Quantitative Interaction Networks for Tyrosine-Phosphorylated Proteins

描述(申请人提供)：涉及蛋白酪氨酸激酶的细胞内信号网络在大多数细胞过程的控制中至关重要，包括生长、黏附、迁移、分化和凋亡。这些网络的错误调控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都含有Src同源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。在这项建议中，将利用蛋白质微阵列技术研究人类基因组中编码的几乎每个SH2和PTB结构域与生理相关配体的分子识别特性。重组的SH2/PTB结构域将排列在微滴定板的孔道中，随后用258个荧光标记的磷酸肽(代表实验证实的人受体酪氨酸激酶上的酪氨酸磷酸化位点)以及604个肽(代表下游蛋白(非受体酪氨酸激酶和含SH2/PTB的蛋白)上的酪氨酸磷酸化位点)进行探测。通过探测每个肽的8个不同浓度的阵列，将确定每个肽与每个蛋白质(~140个活性SH2/PTB结构)结合的平衡解离常数。这一努力将产生高质量、定量的蛋白质相互作用网络，它将揭示信号蛋白质之间的个别连接，以及网络连接如何随着蛋白质浓度的变化而变化。我们以前曾提出，蛋白质在过度表达时变得更加混杂的程度有助于其致癌作用，这里描述的研究将产生进一步研究这一假说所需的定量数据。此外，我们系统性的努力所揭示的信息对于研究酪氨酸激酶介导的信号传递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试图对信号转导网络建模的系统生物学家来说应该是非常有价值的。因此，我们打算通过一个互动式网站方便地访问我们的数据，其格式既适合于广泛的计算研究，也适合于更专注于假设驱动的查询。我们希望这里描述的研究将阐明信号蛋白是如何整合到复杂网络中的，以及当这些网络出现问题时，我们如何才能最有效地进行干预。

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.