IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v350y2005i1p52-62.html
   My bibliography  Save this article

Length, protein–protein interactions, and complexity

Author

Listed:
  • Tan, Taison
  • Frenkel, Daan
  • Gupta, Vishal
  • Deem, Michael W.

Abstract

The evolutionary reason for the increase in gene length from archaea to prokaryotes to eukaryotes observed in large-scale genome sequencing efforts has been unclear. We propose here that the increasing complexity of protein–protein interactions has driven the selection of longer proteins, as they are more able to distinguish among a larger number of distinct interactions due to their greater average surface area. Annotated protein sequences available from the SWISS-PROT database were analyzed for 13 eukaryotes, eight bacteria, and two archaea species. The number of subcellular locations to which each protein is associated is used as a measure of the number of interactions to which a protein participates. Two databases of yeast protein–protein interactions were used as another measure of the number of interactions to which each S. cerevisiae protein participates. Protein length is shown to correlate with both number of subcellular locations to which a protein is associated and number of interactions as measured by yeast two-hybrid experiments. Protein length is also shown to correlate with the probability that the protein is encoded by an essential gene. Interestingly, average protein length and number of subcellular locations are not significantly different between all human proteins and protein targets of known, marketed drugs. Increased protein length appears to be a significant mechanism by which the increasing complexity of protein–protein interaction networks is accommodated within the natural evolution of species. Consideration of protein length may be a valuable tool in drug design, one that predicts different strategies for inhibiting interactions in aberrant and normal pathways.

Suggested Citation

  • Tan, Taison & Frenkel, Daan & Gupta, Vishal & Deem, Michael W., 2005. "Length, protein–protein interactions, and complexity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(1), pages 52-62.
  • Handle: RePEc:eee:phsmap:v:350:y:2005:i:1:p:52-62
    DOI: 10.1016/j.physa.2004.11.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437104014712
    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.2004.11.021?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. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    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. Jeff Hasty & James J. Collins, 2001. "Unspinning the web," Nature, Nature, vol. 411(6833), pages 30-31, May.
    4. Ying-Xin Zhang & Kim Perry & Victor A. Vinci & Keith Powell & Willem P. C. Stemmer & Stephen B. del Cardayré, 2002. "Genome shuffling leads to rapid phenotypic improvement in bacteria," Nature, Nature, vol. 415(6872), pages 644-646, February.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Wen, Xiangxi & Tu, Congliang & Wu, Minggong, 2018. "Node importance evaluation in aviation network based on “No Return” node deletion method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 546-559.
    3. 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.
    4. Hayato Goto & Hideki Takayasu & Misako Takayasu, 2017. "Estimating risk propagation between interacting firms on inter-firm complex network," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-12, October.
    5. P.B., Divya & Lekha, Divya Sindhu & Johnson, T.P. & Balakrishnan, Kannan, 2022. "Vulnerability of link-weighted complex networks in central attacks and fallback strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    6. Foti, Nicholas J. & Pauls, Scott & Rockmore, Daniel N., 2013. "Stability of the World Trade Web over time – An extinction analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 37(9), pages 1889-1910.
    7. Caccioli, Fabio & Farmer, J. Doyne & Foti, Nick & Rockmore, Daniel, 2015. "Overlapping portfolios, contagion, and financial stability," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 50-63.
    8. Tsuchiya, Masa & Selvarajoo, Kumar & Piras, Vincent & Tomita, Masaru & Giuliani, Alessandro, 2009. "Local and global responses in complex gene regulation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1738-1746.
    9. Sun, Lanfang & Jiang, Lu & Li, Menghui & He, Dacheng, 2006. "Statistical analysis of gene regulatory networks reconstructed from gene expression data of lung cancer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 663-671.
    10. Yuji Yamamoto & Keiko Yokoyama, 2011. "Common and Unique Network Dynamics in Football Games," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-6, December.
    11. LaRocca, Sarah & Guikema, Seth D., 2015. "Characterizing and predicting the robustness of power-law networks," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 157-166.
    12. Pagani, Giuliano Andrea & Aiello, Marco, 2013. "The Power Grid as a complex network: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(11), pages 2688-2700.
    13. N. Foti & S. Pauls & Daniel N. Rockmore, 2011. "Stability of the World Trade Web over Time - An Extinction Analysis," Papers 1104.4380, arXiv.org, revised May 2011.
    14. John Platig & Peter J Castaldi & Dawn DeMeo & John Quackenbush, 2016. "Bipartite Community Structure of eQTLs," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-17, September.
    15. Biggiero, Lucio & Angelini, Pier Paolo, 2015. "Hunting scale-free properties in R&D collaboration networks: Self-organization, power-law and policy issues in the European aerospace research area," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 21-43.
    16. Markovič, Rene & Gosak, Marko & Marhl, Marko, 2014. "Broad-scale small-world network topology induces optimal synchronization of flexible oscillators," Chaos, Solitons & Fractals, Elsevier, vol. 69(C), pages 14-21.
    17. Gong, Pulin & van Leeuwen, Cees, 2003. "Emergence of scale-free network with chaotic units," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 321(3), pages 679-688.
    18. Tachimori, Yutaka & Iwanaga, Hiroaki & Tahara, Takashi, 2013. "The networks from medical knowledge and clinical practice have small-world, scale-free, and hierarchical features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6084-6089.
    19. 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.
    20. Heath Henderson & Arnob Alam, 2022. "The structure of risk-sharing networks," Empirical Economics, Springer, vol. 62(2), pages 853-886, February.

    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:350:y:2005:i:1:p:52-62. 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.