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.

背景我开发了基础统计物理学，以了解生物如何解决其操作分子级的要求：在强大的机械力和化学力的驱动下，使用纳米级软物质物体快速执行生产功能（包括高效的能量转换）。我的长期目标是阐明分子能量转换机制在效率、精度、速度、稳健性和可调节性方面的物理极限（考虑到这些令人畏惧的生物物理挑战）；这些理想特性之间的权衡；达到这些极限的分子机器的设计；以及自然进化的系统在多大程度上符合这些预测。 短期目标 在接下来的五年中，我的研究将集中在三个目标上，两个目标是利用理论来理解生物分子机器的物理原理，第三个目标是将我们的理论框架应用于协作实验室实验：揭示化学机械传导的设计原理自由能源。我们将开发基本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，从而揭示蛋白质（驱动生物学的复杂机械物体）能量转换的设计原理。 开发异质多级能量转换的理论框架。我们将推导理论并以数值方式探索通过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在强烈波动和许多能量泄漏机会的情况下稳健运行。设计、分析和解释探索分子能量转导的实验。在合作实验中，我们将测试我们的新理论在模型生物物理系统和核心分子机器中的实用性，以了解如何通过“智能”操作来节省能源，以及如何调整自然进化的机器来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）发展了基础理论，并在分子机器实验中展示了其实用性，从而在这个新兴的跨学科领域取得了根本性和实际的突破。识别控制物理约束将有助于揭示生物学中功能驱动的能量转换的基本设计原理，进而阐明分子机器的（受限）进化优化，并提高我们对生命物理基础的理解。这些发现将为改进新型合成机器的功能提供定量指导，并具有促进加拿大工业的潜在技术应用，包括提高人工光合材料和计算机内存的效率。