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Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpan

Author

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  • Morten Nielsen
  • Claus Lundegaard
  • Thomas Blicher
  • Bjoern Peters
  • Alessandro Sette
  • Sune Justesen
  • Søren Buus
  • Ole Lund

Abstract

CD4 positive T helper cells control many aspects of specific immunity. These cells are specific for peptides derived from protein antigens and presented by molecules of the extremely polymorphic major histocompatibility complex (MHC) class II system. The identification of peptides that bind to MHC class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent. Validation of the method includes identification of endogenously derived HLA class II ligands, cross-validation, leave-one-molecule-out, and binding motif identification for hitherto uncharacterized HLA-DR molecules. The validation shows that the method can successfully predict binding for HLA-DR molecules—even in the absence of specific data for the particular molecule in question. Moreover, when compared to TEPITOPE, currently the only other publicly available prediction method aiming at providing broad HLA-DR allelic coverage, NetMHCIIpan performs equivalently for alleles included in the training of TEPITOPE while outperforming TEPITOPE on novel alleles. We propose that the method can be used to identify those hitherto uncharacterized alleles, which should be addressed experimentally in future updates of the method to cover the polymorphism of HLA-DR most efficiently. We thus conclude that the presented method meets the challenge of keeping up with the MHC polymorphism discovery rate and that it can be used to sample the MHC “space,” enabling a highly efficient iterative process for improving MHC class II binding predictions.Author Summary: CD4 positive T helper cells provide essential help for stimulation of both cellular and humoral immune reactions. T helper cells recognize peptides presented by molecules of the major histocompatibility complex (MHC) class II system. HLA-DR is a prominent example of a human MHC class II locus. The HLA molecules are extremely polymorphic, and more than 500 different HLA-DR protein sequences are known today. Each HLA-DR molecule potentially binds a unique set of antigenic peptides, and experimental characterization of the binding specificity for each molecule would be an immense and highly costly task. Only a very limited set of MHC molecules has been characterized experimentally. We have demonstrated earlier that it is possible to derive accurate predictions for MHC class I proteins by interpolating information from neighboring molecules. It is not straightforward to take a similar approach to derive pan-specific HLA-DR class II predictions because the HLA class II molecules can bind peptides of very different lengths. Here, we nonetheless show that this is indeed possible. We develop an HLA-DR pan-specific method that allows for prediction of binding to any HLA-DR molecule of known sequence—even in the absence of specific data for the particular molecule in question.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1000107
    DOI: 10.1371/journal.pcbi.1000107
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    1. Bjoern Peters & Huynh-Hoa Bui & Sune Frankild & Morten Nielsen & Claus Lundegaard & Emrah Kostem & Derek Basch & Kasper Lamberth & Mikkel Harndahl & Ward Fleri & Stephen S Wilson & John Sidney & Ole L, 2006. "A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules," PLOS Computational Biology, Public Library of Science, vol. 2(6), pages 1-11, June.
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    1. 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.
    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.
    3. 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.
    4. 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.
    5. 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.

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