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DESCRIPTION (provided by applicant): The long-term goal of this proposal is to develop effective integrated computational and experimental approaches to identify interactions between proteins and major metabolites on a proteomics scale. The proposed approach allows rapid identification of ligand binding proteins on a proteomics scale and constructing protein-metabolite interaction network, which will provide crucial information for understanding biological systems. As a model case, we will identify NAD+ and NADP+ binding proteins in the E. coli proteome. In particular, the following specific aims are proposed: (1) To develop a set of computational methods for predicting proteins that bind to metabolites. We employ both sequence- based and structure-based methods. The sequence-based methods we will develop and employ include our novel function prediction methods, PFP and its variant, which are shown to have higher sensitivity and higher function assignment coverage than conventional methods. Structure-based methods include fast local protein surface shape comparison method, which directly compare shape and physicochemical property of local surface regions. (2) To apply energetics-based target identification approach to efficiently screen proteins that bind to metabolites. Proteins stabilized upon binding to NAD+ and NADP+ will be identified in a E. coli lysate by combining a brief incubation with a protease and quantitative mass spectrometry. Proteins identified by either computational or experimental methods will be cross-validated by the complementary approaches. Successful completion of this project will establish methodology for systematic identification of proteins that bind to specific metabolites and thus will enable us to provide interaction network of proteins and metabolites in cells. The methodology to be developed and the resulting interaction network will assist in the early stages of drug discovery, and hence the proposed project could have significant therapeutic utility.
期刊论文(26)
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DOI: 10.1021/acs.jcim.6b00163
发表时间: 2016-09-26
期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
影响因子: 5.6
作者: [Shin, Woong-Hee, Christoffer, Charles W., Wang, Jibo, Kihara, Daisuke]
通讯作者: Kihara, Daisuke
DOI: 10.1093/bioinformatics/btx231
发表时间: 2017-07-15
期刊: Bioinformatics (Oxford, England)
影响因子: --
作者: [Khan IK, Bhuiyan M, Kihara D]
通讯作者: Kihara D
DOI: 10.3390/molecules200712841
发表时间: 2015-07-16
期刊: Molecules (Basel, Switzerland)
影响因子: --
作者: [Shin WH, Zhu X, Bures MG, Kihara D]
通讯作者: Kihara D
DOI: 10.1038/srep40629
发表时间: 2017-01-11
期刊: Scientific reports
影响因子: 4.6
作者: [Shin WH, Kang X, Zhang J, Kihara D]
通讯作者: Kihara D
13
    Building protein structure models for intermediate resolution cryo-electron microscopy maps
    • 批准号:
      10405197
    • 项目类别:
    • 资助金额:
      $23.25万
    • 财政年份:
      2020
    • 负责人:
      Daisuke Kihara
    • 依托单位:
    Building protein structure models for intermediate resolution cryo-electron microscopy maps
    • 批准号:
      10266083
    • 项目类别:
    • 资助金额:
      $30.55万
    • 财政年份:
      2020
    • 负责人:
      Daisuke Kihara
    • 依托单位:
    Building protein structure models for intermediate resolution cryo-electron microscopy maps
    • 批准号:
      10794660
    • 项目类别:
    • 资助金额:
      $16.87万
    • 财政年份:
      2020
    • 负责人:
      Daisuke Kihara
    • 依托单位:
    Building protein structure models for intermediate resolution cryo-electron microscopy maps
    • 批准号:
      10462711
    • 项目类别:
    • 资助金额:
      $30.55万
    • 财政年份:
      2020
    • 负责人:
      Daisuke Kihara
    • 依托单位:
    海外基金