Illuminating Function of the Understudied Druggable Kinome

正在研究的可药物激酶组的照明功能

• 批准号: 10251291
• 负责人: GARY L. JOHNSON
• 金额: $ 225.18万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10251291
• 关键词: Algorithms; Anabolism; Arthritis; Attention; Biochemical; Biological; Biological Assay; Biological Process; Cell Line; Cell physiology; Cells; Chemicals; Chronic Obstructive Airway Disease; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Collection; Complement; Computational algorithm; Cystic Fibrosis; Data; Data Analyses; Development; Diabetes Mellitus; Disease; Down-Regulation; Drug Targeting; Engineering; Ensure; FAIR principles; Foundations; Funding; Gene Expression; Generations; Genes; Genetic; Goals; Health; Homeostasis; Human; Image; Individual; Information Resources Management; Knowledge; Libraries; Ligands; Light; Link; Logic; Machine Learning; Mass Spectrum Analysis; Measurement; Measures; Mediating; Metabolic; Methods; Modeling; Molecular; Monitor; Morphology; Mutate; Pharmaceutical Preparations; Phenotype; Phosphotransferases; Play; Precision Medicine Initiative; Production; Proteins; Proteomics; Reaction; Reagent; Reporter; Reporter Genes; Research; Research Personnel; Resolution; Resources; Role; Signal Transduction; Signal Transduction Pathway; Site; Supervision; Technology; Testing; Therapeutic; Tissues; Tumor Tissue; United States National Institutes of Health; Up-Regulation; Validation; base; cell type; cellular imaging; clinically significant; computerized tools; data interoperability; data resource; data reuse; data tools; design; human disease; informatics tool; innovation; insight; kinase inhibitor; knockout gene; knowledge base; metabolomics; metadata standards; new therapeutic target; novel; novel therapeutics; phosphoproteomics; programs; protein protein interaction; screening; small molecule; small molecule inhibitor; stable isotope; structural genomics; therapeutic development; therapeutic target; tool; transcription factor; transcriptome sequencing; unsupervised learning

Project Summary/Abstract Kinases are among the most important drug targets and clinically significant kinase inhibitors have been developed for multiple diseases. A subset of kinases, the understudied dark kinases (DKs), have received little or no attention because foundational data on their biochemical and biological functions is not available. This proposal will collect such data by perturbing DKs genetically and with small molecules and then measuring the cellular consequences using multiplex proteomic, gene expression, metabolomic and imaging assays. A subset of DKs with potential links to human disease will be intensively studied as a means to qualify new therapeutic drug targets. Data collected in this project will be aggregated with existing information from previous NIH-funded large-scale structural and genomic projects to create a Dark Kinase Knowledgebase (DKK) that provides gene-by-gene and network-level information on the dark kinome and its interaction with other signal transduction and regulatory networks. Close coordination with the NIH LINCS project will ensure data interoperability and make efficient use of informatics tools. The DKK will be developed in collaboration with the IDG Knowledge Management Center (KMC), adhere to standards for Findable, Accessible, Interoperable and Reusable (FAIR) data, and be accessible to human users and machines (via an API). Commercially available DK reagents be validated and extended with new genetic and chemical tools provided to the Resource Dissemination Center (RDOC). The overall approach will be iterative, with simpler methods applied first (e.g. simple gene knockout) and more sophisticated methods subsequently (e.g. stable CRIPSRa/i) pursued by an interdisciplinary team of chemists, computational biologists, mass spectroscopists and pharmacologists working on five linked aims. Aim 1 will develop a computational algorithm for prioritizing DKs, develop and maintain the DKK, and perform network-level analysis on the kinome using supervised and unsupervised machine learning. Aim 2 will measure kinase abundance in normal and perturbed cells using parallel reaction monitoring with stable isotope dilution (PRM-SID) and RNASeq and data analyzed using network inference tools to provide insight into dark and light kinome in diverse cell types. Aim 3 will perturb DKs with genetic tools such as CRIPSR/Cas9-mediated gene knockout, CRIPSRa/i to induce more subtle-up and down regulation and inducible gene inaction. The impact on cell fate, morphology and signal transduction will then be determined using PRM-SID, phosphoproteomics, RNASeq, gene reporter assays, metabolomics profiling and highly multiplex single-cell imaging. Aim 4 will extend DK analysis to small molecule inhibitors by carefully profiling existing drugs against DKs and by designing and synthesizing new chemical ligands. Aim 5 will involve collaboration with other investigators to assay the expression and function of DKs in primary human cells and tissues relevant to the NIH Precision Medicine Initiative. All aims will be pursued in parallel for a progressively expanding resource of data and tools for continued study of DKs.

项目总结/文摘