IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i3p905-916.html
   My bibliography  Save this article

Network analysis of transmembrane protein structures

Author

Listed:
  • Emerson, I. Arnold
  • Gothandam, K.M.

Abstract

Most studies have shown that globular proteins exist in small-world networks. The present study is an attempt to determine differences in network parameters between transmembrane and globular proteins. Each protein is represented as an undirected graph, where residues represent nodes and inter-residue interactions as the edges. This was then compared to the degree-preserved random controls, to observe if any variation existed. Results indicate that there is a significant difference in shortest path lengths between transmembrane and globular proteins. Hydrophobic amino acids were found to be more spatially distributed in the transmembrane than in globular proteins causing significantly higher values of shortest path lengths (L). Assortativity values too, were found to be significantly higher in the transmembrane than in globular proteins which is due to the highly connected amino acid residues being exposed to the solvent in transmembrane proteins. On analyzing the closeness centrality, it was found that globular proteins have significantly higher values than the transmembrane proteins. We therefore conclude that transmembrane proteins possess small-world characteristics similar to that of globular proteins.

Suggested Citation

  • Emerson, I. Arnold & Gothandam, K.M., 2012. "Network analysis of transmembrane protein structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 905-916.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:3:p:905-916
    DOI: 10.1016/j.physa.2011.08.065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437111007114
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2011.08.065?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. H. Jeong & B. Tombor & R. Albert & Z. N. Oltvai & A.-L. Barabási, 2000. "The large-scale organization of metabolic networks," Nature, Nature, vol. 407(6804), pages 651-654, October.
    3. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    4. Bagler, Ganesh & Sinha, Somdatta, 2005. "Network properties of protein structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(1), pages 27-33.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Emerson, Isaac Arnold & Amala, Arumugam, 2017. "Protein contact maps: A binary depiction of protein 3D structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 782-791.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Emerson, Isaac Arnold & Amala, Arumugam, 2017. "Protein contact maps: A binary depiction of protein 3D structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 782-791.
    2. Sgrignoli, P. & Agliari, E. & Burioni, R. & Schianchi, A., 2015. "Instability and network effects in innovative markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 260-271.
    3. Long Ma & Xiao Han & Zhesi Shen & Wen-Xu Wang & Zengru Di, 2015. "Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-12, November.
    4. Mahyar, Hamidreza & Hasheminezhad, Rouzbeh & Ghalebi K., Elahe & Nazemian, Ali & Grosu, Radu & Movaghar, Ali & Rabiee, Hamid R., 2018. "Compressive sensing of high betweenness centrality nodes in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 166-184.
    5. Laurienti, Paul J. & Joyce, Karen E. & Telesford, Qawi K. & Burdette, Jonathan H. & Hayasaka, Satoru, 2011. "Universal fractal scaling of self-organized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3608-3613.
    6. Chen, Qinghua & Shi, Dinghua, 2004. "The modeling of scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 240-248.
    7. Lawford, Steve & Mehmeti, Yll, 2020. "Cliques and a new measure of clustering: With application to U.S. domestic airlines," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    8. Katya Pérez-Guzmán & Isela-Elizabeth Téllez-León & Ali Kharrazi & Brian Fath & Francisco Venegas-Martínez, 2018. "What makes Input-Output Tables of Trade of Raw Material Goods Peculiar Networks? The World and Mexican Cases," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 13(4), pages 483-505, Octubre-D.
    9. Wang, Huan & Xu, Chuan-Yun & Hu, Jing-Bo & Cao, Ke-Fei, 2014. "A complex network analysis of hypertension-related genes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 166-176.
    10. Selen Onel & Abe Zeid & Sagar Kamarthi, 2011. "The structure and analysis of nanotechnology co-author and citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 119-138, October.
    11. Ramon Ferrer i Cancho & Ricard V. Solé, 2001. "The Small-World of Human Language," Working Papers 01-03-016, Santa Fe Institute.
    12. Ruskin, Heather J. & Burns, John, 2006. "Weighted networks in immune system shape space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 365(2), pages 549-555.
    13. Daniel Straulino & Mattie Landman & Neave O'Clery, 2020. "A bi-directional approach to comparing the modular structure of networks," Papers 2010.06568, arXiv.org.
    14. Guillaume, Jean-Loup & Latapy, Matthieu, 2006. "Bipartite graphs as models of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 795-813.
    15. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    16. Marr, Carsten & Hütt, Marc-Thorsten, 2005. "Topology regulates pattern formation capacity of binary cellular automata on graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 641-662.
    17. Salcedo-Sanz, S. & Cuadra, L., 2019. "Quasi scale-free geographically embedded networks over DLA-generated aggregates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1286-1305.
    18. Dan Braha & Yaneer Bar-Yam, 2004. "Information Flow Structure in Large-Scale Product Development Organizational Networks," Industrial Organization 0407012, University Library of Munich, Germany.
    19. Wu, Jianshe & Jiao, Licheng, 2007. "Synchronization in complex delayed dynamical networks with nonsymmetric coupling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 513-530.
    20. Dan Braha & Yaneer Bar-Yam, 2007. "The Statistical Mechanics of Complex Product Development: Empirical and Analytical Results," Management Science, INFORMS, vol. 53(7), pages 1127-1145, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:391:y:2012:i:3:p:905-916. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.