Semantic mapping to align PPI networks and predict conserved protein complexes

PPI networks are significant resources to determine molecular organizations in a cell. The availability of genome-wide PPI networks on diverse model species have provided a new paradigm to identify evolutionarily conserved substructures. Computational methods for cross-species comparison of PPI networks have recently been applied to prediction of conserved protein complexes. These methods use network alignment techniques by mapping homologous proteins. We propose a novel network alignment approach by semantic mapping between proteins from different species. We apply this approach to predict conserved human protein complexes by aligning yeast PPI networks representing well-studied protein complexes with a human PPI network in a genomic scale. In our experiments, we used a recently proposed integrative semantic similarity measure, simICND, for semantic mapping. The experimental results show that the proposed network alignment approach has higher accuracy on predicting human protein complexes than other clustering-based methods. The experimental results also show that the proposed approach has higher efficiency than previous network alignment algorithms. This study provides a valuable framework to discover conserved systems from a functional standpoint.