IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0079439.html
   My bibliography  Save this article

Inference of Structure Ensembles of Flexible Biomolecules from Sparse, Averaged Data

Author

Listed:
  • Simon Olsson
  • Jes Frellsen
  • Wouter Boomsma
  • Kanti V Mardia
  • Thomas Hamelryck

Abstract

We present the theoretical foundations of a general principle to infer structure ensembles of flexible biomolecules from spatially and temporally averaged data obtained in biophysical experiments. The central idea is to compute the Kullback-Leibler optimal modification of a given prior distribution with respect to the experimental data and its uncertainty. This principle generalizes the successful inferential structure determination method and recently proposed maximum entropy methods. Tractability of the protocol is demonstrated through the analysis of simulated nuclear magnetic resonance spectroscopy data of a small peptide.

Suggested Citation

  • Simon Olsson & Jes Frellsen & Wouter Boomsma & Kanti V Mardia & Thomas Hamelryck, 2013. "Inference of Structure Ensembles of Flexible Biomolecules from Sparse, Averaged Data," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-7, November.
    • Handle: RePEc:plo:pone00:0079439
    DOI: 10.1371/journal.pone.0079439
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079439
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0079439&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0079439?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. Wouter Boomsma & Jesper Ferkinghoff-Borg & Kresten Lindorff-Larsen, 2014. "Combining Experiments and Simulations Using the Maximum Entropy Principle," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-9, February.
    2. Matteo Tiberti & Elena Papaleo & Tone Bengtsen & Wouter Boomsma & Kresten Lindorff-Larsen, 2015. "ENCORE: Software for Quantitative Ensemble Comparison," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-16, October.

    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:pone00:0079439. 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.