Kinase-centric chemoproteomics utilizing targeted affinity enrichment and state-of-the-art quantitative mass spectrometry analyses

利用靶向亲和力富集和最先进的定量质谱分析以激酶为中心的化学蛋白质组学

• 批准号: 237548465
• 负责人: Dr. Martin Golkowski
• 金额: --
• 依托单位: Institut für Organische Chemie
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/237548465?language=en
• 关键词: Kinase; centric; chemoproteomics; utilizing; targeted

Protein phosphorylation by kinases serves as an important molecular switch in most cellular processes. Kinases activate and shut down signaling cascades, mediate protein-protein interactions and their dysregulation commonly leads to diseases like cancer. Because they act as integrators and effectors of cellular stimuli, the abundance and activity of kinases are often used as reporters for the cellular state. Kinase inhibitors have recently been used as affinity agents together with quantitative mass spectrometry (MS) analyses to profile kinase inhibitor selectivity. While demonstrating the ability to enrich kinases for global proteomic analyses, these studies did not functionally interrogate specific signaling pathways or the activation states of kinases. Here we suggest a strategy utilizing targeted affinity enrichment and quantitative mass spectrometry analyses enabling the in depth characterization of abundance of kinases and the functional state of the corresponding signaling modules. To achieve kinase-centric protein enrichment, two series of small molecule (SM) kinase inhibitor-based affinity agents will be prepared. The first series comprises type I kinase inhibitor analogs allowing selective or global, activation-state independent, enrichment of kinases and their interaction partners. The second series consists of type II kinase inhibitors facilitating activation state-dependant enrichment of kinases and associated proteins. In a proof of concept study, affinity matrices prepared from these inhibitor analogs will be used for the pathway-specific and activation state-dependent enrichment of kinases and associated cellular components involved in epidermal growth factor (EGFR) - mitogen activated protein kinase (MAPK) signaling. Seven different NCI60 cell lines resembling important human cancer pathologies will serve as the model proteomes. The enriched sub-proteomes will be analyzed by quantitative MS in regard to (i) enrichment efficiency of EGFR - MAPK signaling components, (ii) accuracy and reproducibility of the identification and quantitation of these components and (iii) the ability to characterize the enriched kinases and associated proteins, including post-translational modifications (PTMs). This should facilitate the functional characterization of the EGFR - MAPK signaling module. On a later stage, we wish to apply our approach to the superordinate ErbB signaling network and proteomes derived from patient samples. Our long term goal is to provide novel analytical tools for functional cancer proteomics enabling rapid and in depth characterization of aberrant kinase signaling. The proteome-level information obtained should complement genome-level information obtained from sequencing cancer genomes and thus extend the analytical armory for the fight against cancer.

蛋白激酶的磷酸化在大多数细胞过程中是一个重要的分子开关。激酶激活和关闭信号级联，介导蛋白质-蛋白质相互作用，它们的失调通常会导致癌症等疾病。因为它们是细胞刺激的整合因子和效应器，所以它们的丰度和活性经常被用来作为细胞状态的报告。最近，激酶抑制剂已被用作亲和剂，并与定量质谱仪(MS)分析一起来分析激酶抑制剂的选择性。虽然这些研究展示了在全球蛋白质组学分析中丰富激酶的能力，但并没有从功能上询问特定的信号通路或激酶的激活状态。在这里，我们建议了一种策略，利用靶向亲和力浓缩和定量质谱分析，能够深入表征丰富的激酶和相应信号模块的功能状态。为了实现以激酶为中心的蛋白浓缩，将制备两个系列的基于小分子(SM)激酶抑制剂的亲和剂。第一个系列包括I型激酶抑制类似物，允许选择性或全局的、激活状态独立的、丰富的激酶及其相互作用伙伴。第二个系列包括促进激活状态依赖的激酶和相关蛋白的富集化的II型激酶抑制剂。在一项概念验证研究中，由这些抑制物类似物制备的亲和力矩阵将用于对参与表皮生长因子(EGFR)-丝裂原激活蛋白激酶(MAPK)信号转导的激酶和相关细胞成分进行路径特异性和激活状态依赖的浓缩。七种不同的NCI60细胞株与重要的人类癌症病理相似，将作为蛋白质组的模型。定量MS将从以下方面分析富集亚蛋白质组：(I)EGFR-MAPK信号组分的富集率，(Ii)这些组分鉴定和定量的准确性和重复性，以及(Iii)富集酶及其相关蛋白的特性，包括翻译后修饰(PTM)的能力。这应该有助于EGFR-MAPK信号模块的功能表征。在稍后的阶段，我们希望将我们的方法应用于来自患者样本的上级ErbB信号网络和蛋白质组。我们的长期目标是为功能癌症蛋白质组学提供新的分析工具，使之能够快速和深入地表征异常激酶信号。获得的蛋白质组水平信息应该补充通过对癌症基因组测序获得的基因组水平信息，从而扩展抗癌斗争的分析武器库。