Quantitative Interaction Networks for Tyrosine-Phosphorylated Proteins

描述（由申请人提供）：涉及蛋白酪氨酸激酶的细胞内信号网络对大多数细胞过程的控制至关重要，包括生长、粘附、迁移、分化和凋亡。这些网络的失调会导致多种人类疾病，包括癌症、糖尿病和免疫缺陷。这些网络中的许多蛋白质含有Src同源性2 （SH2）或磷酸酪氨酸结合（PTB）结构域，它们以序列特异性的方式识别酪氨酸磷酸化的蛋白质。在本研究中，我们将利用蛋白质微阵列技术研究人类基因组中编码的几乎所有SH2和PTB结构域的分子识别特性，以及与生理相关的配体。重组SH2/PTB结构域将被排列在微滴板的孔中，随后用258个荧光标记的磷酸肽（代表实验验证的人受体酪氨酸激酶上酪氨酸磷酸化位点）以及604个代表下游蛋白（非受体酪氨酸激酶和含SH2/PTB的蛋白）上酪氨酸磷酸化位点进行探针检测。通过用8种不同浓度的肽探测阵列，将确定每种肽与每种蛋白质（约140个活性SH2/PTB构建物）结合的平衡解离常数。这项工作将产生高质量、定量的蛋白质相互作用网络，揭示信号蛋白之间的个体连接，以及网络连接如何随着蛋白质浓度的变化而变化。我们之前曾提出过，蛋白质过度表达时变得更加混杂的程度有助于其致癌性，这里描述的研究将产生进一步调查这一假设所需的定量数据。此外，我们的系统性努力所揭示的信息对于研究酪氨酸激酶介导的信号传导的细胞和癌症生物学家，研究分子识别的计算生物学家，以及寻求信号转导网络模型的系统生物学家来说都是无价的。因此，我们打算通过一个交互式网站使我们的数据易于访问，其格式既适合广泛的计算研究，也适合更集中的假设驱动的查询。我们希望这里描述的研究将阐明信号蛋白如何整合到复杂的网络中，以及我们如何在这些网络出错时最有效地进行干预。

DESCRIPTION (provided by applicant): Intracellular signaling networks that involve protein tyrosine kinases are critical in the control of most cellular processes, including growth, adhesion, migration, differentiation, and apoptosis. Misregulation of these networks results in a variety of human diseases, including cancer, diabetes, and immune deficiency. Many of the proteins in these networks contain Src homology 2 (SH2) or phosphotyrosine binding (PTB) domains, which recognize tyrosine-phosphorylated proteins in a sequence-specific fashion. In this proposal, the molecular recognition properties of virtually every SH2 and PTB domain encoded in the human genome will be investigated with respect to physiologically-relevant ligands using protein microarray technology. Recombinant SH2/PTB domains will be arrayed in the wells of microtiter plates and subsequently probed with 258 fluorescently-labeled phosphopeptides representing experimentally-verified sites of tyrosine phosphorylation on human receptor tyrosine kinases, as well as with 604 peptides representing sites of tyrosine phosphorylation on downstream proteins (nonreceptor tyrosine kinases and SH2/PTB-containing proteins). By probing the arrays with eight different concentrations of each peptide, equilibrium dissociation constants will be determined for the binding of each peptide to each protein (~140 active SH2/PTB constructs). This effort will produce high quality, quantitative protein interaction networks which will reveal individual connections between signaling proteins, as well as how network connectivity changes with protein concentration. We have previously proposed that the extent to which a protein becomes more promiscuous when overexpressed contributes to its oncogenicity, and the study described here will generate the quantitative data needed to investigate this hypothesis further. In addition, the information revealed by our systematic efforts should prove invaluable to cell and cancer biologists who study tyrosine kinase-mediated signaling, to computational biologists who study molecular recognition, and to systems biologists who seek to model signal transduction networks. As such, we intend to make our data easily accessible though an interactive web site in formats suitable both for broad computational studies and for more focused hypothesis-driven inquiries. It is our hope that the studies described here will shed light on how signaling proteins are integrated into complex networks and how we can intervene most effectively when these networks go awry.