Graph Partitioning Method for Functional Module Detections of Protein Interaction Network

Study on topology structure of protein interaction network has been suggested as a potential effort to discover biological functions and cellular mechanisms at systems level. In this work, we introduced a graph partitioning method to partition protein interaction network into several clusters of interacting proteins that share similar functions called functional modules. Our proposed method encompasses three major steps which are preprocessing, informative proteins selection and graph partitioning algorithm. We utilized the protein-protein interaction dataset from MIPS to test the proposed method. We use Gene Ontology information to validate the biological significance of the detected modules. We also downloaded protein complex information to evaluate the performance of our method. In our analysis, the method showed high accuracy performance indicates that this method capable to detect highly significance modules. Hence, this showed that functional modules detected by the proposed method are biologically significant which can be used to predict uncharacterized proteins and infer new complexes.

[1]  See-Kiong Ng,et al.  Discovering protein complexes in dense reliable neighborhoods of protein interaction networks. , 2007, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.

[2]  Adam J. Smith,et al.  The Database of Interacting Proteins: 2004 update , 2004, Nucleic Acids Res..

[3]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[4]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[5]  Shigehiko Kanaya,et al.  Development and implementation of an algorithm for detection of protein complexes in large interaction networks , 2006, BMC Bioinformatics.

[6]  Martin C. Frith,et al.  SeqVISTA: a graphical tool for sequence feature visualization and comparison , 2003, BMC Bioinformatics.

[7]  Luonan Chen,et al.  Discovering functions and revealing mechanisms at molecular level from biological networks , 2007, Proteomics.

[8]  Zhao Cai,et al.  An Improved Method Based on Maximal Clique for Predicting Interactions in Protein Interaction Networks , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[9]  Shi-Hua Zhang,et al.  A Graph-Theoretic Method for Mining Functional Modules in Large Sparse Protein Interaction Networks , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[10]  Ioannis Xenarios,et al.  DIP: The Database of Interacting Proteins: 2001 update , 2001, Nucleic Acids Res..

[11]  Jianer Chen,et al.  Greedily Mining l-dense Subgraphs in Protein Interaction Networks , 2008, 2008 The 9th International Conference for Young Computer Scientists.

[12]  Gary D. Bader,et al.  An automated method for finding molecular complexes in large protein interaction networks , 2003, BMC Bioinformatics.

[13]  See-Kiong Ng,et al.  Interaction graph mining for protein complexes using local clique merging. , 2005, Genome informatics. International Conference on Genome Informatics.

[14]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[15]  Limsoon Wong,et al.  Using Indirect protein-protein Interactions for protein Complex Prediction , 2008, J. Bioinform. Comput. Biol..

[16]  Igor Jurisica,et al.  Protein complex prediction via cost-based clustering , 2004, Bioinform..

[17]  R. Sharan,et al.  Network-based prediction of protein function , 2007, Molecular systems biology.

[18]  Dmitrij Frishman,et al.  MIPS: analysis and annotation of genome information in 2007 , 2007, Nucleic Acids Res..

[19]  L. Mirny,et al.  Protein complexes and functional modules in molecular networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[20]  T. Vicsek,et al.  Clique percolation in random networks. , 2005, Physical review letters.

[21]  Martin Kuiper,et al.  BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks , 2005, Bioinform..