Identifying protein complexes from interaction networks based on clique percolation and distance restriction

Jianxin Wang,Yi Pan,Min Li,Binbin Liu

doi:10.1186/1471-2164-11-s2-s10

Jianxin Wang, Yi Pan + Show 2 more

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https://doi.org/10.1186/1471-2164-11-s2-s10

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Abstract

BackgroundIdentification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Each protein might be subordinate multiple protein complexes in the real protein-protein interaction networks. Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic.ResultAs an effective algorithm in identifying overlapping module structures, clique percolation method (CPM) has a wide range of application in social networks and biological networks. However, the recognition accuracy of algorithm CPM is lowly. Furthermore, algorithm CPM is unfit to identifying protein complexes with meso-scale when it applied in protein-protein interaction networks. In this paper, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction, and develop a novel algorithm called CP-DR based on the new topological model for identifying protein complexes. In this new algorithm, the protein complex size is restricted by distance constraint to conquer the shortcomings of algorithm CPM. The algorithm CP-DR is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes.ConclusionThe proposed algorithm CP-DR based on clique percolation and distance restriction makes it possible to identify dense subgraphs in protein interaction networks, a large number of which correspond to known protein complexes. Compared to algorithm CPM, algorithm CP-DR has more outstanding performance.

Highlights

Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era
In order to conquest these shortcomings, an algorithm called CP-DR (Clique Percolation Method based on Distance Restriction) for identifying protein complexes based on clique percolation and distance restriction is proposed in this paper
According to the detailed depiction in characteristics of our new topological model, we propose a novel algorithm called CP-DR (Clique Percolation Method based on Distance Restriction) for identifying protein complexes based on clique percolation and distance restriction

Summary

Introduction

Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. One of the most important challenges is to systematically analyze and comprehensively understand how the proteins accomplish the life activities by interacting with each other [1]. It plays an important role in predicting the protein functions and understanding specific biological processes that identify. The basic idea of Non-overlapping Clustering Algorithms is that each protein belongs to one and only one protein complex in large-scale protein-protein interaction network. The researches on identification algorithm in mining overlapping protein complexes are more significance [9]

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