Physical and Genetic Interaction Landscape of the Tyrosine Kinome

描述（由申请人提供）： 酪氨酸激酶的信号传导在哺乳动物信号转导中起主要作用，并且是几乎每种主要疾病类型的主要组成部分。然而，对信号通路的系统理解仍然难以捉摸。虽然已经开发了许多技术来映射信号传导途径，但单独使用它们只能提供对信号传导的一个方面的洞察。我们将使用一个综合的方法，通过使用互补的物理和遗传相互作用映射方法，绘制在多个水平上控制酪氨酸激酶信号传导的网络。这些地图将导致网络模型，反映蛋白质，可以在功能上调节信号，并与激酶，包括底物，衔接子和调节亚基物理相关。为了实现这一目标，该提案整合了加州大学旧金山分校弗朗西斯科研究人员在高通量物理和遗传相互作用图谱（Krogan）、追踪信号通路的化学遗传方法（Shokat）以及网络分析和数据集成（Bandyopadhyay）方面的互补专业知识。我们的目标是通过亲和纯化-质谱法（AP-MS）鉴定与酪氨酸激酶相关的蛋白质，并通过目标1中的共价捕获和释放鉴定激酶底物。这些数据将进一步特点，使用一个新开发的平台，在哺乳动物细胞中的定量遗传相互作用映射，这将建立这些相互作用的功能相关性，通过系统地确定目的2中的激酶和相关蛋白质之间的上位遗传关系。最后，在目标3中，我们将使用网络建模来统一前两个目标中收集的数据，以揭示与哺乳动物酪氨酸激酶相关的详细的机械生物学见解。所有数据（原始，处理和整合）将立即以互动和可搜索的方式提供，以便其他人可以利用我们收集的酪氨酸激酶组信息。

DESCRIPTION (provided by applicant): Signaling by tyrosine kinases play a major role in mammalian signal transduction and is a major component of nearly every major disease type. However, a systematic understanding of signaling pathways has remained elusive. While a number of techniques have been developed to map signaling pathways, used alone they only provide insight into one facet of signaling. We will use an integrative approach to chart the networks which control tyrosine kinase signaling at multiple levels through the use of complementary physical and genetic interaction mapping approaches. These maps will lead to network models which reflect proteins that can functionally modulate signaling and are physically associated with kinases including substrates, adaptors and regulatory subunits. To achieve this, the proposal integrates the complementary expertise of investigators at the University of California-San Francisco in high-throughput physical and genetic interaction mapping (Krogan), chemical-genetic approaches for tracing signaling pathways (Shokat) and network analysis and data integration (Bandyopadhyay). We aim to identify proteins that associate with tyrosine kinases through affinity purification-mass spectrometry (AP-MS) and kinase substrates through covalent capture-and-release in Aim 1. These data will be further characterized using a newly developed platform for quantitative genetic interaction mapping in mammalian cells, which will establish the functional relevance of these interactions by systematically identifying epistatic genetic relationships between kinases and associated proteins in Aim 2. Lastly, in Aim 3, we will unify data collected in the first two aims using network modeling to uncover detailed, mechanistic biological insights relating to mammalian tyrosine kinases. All data (raw, processed and integrated) will be made immediately available in an interactive and searchable fashion so that others can exploit the information we have collected on the tyrosine kinome.