Topology potential based seed-growth method to identify protein complexes on dynamic PPI data

Abstract

Protein complexes are very important for investigating the characteristics of biological processes. Identifying protein complexes from protein–protein interaction (PPI) networks is one of the recent research endeavors. The critical step of the seed-growth algorithms used for identifying protein complexes from PPI networks is to detect seed nodes (proteins) from which protein complexes are growing up in PPI networks. Topology potential was proposed to understand the evolution behavior and organizational principles of complex networks such as PPI networks. Furthermore, PPI networks are inherently dynamic in nature. In this study, we proposed a new seed-growing algorithm (called TP-WDPIN) for identifying protein complexes, which employs the concept of topology potential to detect significant proteins and mine protein complexes from Weighted Dynamic PPI Networks. To investigate the performance of the method, the TP-WDPIN algorithm was applied to four PPI databases and compared the obtained results to those produced by six other competing algorithms. Experimental results have demonstrated that the proposed TP-WDPIN algorithm exhibits better performance than other methods such as MCODE, MCL, CORE, CSO, ClusterONE, COACH when experimenting with four PPI databases (DIP, Krogan, MIPS, Gavin).