Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks

• Published in: BMC bioinformatics Vol. 20; no. Suppl 3; pp. 131 - 74
• Main Authors: Lei, Xiujuan, Fang, Ming, Guo, Ling, Wu, Fang-Xiang
• Format: Journal Article
• Language: English
• Published: London BioMed Central 29.03.2019; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Acids; Algorithms; Bioinformatics; Biomedical and Life Sciences; Cluster analysis; Computational biology; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer simulation; Cores; Dynamic protein-protein interaction (PPI) network; Dynamical systems; Essential protein; Flower pollination algorithm; Gene expression; Identification; Life Sciences; Metabolism; Methods; Microarrays; Military bases; Networks; Novels; Pollen; Pollination; Protein complex; Protein interaction; Protein research; Protein-protein interactions; Proteins; Research methodology; Yeast; China; Protein complex; Dynamic protein-protein interaction (PPI) network; Essential protein; Flower pollination algorithm
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-019-2649-0

Background Detecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems. Methods In this study, we present a novel algorithm, named Improved Flower Pollination Algorithm (IFPA), to identify protein complexes in multi-relation reconstructed dynamic PPI networks. Specifically, we first introduce a concept called co-essentiality, which considers the protein essentiality to search essential interactions, Then, we devise the multi-relation reconstructed dynamic PPI networks (MRDPNs) and discover the potential cores of protein complexes in MRDPNs. Finally, an IFPA algorithm is put forward based on the flower pollination mechanism to generate protein complexes by simulating the process of pollen find the optimal pollination plants, namely, attach the peripheries to the corresponding cores. Results The experimental results on three different datasets (DIP, MIPS and Krogan) show that our IFPA algorithm is more superior to some representative methods in the prediction of protein complexes. Conclusions Our proposed IFPA algorithm is powerful in protein complex detection by building multi-relation reconstructed dynamic protein networks and using improved flower pollination algorithm. The experimental results indicate that our IFPA algorithm can obtain better performance than other methods.