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Protein–protein interaction (PPI) networks are emerging as valuable prototypes to study important problems in molecular cellular biology and systems biomedicine. An analysis of the topological properties of a PPI network is very helpful for understanding the function and structure of networks. In this study, we analyzed the topological patterns in the BioPlex network containing interactions among 10,961 proteins; most interactions were previously undocumented. The BioPlex network is a comprehensive map of human protein interactions and represents the first phase of a long-term effort to profile the entire human ORFEOME collection. Similar to other biological networks, we observed that the BioPlex network has several topological properties. We also quantified correlations profiles for the BioPlex network and compared them to randomized versions of the same network. We found that for the BioPlex network, edges between proteins with intermediate degrees were strongly suppressed, whereas edges between low-connected proteins were favored. Finally, the degrees of essential genes were compared with the degrees of non-essential genes and randomly selected proteins. There were no significant differences between the groups. [ABSTRACT FROM AUTHOR]

Abstract: Proteins do not exert their function in isolation of one another, but interact together in protein–protein interaction (PPI) networks. Analysis of topological properties of proteins in the PPI network is very helpful to understand the function of proteins. However, until recently, no one has ever undertaken to investigate toxin targets by topological properties. In this study, for the first time, 12 topological properties are used to investigate the characteristics of toxin targets in the PPI network. Most of the topological properties are found to be statistically discriminative between toxin targets and other proteins, and toxin targets tend to play more important roles in the PPI network. In addition, based on the topological properties and the sequence information, support vector machine (SVM) is used to predict toxin targets. The results obtained by the jackknife test and 10-fold cross validation are encouraging, indicating that SVM is a useful tool for predicting toxin targets, or at least can play complementary roles in relevant areas. [Copyright &y& Elsevier]