Illuminating Function of the Understudied Druggable Kinome

项目摘要/摘要 激酶是最重要的药物靶点之一，临床上有重要意义的激酶抑制剂一直是 为多种疾病开发的。未被充分研究的一组蛋白激酶，即暗蛋白激酶(DKs)，几乎没有得到证实 或者因为没有关于它们的生化和生物功能的基础数据而没有关注。这 Proposal将通过对DKS进行基因扰动和小分子干扰来收集此类数据，然后测量 使用多重蛋白质组、基因表达、代谢组学和成像分析的细胞后果。一个 将对与人类疾病有潜在联系的DK子集进行深入研究，作为一种手段来鉴定新的 治疗药物靶点。在此项目中收集的数据将与来自 之前由美国国立卫生研究院资助的大型结构和基因组项目，以创建黑暗激酶知识库 (DKK)，提供关于暗激动体及其相互作用的逐个基因和网络水平的信息 其他信号转导和调节网络。与NIH Lincs项目的密切协调将确保 数据互操作性和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)，遵循可查找、可访问 可互操作和可重复使用(公平)的数据，并可供人类用户和机器访问(通过API)。 提供新的遗传和化学工具来验证和推广商业上可用的DK试剂 至资源传播中心(RDoC)。总体方法将是迭代的，使用更简单的方法 首先应用(例如简单的基因敲除)，然后应用更复杂的方法(例如稳定的 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研究 药剂学家致力于五个相互关联的目标。目标1将开发一种计算算法来确定优先顺序 DKS，开发和维护DKK，并使用Supervised和 无监督机器学习。AIM 2将测量正常和受扰细胞中的激酶丰度 稳定同位素稀释法(PRM-SID)与RNAseq平行反应监测及数据分析 网络推理工具，提供对不同细胞类型中的暗和亮的动态组的洞察。《目标3》将扰乱 利用CRIPSR/Cas9介导的基因敲除、CRIPSRa/i等遗传工具诱导更多微妙的UP 下调和诱导性基因不起作用。对细胞命运、形态和信号转导的影响 然后使用PRM-SID、磷酸蛋白质组学、RNAseq、基因报告分析、代谢组学 剖析和高度多路复用的单细胞成像。AIM 4将通过以下方式将DK分析扩展到小分子抑制剂 仔细分析针对DKS的现有药物，并通过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，以分析DKS在原代人类中的表达和功能 与NIH精密医学倡议相关的细胞和组织。所有目标将在一年内并行实现 逐步扩大DKS继续研究的数据和工具来源。

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.