Asymmetric Global Alignment of Protein-Protein Interaction Graph Databases (Extended Abstract)

In the last few years a large amount of protein interaction data has been collected and stored in public databases. The automatic analysis and management of such data can provide valuable information on the evolution of different organisms. To this aim, interaction data can be modelled as graphs where nodes represent cellular components and edges are associated to interactions. Such graphs are called protein-protein interaction (PPI) networks. A number of methods have been proposed to perform PPI-network alignment. Such methods operate symmetrically, that is to say, they do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed distinguishable on the basis of how well the corresponding organism is biologically well-characterized. We propose a method for global alignment of PPI networks that exploits differences in the characterization of organisms at hand. We assume that the PPI network (called Master) of the best characterized is used as a fingerprint to guide the alignment process to the second input network (called Slave), so that generated results preferably retain the structural characteristics of the Master (and using the Slave) network. We tested our method showing that the results it returns are biologically relevant.