Protein interaction networks as metric spaces: a novel perspective on distribution of hubs

BMC Systems Biology

Eric C Mwambene²  Junaid Gamieldien¹  Emad Fadhal¹ 

[1] South African National Bioinformatics Institute, SA Medical Research Council Bioinformatics Unit, University of the Western Cape, Bellville 7535, South Africa;Department of Mathematics and Applied Mathematics, University of the Western Cape, P/Bag X17, Bellville, South Africa

关键词: Power-law graphs;    Essential proteins;    Hubs;    Topological centrality;    Core-periphery structure;    Metric spaces;    Protein interaction networks;   

Others  :  1091232
DOI  :  10.1186/1752-0509-8-6

 received in 2013-02-18, accepted in 2014-01-07,  发布年份 2014

【 摘 要 】

Background

In the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located.

Results

By analysing how the human functional protein interaction network, the human signalling network, Saccharomyces cerevisiae, Arabidopsis thaliana and Escherichia coli protein-protein interaction networks from various databases are distributed as metric spaces, we found that proteins interact radially through a central node, high degree proteins coagulate in the centre of the network, and those far away from the centre have low degree. We further found that the distribution of proteins from the centre is in some hierarchy of importance and has biological significance.

Conclusions

We conclude that structurally, protein interaction networks are mathematical entities that share properties between organisms but not necessarily with other networks that follow power-law. We therefore conclude that (i) if there are hubs defined by degree, they are not distributed randomly; (ii) zones closest to the centre of the network are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping’ functions; (iii) proteins closest to the network centre are functionally less dispensable and may present good targets for therapy development; and (iv) network biology requires its own network theory modelled on actual biological evidence and that simply adopting theories from the social sciences may be misleading.

