IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0016932.html
   My bibliography  Save this article

Novel Feature for Catalytic Protein Residues Reflecting Interactions with Other Residues

Author

Listed:
  • Yizhou Li
  • Gongbing Li
  • Zhining Wen
  • Hui Yin
  • Mei Hu
  • Jiamin Xiao
  • Menglong Li

Abstract

Owing to their potential for systematic analysis, complex networks have been widely used in proteomics. Representing a protein structure as a topology network provides novel insight into understanding protein folding mechanisms, stability and function. Here, we develop a new feature to reveal correlations between residues using a protein structure network. In an original attempt to quantify the effects of several key residues on catalytic residues, a power function was used to model interactions between residues. The results indicate that focusing on a few residues is a feasible approach to identifying catalytic residues. The spatial environment surrounding a catalytic residue was analyzed in a layered manner. We present evidence that correlation between residues is related to their distance apart most environmental parameters of the outer layer make a smaller contribution to prediction and ii catalytic residues tend to be located near key positions in enzyme folds. Feature analysis revealed satisfactory performance for our features, which were combined with several conventional features in a prediction model for catalytic residues using a comprehensive data set from the Catalytic Site Atlas. Values of 88.6 for sensitivity and 88.4 for specificity were obtained by 10fold crossvalidation. These results suggest that these features reveal the mutual dependence of residues and are promising for further study of structurefunction relationship.

Suggested Citation

  • Yizhou Li & Gongbing Li & Zhining Wen & Hui Yin & Mei Hu & Jiamin Xiao & Menglong Li, 2011. "Novel Feature for Catalytic Protein Residues Reflecting Interactions with Other Residues," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-9, March.
  • Handle: RePEc:plo:pone00:0016932
    DOI: 10.1371/journal.pone.0016932
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0016932
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0016932&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0016932?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
    ---><---

    Citations

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


    Cited by:

    1. Cheng Zheng & Mingjun Wang & Kazuhiro Takemoto & Tatsuya Akutsu & Ziding Zhang & Jiangning Song, 2012. "An Integrative Computational Framework Based on a Two-Step Random Forest Algorithm Improves Prediction of Zinc-Binding Sites in Proteins," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-15, November.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0016932. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.