drive biology. Develop theoretical frameworks for heterogeneous multi-stage energy transduction. We will derive theory and numerically explore energy flows through complex machines with many components, to understand how they achieve directionality and ensure robust operation despite strong fluctuations and many opportunities for energy leakage. Design, analyze, and interpret experiments probing molecular energy transduction. In collaborative experiments, we will test our new theories' utility in model biophysical systems and core molecular machines, to understand how energy can be saved by `smart' operation, and how much naturally evolved machines are tuned to improve energetic efficiency and other functionality. Impact My research program (providing training for many young scientists and instilling valuable quantitative skills for the modern workforce) develops basic theory and demonstrates its utility in experiments on molecular machines, thereby producing fundamental and practical breakthroughs in this emerging interdisciplinary field. Identifying governing physical constraints will help uncover the basic design principles underlying functional driven energy transduction in biology, in turn illuminating the (constrained) evolutionary optimization of molecular machines and improving our understanding of the physical underpinnings of life. These findings will provide quantitative guidance for improving functionality in novel synthetic machines, with potential technological applications to bolster Canadian industry, including enhanced efficiency in artificial photosynthetic materials and computer memory.

背景I开发了基本的统计物理学，以了解生物如何解决其运营分子规模的命令：使用纳米级软件对象，在受强机械和化学力的驱动时，迅速执行生产函数（包括有效的能量转导）。我的长期目标是阐明这些艰巨的生物物理挑战的物理极限，这是在效率，精度，速度，稳健性和分子能量转移机械的可调性中赋予的；这些理想的特征之间的权衡；达到这些极限的分子机器的设计；以及自然发展的系统符合这些预测的程度。 短期目标在未来五年内，我的研究将重点放在三个目标上，两个目标使用理论来了解生物分子机器的物理学，第三个将我们的理论框架应用于协作实验室实验：揭示化学自由能的机械传输设计原理。我们将开发基本的理论框架并对模型机器进行数值模拟，以了解复杂耦合系统中的非平衡能量流，从而发现蛋白质能量转导的设计原理，蛋白质是驱动生物学的复杂机械对象。 开发用于异质多阶段能量转导的理论框架。我们将得出理论，并在数值上探索具有许多组件的复杂机器的能量流，以了解它们如何实现方向性并确保尽管有强大的波动和许多能量泄漏的机会，并确保了强大的操作。设计，分析和解释实验探测分子能转导的实验。在协作实验中，我们将测试我们在模型生物物理系统和核心分子机器中的新理论的实用性，以了解如何通过“智能”操作来节省能量，以及对多少自然进化的机器可以调节以提高能量效率和其他功能。 影响我的研究计划（为许多年轻科学家提供培训，并为现代劳动力灌输宝贵的定量技能）开发了基本理论，并证明了其在分子机器实验中的实用性，从而在这个新兴的跨学科领域中产生了基本和实践的突破。确定治理物理约束将有助于发现生物学功能驱动能量转导的基本设计原理，进而阐明分子机器的（约束）进化优化，并提高我们对生命物理基础的理解。这些发现将为改善新型合成机的功能提供定量指导，并在加拿大辅助工业中使用潜在的技术应用，包括提高人造光合材料和计算机记忆的效率。