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Proteomic Analysis Using FTICR/MS

Proteomic Analysis Using FTICR/MS
使用 FTICR/MS 进行蛋白质组分析
批准号:
6936468
负责人:
I JONATHAN AMSTER
金额:
$25.76万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-09-22 至 2007-07-31

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中文摘要
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英文摘要
DESCRIPTION (provided by applicant): The proposed research is directed toward the development of new methods for quantitatively analyzing changes in protein expression in biological systems. The proposed developments will provide the means to analyze complex mixtures of proteins much more rapidly than is currently possible. Present day methodologies rely on the separation of protein mixtures into their individual components, followed by analysis for the identification of the separated proteins. The proposed developments will allow all proteins in a mixture to be identified simultaneously, thus providing a substantial reduction in analysis time and effort. Mixtures of proteins will be enzymatically digested, and the resulting mixture of proteolytic peptides will be separated by liquid chromatography and analyzed by high-resolution mass spectrometry. Proteins in the original mixture will be identified from their proteolytic fragments by using the accurate mass data that is produced. Presently, only a small proportion of peptides can be assigned to proteins by using accurate mass measurement. Most of the proposed effort will be directed into developing methods to increase the proportion of peptides that can be assigned to their parent proteins. A method called "mass defect labeling" is proposed as a way to increase the specificity of the assignment. Several novel reagents are proposed for mass defect labeling, and will be synthesized as part of the project. These reagents are not only useful for aiding protein identification, but also can be used to perform quantitative proteomics. Additional experiments will explore the use of endogenous labeling with a stable isotope in concert with the use of mass defect labels to achieve high specificity in protein identification. Using these methods together, calculations show that for the analysis of a prokaryotic proteome, up to 95% of the peptides that are measured can be assigned to the protein from which they derive. The success of the proposed developments will have great impact in biological research, drug discovery, and medicine. The proposed efforts will be carried out by both graduate and undergraduate students, and will be beneficial for their scientific development. The students involved in this research will be exposed to state-of-the-art, high-resolution mass spectrometry. This will provide society with well-trained scientists in this key technological area.
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T32 Predoctoral training grant in Glycosciences
  • 批准号:
    10410757
  • 项目类别:
  • 资助金额:
    $20.81万
  • 财政年份:
    2022
  • 负责人:
    I JONATHAN AMSTER
  • 依托单位:
T32 Predoctoral training grant in Glycosciences
  • 批准号:
    10650310
  • 项目类别:
  • 资助金额:
    $21.22万
  • 财政年份:
    2022
  • 负责人:
    I JONATHAN AMSTER
  • 依托单位:
An Automated Platform for the CE-MS Analysis of Glycosaminoglycans
  • 批准号:
    9753175
  • 项目类别:
  • 资助金额:
    $29.47万
  • 财政年份:
    2018
  • 负责人:
    I JONATHAN AMSTER
  • 依托单位:
An Automated Platform for the CE-MS Analysis of Glycosaminoglycans
  • 批准号:
    10005264
  • 项目类别:
  • 资助金额:
    $29.47万
  • 财政年份:
    2018
  • 负责人:
    I JONATHAN AMSTER
  • 依托单位:
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