Estrada Ernesto
Complex Systems Research Group, X-Ray Unit, RIAIDT, University of Santiago de Compostela, Edificio CACTUS, Santiago de Compostela 15782, Spain.
Proteomics. 2006 Jan;6(1):35-40. doi: 10.1002/pmic.200500209.
Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organizational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the nonessential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins, and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.
大规模蛋白质-蛋白质相互作用网络（PINs）的拓扑分析对于理解单个蛋白质的组织和功能原理非常重要。已观察到蛋白质在PIN中的相互作用数量与其不可或缺性相关。必需蛋白质通常比非必需蛋白质具有更多的相互作用。我们在此表明，从酵母蛋白质组中去除一种蛋白质所导致的致死性与PIN中节点的不同中心性度量相关，例如一种蛋白质与许多其他蛋白质的接近程度、在其通信中需要特定蛋白质作为中介的蛋白质对数量，或者一种蛋白质在PIN中不同蛋白质簇中的参与情况。这些度量在识别PIN中的必需蛋白质方面明显优于随机选择。基于网络图谱谱性质的中心性度量，特别是子图中心性，在识别酵母PIN中的必需蛋白质方面表现最佳。子图中心性给出了关于单个蛋白质作用的重要结构信息，并通过识别PIN中的必需蛋白质允许选择合理药物发现的可能靶点。
