| 项目编号: | 1605225
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| 项目名称: | Development of A Novel Class of Protein Conformation Selective Molecular Sensors |
| 作者: | Eva Chi
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| 承担单位: | University of New Mexico
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| 批准年: | 2016
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| 开始日期: | 2016-07-01
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| 结束日期: | 2019-06-30
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| 资助金额: | 326847
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | protein aggregate
; ope
; development
; sensor
; biosensor design challenge modules
; novel class
; pre-fibrillar aggregate conformation
; protein misfolding
; associated protein
; molecular sensor
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| 英文摘要: | PI: Chi, Eva
Proposal No: 1605225
This project will develop a novel class of sensors for the early detection and tracking of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. In particular, the sensors will detect the protein aggregates that form in the brain as early as two decades before the onset of symptoms. The ability to detect and track these protein aggregates will not only help in understanding the diseases better, but will also lead to early diagnosis and contribute towards the development of therapies to prevent and treat these devastating diseases.
Misfolding and aggregation of proteins is a central pathogenic event in neurodegenerative disorders such as Alzheimer's and Parkinson's diseases, particularly the pre-fibrillar aggregate conformations that are the most neurotoxic, which is potentially due to a lack of molecular probes that could selectively and differentially target different protein aggregate conformations. To meet this critical need, development of a novel oligo(p-phenylene ethynylene) electrolytes (OPEs) for the direct sensing of the wide set of amyloid aggregates based on multiple "fluorescence turn-on" mechanisms is proposed. Compared to existing probes that are of limited clinical use, e.g., Thioflavin-T based probes, OPEs offer many distinct advantages, including versatile and highly tailorable structural and chemical properties, and most notably the multiple modes by which OPEs respond to interactions with ligands. Specifically, the researchers will synthesize and test OPEs for the in vitro detection of protein aggregates prepared from Alzheimer's and Parkinson's associated proteins (Objective 1). Experimental findings will also be synergistically combined with closely related computational modeling to gain a fundamental understanding of OPE-protein aggregate interactions as well as OPE's sensing mechanism. Such insights will be used to guide the rational design and synthesis of OPEs Ex vivo detection of amyloid aggregates in brain tissue sections (Objective 2) and in vivo detection in live Alzheimer's animal model brains (Objective 3). Molecular sensors developed in this project will give researchers the tool to simultaneously and dynamically track the protein misfolding and aggregation process for both in vitro and in vivo systems, facilitating research into the cause, diagnosis, and treatment of major neurodegenerative disorders. Additionally, the proposed multidisciplinary research will obligate the PIs to train graduate and undergraduates students in modern methodologies required to address important problems at the interface between chemistry, biology, engineering, and medicine. The acquired interdisciplinary skills will prepare students for careers in academe, national laboratories, and industry. The PIs also propose an ambitious plan to design and integrate "Biosensor Design Challenge Modules" into the core chemical engineering undergraduate curriculum at the University of New Mexico at all levels. The goal of this educational effort is two fold: 1. Improving student retention, particularly underrepresented minorities, and 2. Enhancing student success by providing opportunities for solving open-ended design problems throughout undergraduate education. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/91911
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Eva Chi. Development of A Novel Class of Protein Conformation Selective Molecular Sensors. 2016-01-01.
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