使用SeraFILE分离平台的磷酸二酯酶功能蛋白质组学分析。

International journal of proteomics Pub Date : 2012-01-01 Epub Date: 2012-11-25 DOI:10.1155/2012/515372
Amita R Oka, Matthew P Kuruc, Ketan M Gujarathi, Swapan Roy
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引用次数: 2

摘要

功能蛋白质组学分析可以帮助确定疾病诊断和治疗的靶点。可用的方法是有限的,无法描述许多功能特性测量酶动力学。本文提出的功能性蛋白质组分析方法旨在克服这些限制。首先使用SeraFILE(专有的蛋白质分离平台)对组织/细胞系提取物进行基于表面的蛋白质组分离。然后表征所得亚蛋白质组的酶动力学特性,并将数据整合到蛋白质组谱中。作为一个模型,从牛脑匀浆(BBH)和大鼠脑匀浆(RBH)中提取的serafile衍生的环核苷酸水解磷酸二酯酶(PDEs)亚蛋白质组在存在(激发条件)和不存在cGMP的情况下对cAMP水解活性进行了表征。RBH和BBH的功能谱是根据各自亚蛋白质组对挑战条件的酶活性响应来编制的。样本间分析表明,可比较的图谱仅在少数数据点上存在差异,并且不同的亚蛋白质组可以从来自不同动物的可比较组织样本中产生。这些结果表明,所提出的方法提供了一种简化样品间差异的方法,并定位了可归因于样品特异性反应的蛋白质。它可以潜在地应用于生物标志物发现和药物发现分析中的疾病和非疾病样品比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Functional Proteomic Profiling of Phosphodiesterases Using SeraFILE Separations Platform.

Functional proteomic profiling can help identify targets for disease diagnosis and therapy. Available methods are limited by the inability to profile many functional properties measured by enzymes kinetics. The functional proteomic profiling approach proposed here seeks to overcome such limitations. It begins with surface-based proteome separations of tissue/cell-line extracts, using SeraFILE, a proprietary protein separations platform. Enzyme kinetic properties of resulting subproteomes are then characterized, and the data integrated into proteomic profiles. As a model, SeraFILE-derived subproteomes of cyclic nucleotide-hydrolyzing phosphodiesterases (PDEs) from bovine brain homogenate (BBH) and rat brain homogenate (RBH) were characterized for cAMP hydrolysis activity in the presence (challenge condition) and absence of cGMP. Functional profiles of RBH and BBH were compiled from the enzyme activity response to the challenge condition in each of the respective subproteomes. Intersample analysis showed that comparable profiles differed in only a few data points, and that distinctive subproteomes can be generated from comparable tissue samples from different animals. These results demonstrate that the proposed methods provide a means to simplify intersample differences, and to localize proteins attributable to sample-specific responses. It can be potentially applied for disease and nondisease sample comparison in biomarker discovery and drug discovery profiling.

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