Detection of protein complex from protein-protein interaction network using Markov clustering

International Symposium on Bioinformatics, Chemometrics and Metabolomics

生物科学;化学

Ochieng, P.J.^1,2 ; Kusuma, W.A.^1,2,3 ; Haryanto, T.^1,3

Department of Computer Science, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University Dramaga, Bogor

16680, Indonesia^1

Kenyatta National Hospital, P O BOX 20723-00202, Upper hill, Nairobi, Kenya^2

TropicalBiopharmaca Research Center, Bogor Agricultural University, Jl. Taman Kencana No. 3, Bogor

16128, Indonesia^3

关键词: Biological networks;    Clustering coefficient;    Comparison analysis;    Geometrical networks;    Graph clustering algorithms;    Protein-protein interaction networks;    Topological properties;    Type ii diabetes mellitus;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/835/1/012001/pdf
DOI  :  10.1088/1742-6596/835/1/012001

学科分类：生物科学（综合）

来源: IOP

【 摘 要 】

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.