Chemical Inhibitors to Define an Essential M. Tuberculosis Signaling Network

描述（由申请人提供）：作为对RFA-AI-11-004的回应，本申请提出了一项高度合作的研究计划，以使用小分子化学抑制剂作为探针来表征结核分枝杆菌中受必需丝氨酸/苏氨酸蛋白激酶PknA和PknB调控的生理过程。这些激酶本身就是候选药物靶标，并调节下游蛋白质和途径，这些蛋白质和途径也将成为药物开发的潜在靶标。PI和合作研究者将汇集转录组学，磷酸蛋白质组学，脂质组学，代谢组学和计算生物学方面的最新专业知识，目标是实现对PknA/PknB信号网络的系统级理解。除了这种多平台的“组学”方法，该提案的一个重要优势是在这项研究中使用这些激酶的新型高效化学抑制剂作为PknA/PknB调节网络的探针。该研究提出了两种无偏见的，广泛的方法来确定这些激酶调节的过程，并有针对性的方法集中在两个基本的细胞包膜生物合成途径，大量数据表明PknA和PknB的调节作用：肽聚糖合成和分枝菌酸合成。虽然每个研究平台都将生成数据，直接洞察PknA和PknB的功能，但这种方法的另一个关键组成部分是使用计算方法来建模集成不同类型实验数据的网络。这项研究的结果将为PknA/PknB信号通路及其调节过程提供系统水平的见解。我们期望这些知识将证实这些激酶是M. PknA和PknB蛋白质及其调控过程也可能成为抗结核药物开发的有吸引力的靶点。

DESCRIPTION (provided by applicant): In response to RFA-AI-11-004, this application proposes a highly collaborative research plan to use small molecule chemical inhibitors as probes to characterize the physiologic processes in Mycobacterium tuberculosis that are regulated by the essential Serine/Threonine protein kinases PknA and PknB. These kinases are both candidate drug targets themselves, and regulate downstream proteins and pathways that will also be potential targets for drug development. The PI and co-investigators will bring together state of the art expertise in transcriptomics, phosphoproteomics, lipidomics, metabolomics and computational biology with the goal of achieving a systems-level understanding of the PknA/PknB signaling network. In addition to this multi-platform "omics" approach, an important strength of the proposal is the use in this research of novel highly potent chemical inhibitors of these kinases as probes for PknA/PknB-regulated networks. The research proposes both unbiased, broad approaches to identify processes regulated by these kinases, and targeted approaches focused on two essential cell envelope biosynthetic pathways for which substantial data indicates a regulatory role for PknA and PknB: peptidoglycan synthesis and mycolic acid synthesis. While each research platform will generate data that will provide direct insight into the functions of PknA and PknB, another key component of this approach is to use computational methods to model networks that integrate the different types of experimental data. The results of this research will provide systems-level insights into the PknA/PknB signaling pathways and the processes that they regulate. We expect this knowledge will validate these kinases as excellent targets for M. tuberculosis drug development and identify proteins and processes regulated PknA and PknB that may also be attractive targets for anti-tuberculosis drug development.