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New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size

机译:对蛋白质-蛋白质相互作用数据的新见解导致对啤酒酵母相互作用基因组大小的估计增加

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Background As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required. Results We examined the yeast interactome from a new perspective, by taking into account how thoroughly proteins have been studied. We discovered that the set of literature-curated protein-protein interactions is qualitatively different when restricted to proteins that have received extensive attention from the scientific community. In particular, these interactions are less often supported by yeast two-hybrid, and more often by more complex experiments such as biochemical activity assays. Our analysis showed that high-throughput and literature-curated interactome datasets are more correlated than commonly assumed, but that this bias can be corrected for by focusing on well-studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high-throughput data. It yields an estimate of at least 37, 600 direct physical protein-protein interactions in S. cerevisiae. Conclusions Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected.
机译:背景技术由于蛋白质相互作用介导了大多数细胞机制,因此蛋白质-蛋白质相互作用网络在研究细胞过程中至关重要。因此,已经进行了几个大规模的相互作用组作图项目,并且通过文献管理将蛋白质-蛋白质相互作用提炼到数据库中。然而,即使在模型生物酿酒酵母中,蛋白质-蛋白质相互作用数据仍远未全面。估计相互作用组的大小对于评估当前数据集的完整性非常重要,以便测量所需的剩余工作量。结果我们考虑了对蛋白质的研究,从一个新的角度检查了酵母相互作用组。我们发现,当仅限于受到科学界广泛关注的蛋白质时,由文献策划的蛋白质-蛋白质相互作用的集合在质量上是不同的。特别是,酵母双杂交不经常支持这些相互作用,而更复杂的实验(例如生化活性测定)则更经常支持这些相互作用。我们的分析表明,高通量和文献策划的相互作用组数据集比通常假定的具有更高的相关性,但是可以通过关注经过充分研究的蛋白质来纠正这种偏差。因此,我们提出了一种简单而可靠的方法来估计一个相互作用组的大小,将涉及精心研究的蛋白质的文献资料与高通量数据结合起来。估计啤酒酵母中至少有37,600个直接的物理蛋白质-蛋白质相互作用。结论我们的方法导致对相互作用组大小的更高和更准确的估计,因为它说明了真正的相互作用,但很难用常用的实验方法进行检测。这表明我们与完成酵母相互作用组图谱的距离比预期的还要遥远。

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