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Soft Matter Simulation and Theory of the Crystal Assembly of Globular and Membrane Proteins

球状和膜蛋白晶体组装的软物质模拟和理论

基本信息

批准号：
1749374
负责人：
Patrick Charbonneau
金额：
$ 27万
依托单位：
Duke University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-15 至 2022-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749374&HistoricalAwards=false
关键词：
Soft Matter Simulation Theory Crystal

项目摘要

NONTECHNICAL SUMMARYDivision of Materials Research and the Chemistry Division contribute funds to this award. This award supports theoretical and computational research and education into the crystallization of proteins, which are molecules that are essential to all life forms. Protein crystallization is a key step in determining their structure. Knowing the structure of a protein, in turn, is crucial for understanding its role in biology. Proteins indeed interact with each other and their environment through fine-tuned features. Limited knowledge of protein structures hinders the comprehension of biological molecules and the ability to discover new drugs that interacts with them. The research team will identify how weak yet directional interactions lead to the complex processes through which certain classes of proteins can form crystals. It will also study the materials properties of the ordered lipid phases used for crystallizing those proteins that reside within the membrane of cells.The award also supports the education of students at Duke University, and the PI's participation in various outreach activities within the state of North Carolina and nationally. Through close collaboration with experimental scientists, the findings of the research team will also be tested and used by the protein crystallization community. As a complement to this work, the PI will lead a joint industry-academia consortium aiming to automatize image analysis of protein crystallization experiments, which should dramatically accelerate the experimental process and data gathering. TECHNICAL SUMMARYDivision of Materials Research and the Chemistry Division contribute funds to this award. This award supports theoretical and computational research and education on the soft matter theory and computational modeling of the crystallization of globular proteins and of the lipidic assemblies central to in-meso crystallization of membrane proteins. Both processes are key for protein-structure determination through crystallographic techniques yet are challenging to control experimentally and thus could benefit from additional insight from physical models. More specifically, the research team will examine how weak, non-covalent yet directional interactions lead to the complex crystallization behavior of different classes of proteins. They will study the crystallization of globular proteins that: (i) dimerize, (ii) display an inverted solubility, and (iii) are composed of multiple subdomains. All three cases fall beyond the canonical description of protein crystal assembly. By developing theoretical and computational soft matter models that capture the essence of these richer types of protein crystal assembly, the research team will guide parallel experimental efforts by collaborating researchers.The research team will also examine the formation of lipidic mesophases, which are commonly used for the in-meso crystallization process of membrane proteins but whose microscopic role is incompletely understood. The project will use state-of-the-art numerical tools for determining the phase behavior of various lipid models and examine how membrane proteins embed and order within these ordered phases.The award also supports the education of students at Duke University, and the PI's participation in various outreach activities. Through close collaboration with experimental researchers in the field of protein crystallization, the findings of the research team will also be tested and broadly implemented. As a complement to this work, the PI will also lead a joint industry-academia consortium aiming to automatize image analysis of protein crystallization experiments, and thus remove the biggest bottleneck in the systematic analysis of these experiments.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.

非技术总结材料研究部和化学部为该奖项提供资金。该奖项支持蛋白质结晶的理论和计算研究和教育，蛋白质是所有生命形式所必需的分子。蛋白质结晶是确定其结构的关键步骤。反过来，了解蛋白质的结构对于理解其在生物学中的作用至关重要。蛋白质确实通过微调特征与彼此及其环境相互作用。对蛋白质结构的有限了解阻碍了对生物分子的理解以及发现与它们相互作用的新药的能力。研究小组将确定弱而定向的相互作用如何导致某些类别的蛋白质形成晶体的复杂过程。它还将研究用于结晶那些存在于细胞膜内的蛋白质的有序脂质相的材料特性。该奖项还支持杜克大学学生的教育，以及PI在北卡罗来纳州和全国范围内参与各种外展活动。通过与实验科学家的密切合作，研究团队的发现也将被蛋白质结晶社区测试和使用。作为这项工作的补充，PI将领导一个联合的工业-学术联盟，旨在自动化蛋白质结晶实验的图像分析，这将大大加快实验过程和数据收集。材料研究部和化学部为该奖项提供资金。该奖项支持关于软物质理论和球状蛋白质结晶的计算建模以及对膜蛋白质的介观结晶至关重要的非线性组装的理论和计算研究和教育。这两个过程都是通过晶体学技术确定蛋白质结构的关键，但在实验上控制起来具有挑战性，因此可以从物理模型的额外见解中受益。更具体地说，研究小组将研究弱的，非共价但定向的相互作用如何导致不同类别蛋白质的复杂结晶行为。他们将研究球状蛋白质的结晶：（i）二聚化，（ii）显示反向溶解度，（iii）由多个子域组成。所有这三种情况都超出了蛋白质晶体组装的规范描述。通过开发理论和计算软物质模型，捕捉这些更丰富类型的蛋白质晶体组装的本质，研究团队将指导合作研究人员的平行实验工作。研究团队还将研究非晶中间相的形成，这通常用于膜蛋白质的介观结晶过程，但其微观作用尚未完全了解。该项目将使用最先进的数值工具来确定各种脂质模型的相行为，并研究膜蛋白如何嵌入这些有序相中并进行排序。该奖项还支持杜克大学学生的教育，以及PI参与各种推广活动。通过与蛋白质结晶领域的实验研究人员密切合作，研究团队的发现也将得到测试和广泛实施。作为这项工作的补充，PI还将领导一个旨在实现蛋白质结晶实验图像分析自动化的产学联合体，从而消除这些实验系统分析的最大瓶颈。该奖项反映了NSF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。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Temperature-Dependent Interactions Explain Normal and Inverted Solubility in a γD-Crystallin Mutant

温度依赖性相互作用解释了γD-晶状体蛋白突变体的正常和反向溶解度

DOI：
10.1016/j.bpj.2019.07.019
发表时间：
2019
期刊：
Biophysical Journal
影响因子：
3.4
作者：
Khan, Amir R.;James, Susan;Quinn, Michelle K.;Altan, Irem;Charbonneau, Patrick;McManus, Jennifer J.
通讯作者：
McManus, Jennifer J.

Tissue self-organization underlies morphogenesis of the notochord

组织自组织是脊索形态发生的基础

DOI：
10.1098/rstb.2017.0320
发表时间：
2018
期刊：
Philosophical Transactions of the Royal Society B: Biological Sciences
影响因子：
0
作者：
Norman, James;Sorrell, Emma L.;Hu, Yi;Siripurapu, Vaishnavi;Garcia, Jamie;Bagwell, Jennifer;Charbonneau, Patrick;Lubkin, Sharon R.;Bagnat, Michel
通讯作者：
Bagnat, Michel

Resolving the two-dimensional axial next-nearest-neighbor Ising model using transfer matrices