Quantitative Interaction Networks for Tyrosine-Phosphorylated Proteins

酪氨酸磷酸化蛋白质的定量相互作用网络

• 批准号: 7290872
• 负责人: GAVIN MACBEATH
• 金额: $ 52.32万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-13 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7290872
• 关键词: Address; Adhesions; Antibodies; Apoptosis; Binding; Cell Nucleus; Cell membrane; Cell physiology; Cells; Classification; Complex; Count; Data; Diabetes Mellitus; Dissociation; Epidermal Growth Factor Receptor; Equilibrium; ErbB Receptor Family Protein; Eukaryota; Eukaryotic Cell; Event; FGFR1 gene; FGFR3 gene; FRS2 gene; Family; Fibroblast Growth Factor Receptors; Genome; Growth; Half-Life; Human; Human Genome; IRS1 gene; Immune; Individual; Insulin Receptor; Internet; Investigation; Label; Ligands; Light; Location; Malignant Neoplasms; Mediating; Microarray Analysis; Modeling; Molecular; Neurites; Outcome; PC12 Cells; PTB Domain; Peptides; Phosphopeptides; Phosphotyrosine; Principal Investigator; Process; Property; Protein Microchips; Protein Overexpression; Protein Tyrosine Kinase; Proteins; Receptor Protein-Tyrosine Kinases; Recombinants; Recruitment Activity; Research Personnel; Role; Signal Transduction; Signaling Molecule; Signaling Protein; Site; Specificity; Substrate Interaction; System; Time; Tyrosine; Tyrosine Phosphorylation; Tyrosine Phosphorylation Site; Vascular Endothelial Growth Factor Receptor; Western Blotting; base; cancer cell; computer studies; computerized data processing; enzyme substrate; human FRS2 protein; human disease; insulin receptor substrate 1 protein; metaplastic cell transformation; migration; molecular recognition; neglect; novel; numb protein; programs; protein protein interaction; ras GTPase-Activating Proteins; receptor; src-Family Kinases

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.

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于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 2 (SH2) 或磷酸酪氨酸结合 (PTB) 结构域，它们以序列特异性方式识别酪氨酸磷酸化蛋白质。在该提案中，将使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 结构域的分子识别特性，以及生理相关的配体。重组 SH2/PTB 结构域将排列在微量滴定板的孔中，随后用代表经过实验验证的人受体酪氨酸激酶上酪氨酸磷酸化位点的 258 个荧光标记磷酸肽以及代表下游蛋白质（非受体酪氨酸）上酪氨酸磷酸化位点的 604 个肽进行探测。 激酶和含有 SH2/PTB 的蛋白质）。通过用每种肽的八种不同浓度探测阵列，将确定每种肽与每种蛋白质（约 140 个活性 SH2/PTB 构建体）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，该网络将揭示信号蛋白之间的个体连接，以及网络连接如何随蛋白质浓度而变化。我们之前提出，过度表达时蛋白质变得更加混杂的程度有助于其致癌性，本文描述的研究将产生进一步研究这一假设所需的定量数据。此外，我们的系统工作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建模的系统生物学家来说应该是无价的。因此，我们打算通过交互式网站以适合广泛计算研究和更集中的假设驱动查询的格式轻松访问我们的数据。我们希望这里描述的研究能够阐明信号蛋白如何整合到复杂的网络中，以及当这些网络出现问题时我们如何能够最有效地进行干预。