Mining the maximum conserved functional module from the alignment of pairwise protein interaction networks

The study of Bioinformatics has been growing rapidly over the past few years, it is mainly focused on DNAs and proteins. Proteins are the main body of performing complex physiological functions of organisms, and protein-protein interactions are the basis of maintaining the structure of cells and realizing functions, so studying protein interaction networks is of great importance. By aligning and analyzing the protein interaction networks from different species, we can predict proteins´ functions, mine conserved functional modules and so on. To this end, this paper presents a method of mining the maximum conserved functional module from the alignment of pairwise protein interaction networks. In this method, the protein interaction networks are abstracted as graphical models. Considering that the alignment of networks may result in combinatorial explosion and other issues, this paper introduces a scoring system and a pruning strategy, and thus the complexity of the algorithm is reduced. In order to verify the effectiveness and robustness of our method, we experiment with the S. cerevisiae and D. melanogaster protein-protein interaction networks, and the experimental results show that our method is effective.