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The function of a protein is defined by its interaction partners. Thus,topology-driven network alignment of the protein-protein interaction (PPI)networks of two species should uncover similar interaction patterns and allowidentification of functionally similar proteins. Howver, few of the fifty ormore algorithms for PPI network alignment have demonstrated a significant linkbetween network topology and functional similarity, and none have recoveredorthologs using network topology alone. We find that the major contributingfactors to this failure are: (i) edge densities in current PPI networks are toolow to expect topological network alignment to succeed; (ii) when edgedensities are high enough, some measures of topological similarity easilyuncover functionally similar proteins while others do not; and (iii) mostnetwork alignment algorithms fail to optimize their own topological objectivefunctions, hampering their ability to use topology effectively. We demonstratethat SANA-the Simulated Annealing Network Aligner-significantly outperformsexisting aligners at optimizing their own objective functions, even achievingnear-optimal solutions when optimal solution is known. We offer the firstdemonstration of global network alignments based on topology alone that alignfunctionally similar proteins with p-values in some cases below 1e-300. Wepredict that topological network alignment has a bright future as edgedensities increase towards the value where good alignments become possible. Wedemonstrate that when enough common topology is present at high enough edgedensities-for example in the recent, partly synthetic networks of theIntegrated Interaction Database-topological network alignment easily recoversmost orthologs, paving the way towards high-throughput functional predictionbased on topology-driven network alignment.

On the current failure -- but bright future -- of topology-driven biological network alignment