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Project 3: Protein Design for Selective Interference with LPA Signaling in Colon Cancer

Project 3: Protein Design for Selective Interference with LPA Signaling in Colon Cancer
项目 3:选择性干扰结肠癌 LPA 信号传导的蛋白质设计
批准号:
8813298
负责人:
Gevorg Grigoryan
金额:
$27.42万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
--
资助国家:
美国
项目状态:
未结题
起止时间:
至

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中文摘要
翻译
2013年,结直肠癌是美国第三大最常见的癌症死亡原因。这 在美国,疾病每年夺走5万多人的生命,折磨着14万多人,强调了一个明确和 迫切需要更好地控制这种无法治愈的恶性肿瘤。这项研究的目标是减弱致癌作用 通过抑制一个关键的增殖信号通路在结肠癌细胞中的活性 丝裂原溶血磷脂酸(LPA)。在结肠癌中,LPA通过LPA2受体发出信号,LPA2受体负责招募 它的C末端是一个支架蛋白NHERF-2。这种招募通过NHERF-2的PDZ结构域发生 (N2P2)-一种普遍存在的蛋白质模块,识别伙伴蛋白的C末端。我们的目标是抑制 NHERF-2:LPA2复合体,以及随之而来的LPA信号,使用设计的与N2P2紧密结合的多肽。 使这一方法复杂化的是最近的发现,另一种含有PDZ的蛋白质MAGI-3竞争 与NHERF-2结合LPA2并改变功能结果。NHERF-2与LPA2结合增加 致癌信号,而MAGI-3的结合(通过其PDZ结构域M3P6)降低了它,强调了需要 用于N2P2靶向的选择性。我们独一无二地能够使用我们的计算能力来提供这种选择性 使我们能够针对一系列域中的单个成员的技术。因此,我们的中心假设是 旨在抑制N2P2的多肽将下调致癌的LPA信号,我们的计算 方法学将发现对M3P6具有高N2P2亲和力和良好选择性的有效抑制剂 和其他PDZ结构域,在体外和细胞内。这项研究将通过三个具体目标来检验我们的假设。 目标1将确定已经针对N2P2而不是M3P6的多肽下调LPA信号 在结肠癌细胞中。目标2将测试我们的计算技术可以产生多肽的假设 通过设计和生物物理特性来选择跨越PDZome的N2P2。最后, AIM 3将使用蛋白质组/质谱分析和荧光相关技术来量化 我们的多肽在细胞内的选择性,并将生物物理参数与功能联系起来。我们预计为1) 验证N2P2靶向作为降低LPA致瘤性的可行策略,2)建立功能性 对M3P6(目标1)和其他PDZ结构域(目标2、3)和3)产生试剂的选择性的相关性 适合发起结肠癌治疗药物的开发。我们的目标与以下总主题非常契合 ITarget以生物分子靶向为中心。成为iTarget的一部分将极大地加快我们的工作。 与Kettenbach和McLellan博士的互动将在我们的蛋白质组/MS实验中提供关键指导 和结构特征。Madden博士和Gerber博士对职业生涯和 研究事项将确保外部资金具有很强的竞争力。VMIC和MTC核心将是无价的 用于细胞内和生物物理实验(VMIC)以及蛋白质的表达和纯化(MTC)。最后, ITarget将为我们的研究提供一个激励的智力环境,使我们的研究蓬勃发展。
英文摘要
Colorectal cancer was the third-most common cause of deaths from cancer in the United States in 2013. This disease claims over 50,000 lives and afflicts over 140,000 people annually in the US, underscoring a clear and urgent need for better control of this incurable malignancy. The goal of this study is to attenuate oncogenic activities in colon cancer cells by inhibiting a key proliferatory signaling pathway mediated by the powerful mitogen lysophosphatidic acid (LPA). In colon cancer, LPA signals through the LPA2 receptor, which recruits to its C-terminus a scaffolding protein NHERF-2. This recruitment occurs though a PDZ domain of NHERF-2 (N2P2)-a ubiquitous protein module that recognizes C-termini of partner proteins. Our goal is to inhibit the NHERF-2:LPA2 complex, and with it LPA signaling, using designed peptides that associate tightly with N2P2. Complicating this approach is the recent discovery that another PDZ-containing protein, MAGI-3, competes with NHERF-2 for binding to LPA2 and alters the functional outcome. Binding of NHERF-2 to LPA2 increases oncogenic signaling, while binding of MAGI-3 (via its PDZ domain M3P6) decreases it, underscoring the need for selectivity in N2P2 targeting. We are uniquely capable of providing such selectivity using our computational technologies that enable us to target a single member in a family of domains. Thus, our central hypothesis is that peptides designed to inhibit N2P2 will down-regulate oncogenic LPA signaling and that our computational methodology will uncover efficacious inhibitors with high N2P2 affinity and excellent selectivity against M3P6 and other PDZ domains, in vitro and in cells. The study will test our hypothesis through three specific aims. Aim 1 will establish that peptides already designed to target N2P2 and not M3P6 down-regulate LPA signaling in colon cancer cells. Aim 2 will test the hypothesis that our computational technology can produce peptides selective for N2P2 across the PDZome by designing and biophysically characterizing such peptides. Finally, Aim 3 will use a proteomic/mass-spectrometry assay along with fluorescence correlation techniques to quantify the selectivity of our peptides within cells and link biophysical parameters with function. We expect to 1) validate N2P2 targeting as a viable strategy for reducing LPA oncogenicity, 2) establish the functional relevance of selectivity against M3P6 (Aim 1) and other PDZ domains (Aims 2, 3), and 3) produce reagents suitable to initiate the development of colon cancer therapeutics. Our goals fit well with the overall theme of iTarget centered on biomolecular targeting. Being a part of the iTarget will accelerate our work significantly. Interactions with Drs. Kettenbach and McLellan will provide critical guidance in our proteomic/MS experiments and structural characterizations, respectively. Strong mentoring by Drs. Madden and Gerber on career and research matters will ensure high competitiveness for external funding. VMIC and MTC cores will be invaluable for in-cell and biophysical experiments (VMIC) and for expression and purification of proteins (MTC). Finally, iTarget will provide for a stimulating intellectual environment in which our research will thrive.
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Computational design of novel protein binders based on structure mining and learning from data
  • 批准号:
    10326369
  • 项目类别:
  • 资助金额:
    $35.44万
  • 财政年份:
    2020
  • 负责人:
    Gevorg Grigoryan
  • 依托单位:
Computational design of novel protein binders based on structure mining and learning from data
  • 批准号:
    9887271
  • 项目类别:
  • 资助金额:
    $40.64万
  • 财政年份:
    2020
  • 负责人:
    Gevorg Grigoryan
  • 依托单位:
Computational design of novel protein binders based on structure mining and learning from data
  • 批准号:
    10079500
  • 项目类别:
  • 资助金额:
    $35.42万
  • 财政年份:
    2020
  • 负责人:
    Gevorg Grigoryan
  • 依托单位:
Computational design of novel protein binders based on structure mining and learning from data
  • 批准号:
    10541909
  • 项目类别:
  • 资助金额:
    $35.46万
  • 财政年份:
    2020
  • 负责人:
    Gevorg Grigoryan
  • 依托单位:
海外基金