Center for Proteomics and Bioinformatics, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44106, USA.
J Proteome Res. 2010 Oct 1;9(10):4972-81. doi: 10.1021/pr100267t.
Protein interaction network maps have been generated for multiple species, making use of large-scale methods such as yeast two-hybrid (Y2H) and affinity purification mass spectrometry (AP-MS). These methods take fundamentally different approaches toward characterizing protein networks, and the resulting data sets provide complementary views of the protein interactome. The specific determinants of the outcome of Y2H and AP-MS experiments, in terms of detection of interacting proteins are, however, poorly understood. Here we show that a statistical model built using sequence- and annotation-based features of bait proteins is able to identify bait features that are significant determinants of the outcome of interaction proteomics experiments. We show that bait features are able to explain in part the disparities observed between Y2H and AP-MS constructed networks and can be used to derive the "bait compatibility index", a numeric score that assesses the compatibility of bait proteins with each technology. Aside from understanding the bias and limitations of interaction proteomics, our approach provides a rational, data-driven method for prioritization of baits for interaction proteomics experiments, an essential requirement for future proteome-wide applications of these technologies.
已经为多个物种生成了蛋白质相互作用网络图谱，利用了酵母双杂交（Y2H）和亲和纯化质谱（AP-MS）等大规模方法。这些方法从根本上采用了不同的方法来描述蛋白质网络，而得到的数据集合提供了蛋白质相互作用组的互补视图。然而，关于 Y2H 和 AP-MS 实验的结果（就检测相互作用的蛋白质而言）的具体决定因素，理解得很差。在这里，我们表明，使用诱饵蛋白的序列和注释特征构建的统计模型能够识别诱饵特征，这些特征是相互作用蛋白质组学实验结果的重要决定因素。我们表明，诱饵特征能够部分解释在 Y2H 和 AP-MS 构建的网络之间观察到的差异，并且可以用于得出“诱饵兼容性指数”，这是一个数字评分，评估诱饵蛋白与每种技术的兼容性。除了了解相互作用蛋白质组学的偏差和局限性之外，我们的方法还为相互作用蛋白质组学实验中的诱饵优先级提供了一种合理的数据驱动方法，这是这些技术在未来全蛋白质组应用中的基本要求。
