Algorithms for global protein–protein interaction network alignment

Abstract

Protein–protein interaction creates a complex relation among molecular organs. The pivotal functionalities among cells depend on these interactions. In the light of computational biology (CB), it is possible to retrieve the exact information regarding these relationships. The global alignments among proteins are more important. Toward the development of the PPI network analysis, various methods such as heuristics, evolutionary, probabilistic, semi-probabilistic, spectral graph analysis and mapping methods have been developed and this is an ongoing process of progress. Some remarkable contributions to the PPI global network alignments are Common neighbors-based global GRAph (C-GRAAL), GRAph ALigner (GRAAL), Hungarian algorithm-based GRAAL (H-GRAAL), Matching-based GRAph aligner (M-GRAAL), IsoRankN, IsoRank, Scalable Global alignment algorithm, SMETENA, (software package) and GraphCrunch 2 (software package). All the mentioned algorithms and software package mentioned above have tried to address the best illustration and mapping between protein networks for global protein network alignment. For simplicity we avoid local sequence alignment methods and algorithms. For experimental data analysis, five eukaryotic species such as C. elegans (CE), D. melanogaster (DM), S. cerevisiae (SC), H. sapiens (HS) and M. musculus (MM) have been considered.