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Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules

Author

Listed:
  • Hao Zhang
  • Peng Wang
  • Nikitas Papangelopoulos
  • Ying Xu
  • Alessandro Sette
  • Philip E Bourne
  • Ole Lund
  • Julia Ponomarenko
  • Morten Nielsen
  • Bjoern Peters

Abstract

Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0009272
    DOI: 10.1371/journal.pone.0009272
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    References listed on IDEAS

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    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. Peter D. Kwong & Richard Wyatt & James Robinson & Raymond W. Sweet & Joseph Sodroski & Wayne A. Hendrickson, 1998. "Structure of an HIV gp120 envelope glycoprotein in complex with the CD4 receptor and a neutralizing human antibody," Nature, Nature, vol. 393(6686), pages 648-659, June.
    3. Peng Wang & John Sidney & Courtney Dow & Bianca Mothé & Alessandro Sette & Bjoern Peters, 2008. "A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach," PLOS Computational Biology, Public Library of Science, vol. 4(4), pages 1-10, April.
    4. 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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    Cited by:

    1. 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.

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