IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0003400.html
   My bibliography  Save this article

ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction

Author

Listed:
  • Sitao Wu
  • Yang Zhang

Abstract

We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28°/46°, which is ∼10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value

Suggested Citation

  • Sitao Wu & Yang Zhang, 2008. "ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-8, October.
  • Handle: RePEc:plo:pone00:0003400
    DOI: 10.1371/journal.pone.0003400
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0003400
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0003400&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0003400?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. Ivan Kalev & Michael Habeck, 2013. "Confidence-Guided Local Structure Prediction with HHfrag," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-7, October.
    2. Harinder Singh & Sandeep Singh & Gajendra P S Raghava, 2014. "Evaluation of Protein Dihedral Angle Prediction Methods," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-9, August.
    3. Jiangning Song & Hao Tan & Mingjun Wang & Geoffrey I Webb & Tatsuya Akutsu, 2012. "TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-16, February.
    4. Ngaam J Cheung & Wookyung Yu, 2018. "De novo protein structure prediction using ultra-fast molecular dynamics simulation," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-17, 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:pone00:0003400. 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.