Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.
PLoS Comput Biol. 2011 Jan 27;7(1):e1001064. doi: 10.1371/journal.pcbi.1001064.
Following recent advances in high-throughput mass spectrometry (MS)-based proteomics, the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role of phosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, we report unexpected, large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ∼6,000 yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein-protein interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance, proteome disorders, literature-derived signaling reactomes, and in vitro substratomes of kinases. In the phospho-PPI, phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells, the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level. Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other.
近年来,高通量质谱(MS)为基础的蛋白质组学取得了显著进展,在各种生物中鉴定的磷酸化蛋白质及其磷酸化位点数量大大增加。尽管磷酸化对蛋白质信号的控制起着关键作用,但我们对磷酸蛋白质组的了解仍然有限。在这里,我们通过整合酵母多组学数据的数据挖掘,报告了磷酸蛋白质组和蛋白质相互作用组之间出乎意料的大规模联系。首先,通过基于 MS 的蛋白质组学获得了酵母细胞的新磷酸蛋白质组数据,并将其与公开的酵母磷酸蛋白质组数据进行了统一。这表明,近 60%的约 6000 个酵母基因编码磷酸化蛋白质。我们将这些统一的磷酸蛋白质组数据映射到酵母蛋白质-蛋白质相互作用(PPI)网络上,同时还将其他酵母多组学数据集(包括蛋白质丰度、蛋白质紊乱、文献衍生的信号转导反应组和激酶的体外基质组)中的信息映射到 PPI 网络上。在磷酸化 PPI 中,磷酸化蛋白质比非磷酸化蛋白质有更多的相互作用伙伴,这意味着细胞内很大一部分蛋白质相互作用模式(包括蛋白质复合物形成)受到可逆和替代磷酸化反应的影响。尽管高度丰富或无序的蛋白质在与其他蛋白质相互作用和在细胞内被磷酸化方面都有很高的机会,但磷酸化蛋白质和非磷酸化蛋白质的相互作用伙伴数量之间的差异在独立于蛋白质丰度和紊乱水平的情况下仍然显著。此外,对磷酸化 PPI 和酵母信号转导反应组数据的分析表明,单个激酶对相互作用蛋白的共磷酸化在细胞内是常见的。这些多组学分析阐明了广泛的细胞内磷酸化事件和物理蛋白质相互作用的多样性是如何相互影响的。