Exploiting locational and topological overlap model to identify modules in protein interaction networks

Lixin Cheng,Kwong-Sak Leung,Pengfei Liu,Dong Wang

doi:10.1186/s12859-019-2598-7

Abstract

BackgroundClustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells.ResultsIn this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL.ConclusionTaking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks.

Highlights

Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules
Since all the information in the HPRD database has been manually extracted from the literature by expert biologists and it is frequently regarded as the reference of interactions, the proportion of co-localized protein pairs in HPRD is expected to be larger than the other protein-protein interaction (PPI) datasets
We found that cancer proteins are more likely to be involved in the extended-network inferred modules, Topological Overlap PIN (TOPIN) and Locational and Topological Overlap Protein Interaction Network (PIN) (LTOPIN), when compared with the modules generated from other networks, global protein interaction network (GPIN) and co-localization protein interaction networks (CLPIN)

Summary

Introduction

Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. Protein Interaction Network (PIN) is the most common biological networks where the cellular components are proteins [1, 6]. Interacting protein pairs often participate in the same biological processes or associate with specific molecular functions. In system biology, clustering PIN is a typical and effective operation to predict protein complexes or functional modules, where a module is a cluster of densely connected proteins in a PIN. The detection of modules using biological networks can help in understanding the mechanisms regulating cell life and predicting the biological functions of the uncharacterized proteins [7,8,9,10,11]

Methods

Results

Conclusion