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Aligning protein-protein interaction networks from different species is a useful mechanism for figuring out orthologous proteins, predicting/verifying protein unknown functions or constructing evolutionary relationships. The network alignment problem is proved to be
NP-hard, requiring exponential-time algorithms, which is not feasible for the fast growth of biological data. In this paper, we present a novel protein-protein interaction global network alignment algorithm, which is enhanced with an extended large neighborhood search heuristics. Evaluated on benchmark datasets of yeast, fly, human and worm, the proposed algorithm outperforms state-of-the-art. Furthermore, the complexity of ours is polynomial, thus being scalable to large biological networks in practice.
Heuristic, Protein-protein interaction networks, network alignment, neighborhood search
 R.L. Finley, R. Brent, Interaction mating reveals binary and ternary connections between drosophila cell cycle regulators. Proc. Natl Acad. Sci. USA. 91 (1994) 12980-12984.
 R. Aebersold, M. Mann, Mass spectrometry-based proteomics, Nature. 422 (2003) 198-207.
 C.S. Goh, F.R. Cohen, Co-evolutionary analysis reveals insights into protein-protein interactions, J. Mol. Biol. 324 (2002) 177-192.
 J.D. Han et al, Evidence for dynamically organized modularity in the yeast proteinprotein interaction network, Nature. 430 (2004) 88-93.
 G.D. Bader, C.W. Hogue, Analyzing yeast protein-protein interaction data obtained from different sources, Nat. Biotechnol. 20 (2002) 991-997.
 H.B. Hunter et al, Evolutionary rate in the protein interaction network, Science. 296 (2002) 750-752.
 J. Dutkowski, J. Tiuryn,J, Identification of functional modules from conserved ancestral protein-protein interactions, Bioinformatics. 23 (2007) i149-i158.
 B.P. Kelley et al, Conserved pathways within bacteria and yeast as revealed by global protein network alignment, Proc. Natl Acad. Sci. USA. 100 (2003) 11394-11399.
 O. Kuchaiev, N. PrzË‡ ulj, Integrative network alignment reveals large regions of global network similarity in yeast and human, Bioinformatics. 27 (2011) 1390-1396.
 M. Remm et al, Automatic clustering of orthologs and in-paralogs from pairwise species comparisons, J. Mol. Biol. 314 (2001) 1041-1052.
 L. Chindelevitch et al, Local optimization for global alignment of protein interaction networks, In: Pacific Symposium on Biocomputing, Hawaii, USA, 2010, pp. 123-132.
 E. hmet, AladaÄŸ, Cesim Erten, SPINAL: scalable protein interaction network alignment, Bioinformatics. Volume 29(7) (2013) 917-924. https://doi.org/10.1093/bioinformatics/btt071.
 B.P. Kelley et al, Pathblast: a tool for alignment of protein interaction networks, Nucleic Acids Res. 32 (2004) 83-88.
 R. Sharan et al, Conserved patterns of protein interaction in multiple species, Proc. Natl Acad. Sci. USA. 102 (2005) 1974-1979.
 M. Koyuturk et al, Pairwise alignment of protein interaction networks, J. Comput. Biol. 13 (2006) 182-199.
 M. Narayanan, R.M. Karp, Comparing protein interaction networks via a graph match-and-split algorithm, J. Comput. Biol. 14 (2007) 892-907.
 J. Flannick et al, Graemlin: general and robust alignment of multiple large interaction networks, Genome Res. 16 (2006) 1169-1181.
 R. Singh et al, Global alignment of multiple protein interaction networks. In: Pacific Symposium on Biocomputing, 2008, pp. 303-314.
 M. Zaslavskiy et al, Global alignment of protein-protein interaction networks by graph matching methods, Bioinformatics. 25 (2009) 259-267.
 L. Chindelevitch, Extracting information from biological networks. PhD Thesis, Department of Mathematics, Massachusetts Institute of Technology, Cambridge, 2010.
 Do Duc Dong et al, An efficient algorithm for global alignment of protein-protein interaction networks, Proceeding of ATC15, 2015, pp. 332-336.
 S. Ropke, D. Pisinger, An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows. Transportation Science. 40 (2006) 455-472. https:// doi.org/10.1287/trsc.1050.0135.
 P. Shaw, A new local search algorithm providing high quality solutions to vehicle routing problems, Technical report, Department of Computer Science, University of Strathclyde, Scotland, 1997.
 Roman Lutz, Adaptive Large Neighborhood Search, Bachelor thesis, Ulm University, 2014.
 M.A. Trick, A linear relaxation heuristic for the generalized assignment prob-lem, Naval Research Logistics. 39 (1992) 137-151.
 J.Y. Potvin, M. Rousseau, Parallel Route Building Algorithm for the Vehicle Routing and Scheduling Problem with Time Windows, European Journal of Operational Research. 66(3) (1993) pp. 331-340.
 J.M. Peter, Van Laarhoven, H.L. Emile, Aarts. Simulated annealing. Springer, 1987.