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. 2009 Jun 15;25(12):i259-67.
doi: 10.1093/bioinformatics/btp196.

Global alignment of protein-protein interaction networks by graph matching methods

Mikhail Zaslavskiy  1 , Francis Bach, Jean-Philippe Vert
Affiliations

Global alignment of protein-protein interaction networks by graph matching methods

Mikhail Zaslavskiy et al. Bioinformatics. .

Abstract

Motivation: Aligning protein-protein interaction (PPI) networks of different species has drawn a considerable interest recently. This problem is important to investigate evolutionary conserved pathways or protein complexes across species, and to help in the identification of functional orthologs through the detection of conserved interactions. It is, however, a difficult combinatorial problem, for which only heuristic methods have been proposed so far.

Results: We reformulate the PPI alignment as a graph matching problem, and investigate how state-of-the-art graph matching algorithms can be used for that purpose. We differentiate between two alignment problems, depending on whether strict constraints on protein matches are given, based on sequence similarity, or whether the goal is instead to find an optimal compromise between sequence similarity and interaction conservation in the alignment. We propose new methods for both cases, and assess their performance on the alignment of the yeast and fly PPI networks. The new methods consistently outperform state-of-the-art algorithms, retrieving in particular 78% more conserved interactions than IsoRank for a given level of sequence similarity.

Availability: All data and codes are freely and publicly available upon request.

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Figures

Fig. 1.
Fig. 1.
Inparanoid cluster network. Two clusters are connected if there exist at least one pair of proteins in one cluster, and one pair of proteins in the other cluster, which may produce a conserved interaction.
Fig. 2.
Fig. 2.
Inparanoid cluster networks. (a) The case of the benchmark data used in Bandyopadhyay et al. (2006). (b) The case of generalized interactions (1–4), see text.
Fig. 3.
Fig. 3.
Illustration of difference between MRF and MP alignment. Each box represents an Inparanoid cluster, white unfilled boxes represent clusters where MP and MRF assignments are the same. Red solid lines represent interactions conserved by MP alignment and not by MRF, black dotted lines represent interactions conserved by MRF and not by MP.
Fig. 4.
Fig. 4.
Algorithm performance comparison. Number of conserved interaction J(P) versus sequence similarity S(P).
See this image and copyright information in PMC

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