【 授权许可】

   
2014 Fadhal et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Kar G, Gursoy A, Keskin O: Human cancer protein-protein interaction network: a structural perspective. PLoS Comput Biol 2009, 5(12):e1000601.
• [2]Koyutürk M, Szpankowski W, Grama A: Assessing significance of connectivity and conservation in protein interaction networks. J Comput Biol 2007, 14(6):747-764.
• [3]Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Rev Genet 2004, 5(2):101-113. [ http://www.ncbi.nlm.nih.gov/pubmed/14735121 webcite]
• [4]He X, Zhang J: Why do hubs tend to be essential in protein networks? PLoS Genet 2006, 2(6):e88.
• [5]Jeong H, Mason S, Barabasi A, Oltvai Z: Lethality and centrality in protein networks. Arxiv preprint cond-mat/0105306 2001.
• [6]Vallabhajosyula R, Chakravarti D, Lutfeali S, Ray A, Raval A: Identifying hubs in protein interaction networks. PLoS One 2009, 4(4):e5344.
• [7]Batada N, Hurst L, Tyers M: Evolutionary and physiological importance of hub proteins. PLoS Comput Biol 2006, 2(7):e88.
• [8]Hahn MW, Kern AD: Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol 2005, 22(4):803-806. [ http://www.ncbi.nlm.nih.gov/pubmed/15616139 webcite]
• [9]Yu H, Greenbaum D, Xin Lu H, Zhu X, Gerstein M: Genomic analysis of essentiality within protein networks. Trends Genet 2004, 20(6):227-231. [ http://www.ncbi.nlm.nih.gov/pubmed/15145574 webcite]
• [10]Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M: The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol 2007, 3(4):8. [ http://www.ncbi.nlm.nih.gov/pubmed/17447836 webcite]
• [11]Zaki N, Berengueres J, Efimov D: Detection of protein complexes using a protein ranking algorithm. Proteins 2012, 80(10):2459-2468. [ http://www.ncbi.nlm.nih.gov/pubmed/22685080 webcite]
• [12]Zaki N, Efimov D, Berengueres J: Protein complex detection using interaction reliability assessment and weighted clustering coefficient. BMC Bioinformatics 2013, 14:163. [ http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3680028&tool=pmcentrez&rendertype=abstract webcite] BioMed Central Full Text
• [13]Mete M, Tang F, Xu X, Yuruk N: A structural approach for finding functional modules from large biological networks. BMC Bioinformatics 2008, 9(Suppl 9):S19. [ http://www.ncbi.nlm.nih.gov/pubmed/18793464 webcite] BioMed Central Full Text
• [14]Everett M, Borgatti S: The centrality of groups and classes. J Math Sociol 1999, 23(3):181-201.
• [15]Newman MEJ: A measure of betweenness centrality based on random walks. 2003. [ http://arxiv.org/abs/cond-mat/0309045 webcite] [Comment: 15 pages, 7 figures, 2 tables]
• [16]Friedel CC, Zimmer R: Inferring topology from clustering coefficients in protein-protein interaction networks. BMC Bioinformatics 2006, 7:519. [ http://www.ncbi.nlm.nih.gov/pubmed/17137490 webcite] BioMed Central Full Text
• [17]Silva Md, Ma HMH, Zeng APZAP: Centrality, network capacity, and modularity as parameters to analyze the core-periphery structure in metabolic networks. Proc IEEE 2008, 96(8):1411-1420.
• [18]Ignacio AJ, DallAsta L, Barrat A, Vespignani A: k-core decomposition : a tool for the visualization of large scale networks. World Wide Web Internet And Web Info Syst 2005. abs/cs/050. [ http://arxiv.org/abs/cs/0504107 webcite]
• [19]Vogelstein B, Lane D, Levine AJ: Surfing the p53 network. Nature 2000, 408(6810):307-310.
• [20]Jeong H, Barab AL, Oltvai ZN: Prediction of protein essentiality based on genomic data. Complexus 2003, 1:19-28.
• [21]Lin CC, Juan HF, Hsiang JT, Hwang YC, Mori H, Huang HC: Essential core of protein-protein interaction network in Escherichia coli. J Proteome Res 2009, 8(4):1925-1931.
• [22]Katzir Y, Elhanati Y, Averbukh I, Braun E: Dynamics of the cell-cycle network under genome-rewiring perturbations. Phys Biol 2013, 10(6):066001. [ http://iopscience.iop.org/1478-3975/10/6/066001/article/ webcite]
• [23]Zhuge H, Zhang J: Topological Centrality and its e-Science Applications. J Am Soc Inf Sci Technol 2010, 61(9):1824-1841.
• [24]Mirzarezaee M, Araabi BN, Sadeghi M: Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae. BMC Syst Biol 2010, 4:172. BioMed Central Full Text
• [25]Patil A, Kinoshita K, Nakamura H: Hub promiscuity in protein-protein interaction networks. Int J Mol Sci 2010, 11(4):1930-1943. [ http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2871146&tool=pmcentrez&rendertype=abstract webcite]
• [26]Bales ME, Johnson SB: Graph theoretic modeling of large-scale semantic networks. J Biomed Inform 2006, 39(4):451-464. [ http://www.ncbi.nlm.nih.gov/pubmed/16442849 webcite]
• [27]Albert R, Barabasi AL: Statistical mechanics of complex networks. Rev Modern Phys 2002, 74:47. [ http://www.springer.com/physics/theoretical,+mathematical+&+computational+physics/book/978-3-540-40372-2 webcite]
• [28]Watts DJ, Strogatz SH: Collective dynamics of 'small-world’ networks. Nature 1998, 393(6684):440-442. [ http://www.ncbi.nlm.nih.gov/pubmed/9623998 webcite]
• [29]Bryant V: Metric Spaces: Iteration and Application. Cambridge: Press Syndicate of the University of Cambridge; 1985.
• [30]Dugundji J: Topology. Boston: Allyn and Bacon; 1974.
• [31]Bandyopadhyay S, Chiang CY, Srivastava J, Gersten M, White S, Bell R, Kurschner C, Martin CH, Smoot M, Sahasrabudhe S, Barber DL, Chanda SK, Ideker T: A human MAP kinase interactome. Nat Methods 2010, 7(10):801-805. [ http://www.ncbi.nlm.nih.gov/pubmed/20936779 webcite]
• [32]Eisenberg E, Levanon EY: Preferential attachment in the protein network evolution. Phys Rev Lett 2003, 91(13):138701. [ http://arxiv.org/abs/cond-mat/0303490 webcite]
• [33]Gibson TA, Goldberg DS: Improving evolutionary models of protein interaction networks. Bioinformatics 2011, 27(3):376-382. [ http://www.ncbi.nlm.nih.gov/pubmed/21067999 webcite]
• [34]Aiello W, Chung F, Lu L: A random graph model for power law graphs. Exp Math 2001, 10:53-66. [ http://www.emis.ams.org/journals/EM/restricted/10/10.1/aiello.ps webcite]
• [35]Luo F, Li B, Wan XF, Scheuermann RH: Core and periphery structures in protein interaction networks. BMC Bioinformatics 2009, 10(Suppl 4):S8. [ http://www.ncbi.nlm.nih.gov/pubmed/19426456 webcite] BioMed Central Full Text
• [36]Wu G, Feng X, Stein L: A human functional protein interaction network and its application to cancer data analysis. Genome Biol 2010, 11(5):R53. BioMed Central Full Text
• [37]Brandão MM, Dantas LL, Silva-Filho MC: AtPIN: Arabidopsis thaliana protein interaction network. BMC Bioinformatics 2009, 10:454. [ http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2810305&tool=pmcentrez&rendertype=abstract webcite] BioMed Central Full Text
• [38]Chakrabarti D, Zhan Y, Faloutsos C: R-MAT: A recursive model for graph mining. Computer 2004, 6:442-446. [ http://repository.cmu.edu/cgi/viewcontent.cgi?article=1541&context=compsci webcite]