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: 9453342
• 负责人: GARY L. JOHNSON
• 金额: $ 230.82万
• 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/9453342
• 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; Darkness; 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; Informatics; 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; drug development; human disease; 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

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/摘要 激酶是最重要的药物靶点之一，临床上重要的激酶抑制剂已经被发现。 针对多种疾病开发。激酶的一个子集，研究不足的暗激酶（DKs）， 或者因为没有关于它们的生物化学和生物学功能的基础数据而不受关注。这 一项提案将通过遗传学和小分子干扰DK来收集这些数据，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 将深入研究与人类疾病有潜在联系的DK子集，作为鉴定新的 治疗药物靶点。本项目中收集的数据将与来自 以前NIH资助的大规模结构和基因组项目，以创建一个黑暗激酶知识库 (DKK)它提供了关于暗激酶组及其与 其他信号转导和调控网络。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将与 与IDG知识管理中心（KMC）合作，遵守可查找、可解释 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）。 通过提供新的遗传和化学工具验证和扩展市售DK试剂 资源传播中心（RDOC）。总体方法将是迭代的，方法更简单 首先应用（例如简单基因敲除），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进行 和药理学家致力于五个相互关联的目标。Aim 1将开发一种计算算法， DK，开发和维护DKK，并使用监督和 无监督机器学习目标2将测量激酶丰度在正常和扰动细胞使用 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，以提供对不同细胞类型中的暗和亮激酶组的洞察。Aim 3会干扰 DK与基因工具，如CRIPSR/Cas9介导的基因敲除，CRIPSRa/i诱导更多的微妙了 下调和诱导基因不活动。对细胞命运、形态和信号转导的影响 然后将使用PRM-SID、磷酸化蛋白质组学、RNASeq、基因报告基因测定、代谢组学 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， 仔细分析现有的药物对DK和设计和合成新的化学配体。目标5 将与其他研究者合作，在原代人中检测DKs的表达和功能， 与NIH精准医学计划相关的细胞和组织。所有目标都将同时实现， 逐步扩大数据和工具资源，以继续研究DK。