A Review on Graph Analytics-Based Approaches in Protein-Protein Interaction Network

  • D. NarmadhaEmail author
  • A. Pravin
  • G. Naveen Sundar
  • Premnath Dhanaraj
Conference paper


Essential proteins play a vital role in the biological and cellular activity of a living organism. Identification of essential proteins is crucial for understanding the cellular life mechanisms for medical treatments and disease diagnosis. The existing computational measures are primarily based on identifying dense sub-graphs from the protein interaction network. In this research paper, the existing computational, graph theoretic approaches are reviewed and a novel research direction to find essential proteins is proposed.


Protein-protein interaction Centrality measure Graph theory Essential proteins Drug discovery Computational methods Knowledge discovery Unsupervised and supervised methodologies 



Protein-protein interaction


Ribonucleic acid


The Biological General Repository for Interaction Datasets


Protein-protein interaction database for maize


Database of Interacting Proteins


The Saccharomyces Genome Database


Munich Information Center for Protein Sequences


  1. 1.
    Pál C, Papp B, Hurst LD (2003) Genomic function (communication arising): rate of evolution and gene dispensability. Nature 421:496CrossRefGoogle Scholar
  2. 2.
    He X, Zhang J (2006) Why do hubs tend to be essential in protein networks? PLoS Gen 2:e88CrossRefGoogle Scholar
  3. 3.
    Fuentes G et al (2011) Role of protein flexibility in the discovery of new drugs. Drug Dev Res 72:26–35CrossRefGoogle Scholar
  4. 4.
    Clatworthy AE, Pierson E, Hung DT (2007) Targeting virulence: a new paradigm for antimicrobial therapy. Nat Chem Biol. 3:541CrossRefGoogle Scholar
  5. 5.
    Roemer T, Jiang B, Davison J, Ketela T, Veillette K, Breton A, Tandia F, Linteau A, Sillaots S, Marta C (2003) Large-scale essential gene identification in candida albicans and applications to antifungal drug discovery. Mol Microbiol 50:167–181CrossRefGoogle Scholar
  6. 6.
    Xu Z, Zikos D, Osterrieder N, KarstenTischer B (2014) Generation of a complete single-gene knockout bacterial artificial chromosome library of cowpox virus and identification of its essential genes. J Virol 88:490–502CrossRefGoogle Scholar
  7. 7.
    Walia RR, Caragea C, Lewis BA, Towfic F, Terribilini M, El-Manzalawy Y, Dobbs D, Honavar V (2012) Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art. BMC Bioinform 13:89CrossRefGoogle Scholar
  8. 8.
    Qin C, Sun Y, Dong Y (2016) A new method for identifying essential proteins based on network topology properties and protein complexes. PLoS One 11:e0161042CrossRefGoogle Scholar
  9. 9.
    Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, Margalit H (2004) Int Act: an open source molecular interaction database. Nuc Acid Res 32(Suppl_1):D452–D455CrossRefGoogle Scholar
  10. 10.
    Stark C, Breitkreutz B-J, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) Bio GRID: a general repository for interaction datasets. Nuc Acid Res 34(Database issue):D535–D539CrossRefGoogle Scholar
  11. 11.
    Chatr-aryamontri A, Ceol A, Palazzi LM, Nardelli G, Schneider MV, Castagnoli L, Cesareni G (2007) MINT: the molecular interaction database. Nuc Acid Re 35(Database issue):D572–D574CrossRefGoogle Scholar
  12. 12.
    Zhu G, Wu A, Xu X-J, Xiao P-P, Lu L, Liu J, Zhao X-M (2016) PPIM: a protein-protein interaction database for maize. Plant Physiol 170:618–626CrossRefGoogle Scholar
  13. 13.
    Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, von Mering C (2017) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucl Acid Res 45(Database issue):D362–D368CrossRefGoogle Scholar
  14. 14.
    Arighi CN, Roberts PM, Agarwal S, Bhattacharya S, Cesareni G, Chatr-aryamontri A, Wu CH (2011) Bio Creative III interactive task: an overview. BMC Bioinform 12(Suppl 8):S4CrossRefGoogle Scholar
  15. 15.
    Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D (2000) DIP: the database of interacting proteins. Nuc Acid Res 28:289–291CrossRefGoogle Scholar
  16. 16.
    Luo H, Lin Y, Gao F, Zhang CT, Zhang R (2013) DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nuc Acid Res 42:D574–D580CrossRefGoogle Scholar
  17. 17.
    Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, Montrone C, Mark P, Stümpflen V, Mewes HW, Ruepp A (2004) The MIPS mammalian protein–proteininteraction database. Bioinformatics 21:832–834CrossRefGoogle Scholar
  18. 18.
    Vazquez A, Alzate O (eds) (2010) Protein interaction networks, neuroproteomics. CRC Press/Taylor & Francis, Boca RatonGoogle Scholar
  19. 19.
    Hahn MW, Kern AD (2005) Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol 22:803–806CrossRefGoogle Scholar
  20. 20.
    Mistry D, Wise RP, Dickerson JA (2017) Diff SLC: a graph centrality method to detect essential proteins of a protein-protein interaction network. PLoS One 12:e0187091CrossRefGoogle Scholar
  21. 21.
    Opsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: generalizing degree and shortest paths. Social Net 32:245–251CrossRefGoogle Scholar
  22. 22.
    Abedi M, Gheisari Y (2015) Nodes with high centrality in protein interaction networks are responsible for driving signaling pathways in diabetic nephropathy. Peer J 3:e1284CrossRefGoogle Scholar
  23. 23.
    Joy MP, Brock A, Ingber DE, Huang S (2005) High-betweenness proteins in the yeast protein interaction network. Bio Med Res Int 2:96–103Google Scholar
  24. 24.
    Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Nat Acad Sci USA 99:12–7821-7826MathSciNetCrossRefGoogle Scholar
  25. 25.
    Hahn MW, Kern AD (2004) Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol 22:803–806CrossRefGoogle Scholar
  26. 26.
    Bihari A, Pandia MK (2015) Eigenvector centrality and its application in research professionals’ relationship network. In: Futuristic trends on computational analysis and knowledge management (ABLAZE), pp 510–514Google Scholar
  27. 27.
    Özgür A, Vu T, Erkan G, Radev DR (2008) Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics 24:i277–i285CrossRefGoogle Scholar
  28. 28.
    Newman ME (2006) Modularity and community structure in networks. Proceed Nat Acad Sci 103:8577–8582CrossRefGoogle Scholar
  29. 29.
    Bennett L, Kittas A, Liu S, Papageorgiou LG, Tsoka S (2014) Community structure detection for overlapping modules through mathematical programming in protein interaction networks. PloS One 20:e112821CrossRefGoogle Scholar
  30. 30.
    Lewis AC, Jones NS, Porter MA, Deane CM (2010) The function of communities in protein interaction networks at multiple scales. BMC Syst Biol 4:100CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • D. Narmadha
    • 1
    • 2
    Email author
  • A. Pravin
    • 1
  • G. Naveen Sundar
    • 2
  • Premnath Dhanaraj
    • 2
  1. 1.Sathyabama Institute of Science and TechnologyChennaiIndia
  2. 2.Karunya Institute of Technology and SciencesCoimbatoreIndia

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