IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0014383.html
   My bibliography  Save this article

Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes

Author

Listed:
  • Andrew J Bordner

Abstract

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632–0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.

Suggested Citation

  • Andrew J Bordner, 2010. "Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-12, December.
  • Handle: RePEc:plo:pone00:0014383
    DOI: 10.1371/journal.pone.0014383
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0014383
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0014383&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0014383?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
    ---><---

    References listed on IDEAS

    as
    1. Morten Nielsen & Claus Lundegaard & Thomas Blicher & Bjoern Peters & Alessandro Sette & Sune Justesen & Søren Buus & Ole Lund, 2008. "Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpan," PLOS Computational Biology, Public Library of Science, vol. 4(7), pages 1-10, July.
    2. Hao Zhang & Peng Wang & Nikitas Papangelopoulos & Ying Xu & Alessandro Sette & Philip E Bourne & Ole Lund & Julia Ponomarenko & Morten Nielsen & Bjoern Peters, 2010. "Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-10, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Webber W P Liao & Jonathan W Arthur, 2011. "Predicting Peptide Binding Affinities to MHC Molecules Using a Modified Semi-Empirical Scoring Function," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.

    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. Hao Zhang & Peng Wang & Nikitas Papangelopoulos & Ying Xu & Alessandro Sette & Philip E Bourne & Ole Lund & Julia Ponomarenko & Morten Nielsen & Bjoern Peters, 2010. "Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-10, February.
    2. Nicolas Rapin & Ole Lund & Massimo Bernaschi & Filippo Castiglione, 2010. "Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-14, April.
    3. Gouri Shankar Pandey & Chen Yanover & Tom E Howard & Zuben E Sauna, 2013. "Polymorphisms in the F8 Gene and MHC-II Variants as Risk Factors for the Development of Inhibitory Anti-Factor VIII Antibodies during the Treatment of Hemophilia A: A Computational Assessment," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-11, May.
    4. Kasper Winther Jørgensen & Søren Buus & Morten Nielsen, 2010. "Structural Properties of MHC Class II Ligands, Implications for the Prediction of MHC Class II Epitopes," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-6, December.

    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:pone00:0014383. 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: 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.