A novel topological centrality measure capturing biologically important proteins

• Published in: Molecular bioSystems Vol. 12; no. 2; pp. 666 - 673
• Main Authors: Karabekmez, Muhammed Erkan, Kirdar, Betul
• Format: Journal Article
• Language: English
• Published: England 01.01.2016
• Subjects: Area Under Curve; Cluster Analysis; Gene Ontology; Genetic Predisposition to Disease; Humans; Models, Genetic; Protein Interaction Maps; ROC Curve
• ISSN: 1742-206X; 1742-2051; 1742-2051
• DOI: 10.1039/c5mb00732a

Topological centrality in protein interaction networks and its biological implications have widely been investigated in the past. In the present study, a novel metric of centrality-weighted sum of loads eigenvector centrality (WSL-EC)-based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein-protein interaction network. The metric can capture nodes from peripherals of the network differently from conventional eigenvector centrality. Different metrics were found to selectively identify hub sets that are significantly associated with different biological processes. The widely accepted metrics degree centrality, betweenness centrality, subgraph centrality and eigenvector centrality are subject to a bias towards super-hubs, whereas WSL-EC is not affected by the presence of super-hubs. WSL-EC outperforms other metrics of centrality in detecting biologically central nodes such as pathogen-interacting, cancer, ageing, HIV-1 or disease-related proteins and proteins involved in immune system processes and autoimmune diseases in the human interactome. In the present study, a novel metric of centrality-weighted sum of loads eigenvector centrality (WSL-EC)-based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein-protein interaction network.