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Abstract: Recombinant protein expression technology has revolutionized human health research, enabling scientists to generate large quantities of protein for small-molecule drug discovery campaigns, for the development of protein-based drugs, and for studying protein structure and function. Yet while researchers are adept at producing proteins with known genetic sequences, they possess entirely inadequate knowledge about the protein's post-translational modifications, both during recombinant expression and when the proteins are isolated from endogenous sources. Alterations in a protein's post-translational modifications can change the protein from an active enzyme to a dead one; they can change a protein-based drug from a life-saving molecule to one that is either ineffective or worse, dangerously immunogenic; these seemingly simple “add- ons” to proteins can be the defining features that determine whether the protein functions as designed or not. Characterizing proteins' PTMs is a necessary prerequisite for identifying disease states, modulating protein binding and function, and producing safe and effective protein-based drugs and vaccines. The long term goals of my research program are to expedite the analysis of post-translational modifications (PTMs) on proteins, to use these analytical tools to answer critical health-related challenges, and to widely distribute the technology. We are pursuing both near-term advancements, such as developing a robust solution to quantifying aberrant disulfide bonds in recombinant proteins, as well as long-range, revolutionary changes in the way proteins are analyzed, by laying the foundational infrastructure needed to bring artificial intelligence solutions to the field of PTM analysis. We will develop and launch these technologies in the context of meaningful biological problems where field advancements cannot be made without information about proteins' PTMs. Solutions in the fields of HIV vaccine design, cancer treatment, and antibody therapy will be targeted first.
期刊论文(10)
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会议论文
Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier.
使用更新的亚里士多德分类器,使用蛋白质组学数据改善了疾病状态的歧视。
DOI: 10.1021/acs.jproteome.1c00066
发表时间: 2021-05-07
期刊: Journal of proteome research
影响因子: 4.4
作者: [Hua D, Desaire H]
通讯作者: Desaire H
DOI: 10.1007/s00216-020-03117-2
发表时间: 2021-03
期刊: Analytical and bioanalytical chemistry
影响因子: 4.3
作者: [Desaire H, Patabandige MW, Hua D]
通讯作者: Hua D
DOI: 10.1021/jasms.0c00317
发表时间: 2021-02-03
期刊: Journal of the American Society for Mass Spectrometry
影响因子: 3.2
作者: [Patabandige MW, Go EP, Desaire H]
通讯作者: Desaire H
DOI: 10.1007/s00216-021-03621-z
发表时间: 2021-12
期刊: Analytical and bioanalytical chemistry
影响因子: 4.3
作者: [Go EP, Zhang S, Ding H, Kappes JC, Sodroski J, Desaire H]
通讯作者: Desaire H
Mass Spectrometry Measurements for Complex PTMs
  • 批准号:
    10331717
  • 项目类别:
  • 资助金额:
    $37.71万
  • 财政年份:
    2019
  • 负责人:
    HEATHER R DESAIRE
  • 依托单位:
Mass Spectrometry Measurements for Complex PTMs
  • 批准号:
    10083746
  • 项目类别:
  • 资助金额:
    $37.71万
  • 财政年份:
    2019
  • 负责人:
    HEATHER R DESAIRE
  • 依托单位:
Generating Native Env Glycosylation in HIV Vaccine Candidates
  • 批准号:
    9321275
  • 项目类别:
  • 资助金额:
    $67.18万
  • 财政年份:
    2016
  • 负责人:
    HEATHER R DESAIRE
  • 依托单位:
Characterization of PTMs on Env to support HIV vaccine development
  • 批准号:
    8890080
  • 项目类别:
  • 资助金额:
    $36.46万
  • 财政年份:
    2012
  • 负责人:
    HEATHER R DESAIRE
  • 依托单位:
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