IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v555y2020ics0378437120302697.html
   My bibliography  Save this article

A comparative analysis of dipeptides distribution in eukaryotes and prokaryotes by statistical mechanics

Author

Listed:
  • Moghaddasi, Hanieh
  • Rezaei, Soghra
  • Darooneh, Amir Hossein
  • Heshmati, Emran
  • Khalifeh, Khosrow

Abstract

Previous researches have shown that relevant words to the main topic of a given text have a long-range correlation in their spatial distribution, while common words are randomly distributed. Inspired by the features of human-written texts, in the present study, we have used non-extensive statistical mechanics for ranking dipeptides in proteomes of prokaryotes and eukaryotes. The ranking is performed based on the correlation between the words’ spatial distributions. It is found that some representative dipeptides have higher values of the non-extensivity parameter, and the majority of dipeptides are randomly distributed in their respective proteomes. Interestingly, the number of random-distributed dipeptides in evolutionary higher organisms is greater than those in prokaryotes. It concluded that reduction in the number of clustered dipeptides; provide a more effective energy optimization to overcome energetic challenges in more complex organisms. Also the reduction of the dipeptides clustering in the proteome of eukaryotes could be effectively compensated by higher clustering of CG dinucleotide at the genome level, that are used to control and optimize the gene expression.

Suggested Citation

  • Moghaddasi, Hanieh & Rezaei, Soghra & Darooneh, Amir Hossein & Heshmati, Emran & Khalifeh, Khosrow, 2020. "A comparative analysis of dipeptides distribution in eukaryotes and prokaryotes by statistical mechanics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    • Handle: RePEc:eee:phsmap:v:555:y:2020:i:c:s0378437120302697
    DOI: 10.1016/j.physa.2020.124567
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120302697
    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.2020.124567?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. Katsaloulis, P. & Theoharis, T. & Zheng, W.M. & Hao, B.L. & Bountis, A. & Almirantis, Y. & Provata, A., 2006. "Long-range correlations of RNA polymerase II promoter sequences across organisms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 308-322.
    2. David B. Searls, 2002. "The language of genes," Nature, Nature, vol. 420(6912), pages 211-217, November.
    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. Silva, R. & Silva, J.R.P. & Anselmo, D.H.A.L. & Alcaniz, J.S. & da Silva, W.J.C. & Costa, M.O., 2020. "An alternative description of power law correlations in DNA sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Ben Jacob, Eshel & Shapira, Yoash & Tauber, Alfred I., 2006. "Seeking the foundations of cognition in bacteria: From Schrödinger's negative entropy to latent information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 359(C), pages 495-524.
    3. Craciun, Dana & Isvoran, Adriana & Avram, N.M., 2009. "Long range correlation of hydrophilicity and flexibility along the calcium binding protein chains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(21), pages 4609-4618.
    4. Wu, Zuo-Bing, 2010. "Global transposable characteristics in the complete DNA sequence of the yeast," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5698-5705.
    5. Sutapa Datta & Subhasis Mukhopadhyay, 2015. "A Grammar Inference Approach for Predicting Kinase Specific Phosphorylation Sites," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-19, April.
    6. Sutapa Datta & Subhasis Mukhopadhyay, 2013. "A Composite Method Based on Formal Grammar and DNA Structural Features in Detecting Human Polymerase II Promoter Region," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, 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:555:y:2020:i:c:s0378437120302697. 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.