Development and Application of Multi-scale Modeling to Biomolecular Association

生物分子关联多尺度建模的发展与应用

• 批准号: 1932984
• 负责人: Chia-en Chang
• 金额: $ 46.68万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932984&HistoricalAwards=false
• 关键词: Development; Application; Multi; scale; Modeling

Accurate and timely molecular recognition is fundamental to all biological processes. In metabolic assembly (or disassembly) lines, sequential participants are thought to co-localize to maximize the rate at which a final product can emerge from a multi-step pathway. The investigator proposes to computationally simulate biosynthetic pathway networks in an effort to understand how, despite the complexity of the cellular milieu, binding partners are able to find each other, and biosynthetic pathway flux can be maximized. Through iterations of experiments with collaborators and computation, the investigator will model and predict the effects of co-localization of enzyme activities (the participants) on pathway flux. The overarching goal of the work is to develop software that allows one to accurately simulate how molecular partners find each other in the complex environment of the cell. The work has application to biosensor development, as sensors must also be able to detect their specific targets in a congested and chaotic environment. The project will also contribute to the training of graduate and undergraduate students. A summer workshop will be organized to train students from local community colleges, universities and high schools. Molecular association, in which two or more molecules must find one another, is the first critical step for all biochemical catalysis and (bio)sensing techniques. The project combines multi-level simulations, theoretical work and experimental collaboration to investigate molecular recognition and kinetic enhancements in multi-enzyme systems. To accelerate the overall product synthesis rate, several factors may be considered in designing a multi-enzyme scaffold, including geometry, local charge distribution, and network motions. The design of biosensors is informed by the binding mechanisms of a target and probe. The project explores novel ideas and incorporates results from experiments, atomistic molecular dynamics (MD), and Brownian dynamics (BD) simulations to investigate multi-enzyme nanostructures and large-scale membrane bound biosensors, either in vitro or a cell-like environment. The results offer insight into multi-enzyme and biosensor systems that will inform engineering and biosensor design.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.

准确及时的分子识别是所有生物过程的基础。在代谢组装（或拆卸）生产线中，顺序参与者被认为是共同定位的，以最大限度地提高最终产品从多步骤路径中出现的速度。 研究人员建议通过计算模拟生物合成途径网络，以了解尽管细胞环境很复杂，但结合伙伴如何能够找到彼此，并使生物合成途径通量最大化。通过与合作者反复进行实验和计算，研究人员将建模并预测酶活性（参与者）的共定位对途径通量的影响。这项工作的总体目标是开发软件，使人们能够准确模拟分子伴侣如何在复杂的细胞环境中找到彼此。这项工作可应用于生物传感器的开发，因为传感器还必须能够在拥挤和混乱的环境中检测其特定目标。该项目还将有助于研究生和本科生的培训。将组织夏季讲习班，培训来自当地社区学院、大学和高中的学生。分子缔合，其中两个或多个分子必须找到彼此，是所有生化催化和（生物）传感技术的第一个关键步骤。该项目结合了多层次模拟、理论工作和实验合作，研究多酶系统中的分子识别和动力学增强。为了加快整体产品合成速率，在设计多酶支架时可以考虑几个因素，包括几何形状、局部电荷分布和网络运动。生物传感器的设计取决于靶标和探针的结合机制。该项目探索新颖的想法，并结合实验、原子分子动力学 (MD) 和布朗动力学 (BD) 模拟的结果，在体外或类细胞环境中研究多酶纳米结构和大规模膜结合生物传感器。研究结果提供了对多酶和生物传感器系统的深入了解，可为工程和生物传感器设计提供信息。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。