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DEVELOPMENT OF HIGHLY SELECTIVE PROTEIN AND LIPID KINASE INHIBITORS

DEVELOPMENT OF HIGHLY SELECTIVE PROTEIN AND LIPID KINASE INHIBITORS
高选择性蛋白质和脂质激酶抑制剂的开发
批准号:
8169783
负责人:
KEVAN M. SHOKAT
金额:
$0.18万
依托单位国家:
美国
项目类别:
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-09-12 至 2011-05-31

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项目成果

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中文摘要
翻译
这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者(PI)可能从另一个NIH来源获得了主要资金, 因此可以在其他CRISP条目中表示。列出的机构是 中心,不一定是研究者的机构。 蛋白质和脂质激酶是细胞信息的重要传递者。 人类基因组中有500多种蛋白激酶和30多种脂质激酶。 我们有兴趣开发化学方法来理解和破译激酶介导的信号通路。 本项目的目标是开发高选择性的小分子蛋白质和脂质激酶抑制剂。 这些小分子被设计成适合激酶的ATP结合口袋。 我们使用合成有机化学结合结构生物学(X射线共晶结构)来开发这些分子。 我们通常使用五个或更少步骤的集中合成,每个设计大约20个候选抑制剂。 这项工作需要中间体和最终产品的标准结构表征,以供出版或结构分配之用。 通常我们需要小分子的高分辨率质谱。 它们通常在结构上高度相关,允许对参数进行优化并同时用于多个样本。
英文摘要
This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Protein and lipid kinases are important transducers of cellular information. There are 500+ protein kinases and 30+ lipid kinases in the human genome. We are interested in developing chemical approaches for understanding and deciphering kinase mediated signaling pathways. The goal of this project is to develop highly selective small molecule protein and lipid kinase inhibitors. These small molecules are designed to fit in the ATP binding pocket of the kinases. We use synthetic organic chemistry coupled with structural biology (X-ray co-crystal structures) to develop these molecules. We typically use focused syntheses of five or fewer steps and make on the order of 20 candidate inhibitors per design. This work requires standard structural characterization of the intermediates and final products for publication or structural assignment purposes. Typically we need high resolution mass spectrometry of the small molecules. They are often highly structurally related, allowing for parameters to be optimized and used for a number of samples at once.
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