Ho Yuen, Gruhler Albrecht, Heilbut Adrian, Bader Gary D, Moore Lynda, Adams Sally-Lin, Millar Anna, Taylor Paul, Bennett Keiryn, Boutilier Kelly, Yang Lingyun, Wolting Cheryl, Donaldson Ian, Schandorff Søren, Shewnarane Juanita, Vo Mai, Taggart Joanne, Goudreault Marilyn, Muskat Brenda, Alfarano Cris, Dewar Danielle, Lin Zhen, Michalickova Katerina, Willems Andrew R, Sassi Holly, Nielsen Peter A, Rasmussen Karina J, Andersen Jens R, Johansen Lene E, Hansen Lykke H, Jespersen Hans, Podtelejnikov Alexandre, Nielsen Eva, Crawford Janne, Poulsen Vibeke, Sørensen Birgitte D, Matthiesen Jesper, Hendrickson Ronald C, Gleeson Frank, Pawson Tony, Moran Michael F, Durocher Daniel, Mann Matthias, Hogue Christopher W V, Figeys Daniel, Tyers Mike
MDS Proteomics, 251 Attwell Drive, Toronto, Canada M9W 7H4, and Staermosegaardsvej 6, DK-5230 Odense M, Denmark.
Nature. 2002 Jan 10;415(6868):180-3. doi: 10.1038/415180a.
The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
近期大量的基因组序列数据使得系统蛋白质组学成为迫切需求,以破解那些决定细胞功能的编码蛋白质网络。到目前为止,大规模蛋白质 - 蛋白质相互作用图谱的生成依赖于酵母双杂交系统,该系统通过报告基因表达的激活来检测二元相互作用。随着超灵敏质谱蛋白质鉴定方法的出现,在全蛋白质组范围内直接鉴定蛋白质复合物成为可能。在此,我们以芽殖酵母酿酒酵母作为测试案例,报告这种方法的一个实例,我们将其称为高通量质谱蛋白质复合物鉴定(HMS - PCI)。以10%的预测酵母蛋白质作为诱饵,我们检测到3617个相关蛋白质,覆盖了酵母蛋白质组的25%。鉴定出了众多蛋白质复合物,包括各种信号通路和DNA损伤应答中的许多新相互作用。将HMS - PCI数据集与文献中报道的相互作用进行比较发现,与大规模双杂交研究相比,检测已知复合物的成功率平均高出三倍。鉴于本研究中观察到的高度连通性,即使是对包括人类在内的复杂蛋白质组进行部分HMS - PCI覆盖,也应该能够全面鉴定细胞网络。