With ever increasing amount of available data on protein-protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Recently proposed algorithms for aligning PPI networks target simplified structures such as conserved pathways to render these problems computationally tractable. However, since conserved structures that are parts of functional modules and protein complexes generally correspond to dense subnets, algorithms that are able to extract conserved patterns in terms of general graphs are necessary. With this motivation, we focus on discovering protein sets that induce subnets that are highly conserved in the interactome of a pair of species. For this purpose, we develop a framework that formally defines the pairwise local alignment problem for PPI networks, model the problem as a graph optimization problem, and present fast algorithms for this problem. In order to capture the underlying biological processes accurately, we base our framework on duplication/divergence models that focus on understanding the evolution of PPI networks. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, both in terms of accuracies and computational cost.
The structure correspondence matching is similar to the graph alignment problem in the bioinformatics field for the alignment of protein-protein interactions networks REF .
Pairwise alignment of protein interaction networks
Journal of Computational Biology
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