Przulj N, Wigle D A, Jurisica I
Department of Computer Science, University of Toronto, Toronto, M5S 3G4, Canada.
Bioinformatics. 2004 Feb 12;20(3):340-8. doi: 10.1093/bioinformatics/btg415.
The building blocks of biological networks are individual protein-protein interactions (PPIs). The cumulative PPI data set in Saccharomyces cerevisiae now exceeds 78 000. Studying the network of these interactions will provide valuable insight into the inner workings of cells.
We performed a systematic graph theory-based analysis of this PPI network to construct computational models for describing and predicting the properties of lethal mutations and proteins participating in genetic interactions, functional groups, protein complexes and signaling pathways. Our analysis suggests that lethal mutations are not only highly connected within the network, but they also satisfy an additional property: their removal causes a disruption in network structure. We also provide evidence for the existence of alternate paths that bypass viable proteins in PPI networks, while such paths do not exist for lethal mutations. In addition, we show that distinct functional classes of proteins have differing network properties. We also demonstrate a way to extract and iteratively predict protein complexes and signaling pathways. We evaluate the power of predictions by comparing them with a random model, and assess accuracy of predictions by analyzing their overlap with MIPS database.
Our models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways. This analysis uncovers structure-function relationships observable in a large PPI network.
生物网络的构建单元是个体蛋白质-蛋白质相互作用（PPI）。酿酒酵母中的累积PPI数据集现已超过78000个。研究这些相互作用的网络将为深入了解细胞的内部运作提供有价值的见解。
我们对该PPI网络进行了基于图论的系统分析，以构建计算模型，用于描述和预测致死突变以及参与遗传相互作用、功能组、蛋白质复合物和信号通路的蛋白质的特性。我们的分析表明，致死突变不仅在网络中高度连接，而且还满足一个额外的特性：它们的去除会导致网络结构的破坏。我们还提供了证据，证明在PPI网络中存在绕过可行蛋白质的替代路径，而致死突变不存在这样的路径。此外，我们表明不同功能类别的蛋白质具有不同的网络特性。我们还展示了一种提取和迭代预测蛋白质复合物和信号通路的方法。我们通过将预测结果与随机模型进行比较来评估预测能力，并通过分析它们与MIPS数据库的重叠来评估预测的准确性。
我们的模型提供了一种理解细胞功能背后复杂连接的方法，并使我们能够预测必要性、遗传相互作用、功能、蛋白质复合物和细胞通路。该分析揭示了在大型PPI网络中可观察到的结构-功能关系。
