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A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE)

机译:从共洗脱数据(PrInCE)预测相互作用组的快速准确方法

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摘要

BackgroundAn organism’s protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome.
机译:背景生物的蛋白质相互作用组或蛋白质-蛋白质相互作用的完整网络定义了驱动细胞进程的蛋白质复合物。传统上,研究蛋白质复合物的技术采用了靶向策略,如酵母双杂交或亲和纯化-质谱法来评估蛋白质相互作用。但是,鉴于蛋白质复合物的数量众多,需要更多可扩展的方法来加速相互作用的发现并构建整个相互作用组。我们最近开发了一种互补技术,该技术基于蛋白相关分析(PCP)和细胞培养氨基酸(SILAC)中氨基酸的稳定同位素标记来评估色谱共洗脱作为相互作用蛋白的证据。重要的是,PCP-SILAC还能够同时在多种生物学条件下测量蛋白质相互作用,从而可以检测相互作用组的治疗特异性变化。鉴于共洗脱数据的独特性和高维性,需要新的工具来比较蛋白质洗脱曲线,控制错误发现率并构建准确的相互作用组。

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