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 DESCRIPTION (provided by applicant): Protein tyrosine kinases are dynamic molecular switches that toggle between a catalytically "on" and "off" state to turn on and off signals responsible for cell growth and survival. While the tyrosine kinase switch is tightly regulated by diverse array of structural mechanisms in normal states, in many disease states, the switch is permanently turned on or off due, in part, to mutations in the tyrosine kinase domain. Although genome sequencing studies have revealed the mutational patterns of tyrosine kinases from many different disease types, understanding the structural and functional impact of these mutations is a challenge because many recurrent mutations occur far from the active site (distal mutations) and the residue networks that contribute to the complex modes of tyrosine kinase allosteric regulation are not fully understood. Our long term goal is to understand the relationships connecting sequence, structure, function, regulation and disease in protein kinases using a combination of computational and experimental approaches. Our objective in this proposal, which is the next logical step toward attainment of our long-term goal, is to delineate the residue interaction networks that contribute to the unique modes of allosteric regulation in tyrosine kinases, and to develop a computational framework for predicting mutation impact using the evolutionary and allosteric properties encoded in three dimensional structures. The central hypothesis is that distal mutations alter evolutionarily conserved allosteric networks in tyrosine kinases, and delineating the allosteric networks unique to tyrosine kinases will provide context for predicting and testing disease mutation impact. The specific aims are: * To identify and characterize natural sequence and structural variants associated with tight allosteric control of tyrosine kinase activity * To develop a computational framework for predicting mutation impact on kinase activation and to experimentally validate computational predictions using selected receptor tyrosine kinases as model systems Successful completion of these aims is expected to reveal novel activating mutations in putative allosteric sites in the tyrosine kinase domain, and pinpoint key residues and interactions for functional studies. These outcomes, in turn, are expected to have major biomedical impact by accelerating the functional characterization of the mutated tyrosine kinome, which is emerging as a major target for personalized medicine. Finally, by providing detailed mechanistic annotation of mutations identified in genome sequencing studies, this proposal will address a fundamental NIH roadmap problem in translational medicine of converting genomic discoveries into therapeutic strategies.
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Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining
  • 批准号:
    10457684
  • 项目类别:
  • 资助金额:
    $47.79万
  • 财政年份:
    2022
  • 负责人:
    Natarajan Kannan
  • 依托单位:
Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining (Kennady Boyd)
  • 批准号:
    10809950
  • 项目类别:
  • 资助金额:
    $0.91万
  • 财政年份:
    2022
  • 负责人:
    Natarajan Kannan
  • 依托单位:
Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining
  • 批准号:
    10661550
  • 项目类别:
  • 资助金额:
    $47.7万
  • 财政年份:
    2022
  • 负责人:
    Natarajan Kannan
  • 依托单位:
Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining (Rayna Carter)
  • 批准号:
    10809931
  • 项目类别:
  • 资助金额:
    $0.91万
  • 财政年份:
    2022
  • 负责人:
    Natarajan Kannan
  • 依托单位:
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