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: 10015261
• 负责人: GARY L. JOHNSON
• 金额: $ 225.92万
• 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/10015261
• 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 interoperability; data resource; data reuse; data tools; design; human disease; informatics tool; innovation; insight; 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.

项目摘要/摘要 激酶是最重要的药物靶标之一，临床上显着的激酶抑制剂一直是 为多种疾病开发。激酶的一部分，研究的深色激酶（DKS）几乎没有收到 或没有注意力，因为没有关于其生化和生物学功能的基础数据。这 提案将通过遗传和小分子扰动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，代谢组和成像测定法的细胞后果。一个 具有与人类疾病潜在联系的DK的子集将被深入研究，以作为新的 治疗药物靶标。该项目中收集的数据将与现有信息一起汇总 NIH资助的大规模结构和基因组项目，以创建一个深色激酶知识基础 （dkk）提供有关黑暗Kinome的基因和网络级别的信息及其与之相互作用 其他信号转导和监管网络。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将在合作中开发 借助IDG知识管理中心（KMC），遵守标准以供可找到，可访问， 可互操作和可重复使用的（公平）数据，并可以通过API访问人类用户和机器。 商业上可用的DK试剂已通过提供的新遗传和化学工具进行验证和扩展 到资源传播中心（RDOC）。总体方法将是迭代的，使用更简单的方法 首先应用（例如，简单基因敲除）和更复杂的方法随后（例如稳定 Cripsra/i）由化学家，计算生物学家，质谱学家的跨学科团队追求 以及从事五个链接目标的药理学家。 AIM 1将开发一种计算算法以优先考虑 DK，开发和维护DKK，并使用监督和 无监督的机器学习。 AIM 2将使用正常细胞和扰动细胞中的激酶丰度测量 使用稳定的同位素稀释（PRM-SID）和RNASEQ和使用数据分析的数据进行平行反应监测 网络推理工具可洞悉各种细胞类型中的黑暗和光性型。 AIM 3会扰动 带有遗传工具的DK，例如Cripsr/Cas9介导的基因敲除，Cripsra/i，以引起更多微妙的 并调节和诱导基因无动物。对细胞命运，形态和信号转导的影响 然后将使用PRM-SID，磷蛋白质组学，RNASEQ，Gene Reporter分析，代谢组学确定 分析和高度多重的单细胞成像。 AIM 4将通过DK分析扩展到小分子抑制剂 通过设计和合成新的化学配体来仔细分析现有的药物。目标5 将涉及与其他研究人员的合作，以测定DKS在主要人类中的表达和功能 与NIH精密医学计划有关的细胞和组织。所有目标都将同时追求 逐步扩大数据和工具的资源，以继续研究DKS。