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: 9762097
• 负责人: GARY L. JOHNSON
• 金额: $ 226.6万
• 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/9762097
• 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; Metadata; 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 resource; design; human disease; human model; informatics tool; innovation; insight; interoperability; kinase inhibitor; knockout gene; knowledge base; metabolic profile; metabolomics; 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.

项目概要/摘要 激酶是最重要的药物靶点之一，具有临床意义的激酶抑制剂已被研究 针对多种疾病而开发。激酶的一个子集，即正在研究的暗激酶 (DK)，很少受到研究。 或由于没有关于其生化和生物学功能的基础数据而不受关注。这 该提案将通过用小分子干扰 DK 来收集此类数据，然后测量 使用多重蛋白质组学、基因表达、代谢组学和成像测定的细胞结果。一个 与人类疾病有潜在联系的 DK 子集将得到深入研究，作为鉴定新的 DK 的手段。 治疗药物靶点。本项目收集的数据将与来自以下机构的现有信息进行汇总 之前 NIH 资助的大规模结构和基因组项目创建了暗激酶知识库 （DKK）提供有关暗激酶组及其相互作用的逐基因和网络级信息 其他信号转导和调节网络。与 NIH LINCS 项目的密切协调将确保 数据互操作性并有效利用信息学工具。 DKK将合作开发 与 IDG 知识管理中心 (KMC) 合作，遵守可查找、可访问、 可互操作和可重用（FAIR）数据，并且可供人类用户和机器访问（通过 API）。 使用提供的新遗传和化学工具对市售 DK 试剂进行验证和扩展 到资源传播中心（RDOC）。整体方法将是迭代的，方法更简单 首先应用（例如简单的基因敲除），随后应用更复杂的方法（例如稳定 CRIPSRa/i）由化学家、计算生物学家、质谱学家组成的跨学科团队追求 和药理学家致力于五个相关目标。目标 1 将开发一种计算算法来确定优先级 DK，开发和维护 DKK，并使用监督和分析对激酶组进行网络级分析 无监督机器学习。目标 2 将使用以下方法测量正常和受干扰细胞中的激酶丰度 使用稳定同位素稀释 (PRM-SID) 和 RNASeq 进行平行反应监测，并使用 网络推理工具可深入了解不同细胞类型中的暗激酶组和亮激酶组。目标 3 会扰乱 使用 CRIPSR/Cas9 介导的基因敲除、CRIPSRa/i 等遗传工具来诱导更微妙的 DK 下调和诱导基因失活。对细胞命运、形态和信号转导的影响 然后将使用 PRM-SID、磷酸蛋白质组学、RNASeq、基因报告分析、代谢组学来确定 分析和高度多重单细胞成像。目标 4 将 DK 分析扩展到小分子抑制剂 仔细分析现有的抗 DK 药物并设计和合成新的化学配体。目标 5 将涉及与其他研究人员合作，检测原代人类中 DK 的表达和功能 与 NIH 精准医学计划相关的细胞和组织。所有目标将同时实现 逐步扩大继续研究 DK 的数据和工具资源。