Moth–flame optimization-based algorithm with synthetic dynamic PPI networks for discovering protein complexes

Abstract

The prediction of protein complex in protein–protein interaction (PPI) networks plays such a crucial role in the understanding of biological processes. This paper presents a moth–flame optimization-based protein complex prediction algorithm, called MFOC. First of all, we build the reliable weighted dynamic PPI networks by synthesizing topological and biological information. After that, we utilize a layer-by-layer scheme to find the cores of protein complexes as the flames and let the moths fly spirally around the flames to form the complexes. To be specific, the critical proteins have priority as the hearts and cores are extended by the hearts. And then we use MFOC algorithm to make the moths converge to the flames in order to obtain the protein complexes. At last, a two-step filtration operation is executed to refine the predicted protein complexes. The proposed algorithm MFOC is applied to the reliable weighted dynamic protein interaction networks including DIP, Krogan and MIPS, and the numerous comparison results show that MFOC outperforms other classic algorithms for identifying protein complexes.