Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016, Article ID 3832176, 10 pages
http://dx.doi.org/10.1155/2016/3832176
Research Article

A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information

1School of Computer Science and Technology, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China
2School of Computational Science and Engineering, University of South Carolina, Columbia, SC, USA

Received 10 March 2016; Accepted 4 May 2016

Academic Editor: Yungang Xu

Copyright © 2016 Zhao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. M. Zinkernagel and P. C. Doherty, “Restriction of in vitro T cell-mediated cytotoxicity in lymphocytic choriomeningitis within a syngeneic or semiallogeneic system,” Nature, vol. 248, no. 5450, pp. 701–702, 1974. View at Publisher · View at Google Scholar · View at Scopus
  2. P. I. Terasaki, “A brief history of HLA,” Immunologic research, vol. 38, no. 1–3, pp. 139–148, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Maenaka and E. Y. Jones, “MHC superfamily structure and the immune system,” Current Opinion in Structural Biology, vol. 9, no. 6, pp. 745–753, 1999. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Robinson, M. J. Waller, S. C. Fail et al., “The IMGT/HLA database,” Nucleic Acids Research, vol. 37, supplement 1, pp. D1013–D1017, 2009. View at Publisher · View at Google Scholar
  5. M. Nielsen, C. Lundegaard, T. Blicher et al., “Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan,” PLoS Computational Biology, vol. 4, no. 7, article e1000107, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. H.-G. Rammensee, J. Bachmann, N. P. N. Emmerich, O. A. Bachor, and S. Stevanović, “SYFPEITHI: database for MHC ligands and peptide motifs,” Immunogenetics, vol. 50, no. 3, pp. 213–219, 1999. View at Publisher · View at Google Scholar · View at Scopus
  7. R. N. Germain, “MHC-dependent antigen processing and peptide presentation: providing ligands for T lymphocyte activation,” Cell, vol. 76, no. 2, pp. 287–299, 1994. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Sturniolo, E. Bono, J. Ding et al., “Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices,” Nature Biotechnology, vol. 17, no. 6, pp. 555–561, 1999. View at Publisher · View at Google Scholar · View at Scopus
  9. N. Pfeifer and O. Kohlbacher, “Multiple instance learning allows MHC class II epitope predictions across alleles,” Algorithms in Bioinformatics, vol. 5251, pp. 210–221, 2008. View at Google Scholar
  10. T. J. Kindt, R. A. Goldsby, B. A. Osborne, and J. Kuby, Kuby Immunology, WH Freeman & Company, New York, NY, USA, 2007.
  11. A. Sette, L. Adorini, E. Appella et al., “Structural requirements for the interaction between peptide antigens and I-Ed molecules,” Journal of Immunology, vol. 143, no. 10, pp. 3289–3294, 1989. View at Google Scholar · View at Scopus
  12. M. Nielsen, C. Lundegaard, T. Blicher et al., “Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan,” PLoS Computational Biology, vol. 4, no. 7, Article ID e1000107, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. A. J. Bordner and H. D. Mittelmann, “MultiRTA: a simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes,” BMC Bioinformatics, vol. 11, article 482, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. P. A. Reche, H. Zhang, J.-P. Glutting, and E. L. Reinherz, “EPIMHC: a curated database of MHC-binding peptides for customized computational vaccinology,” Bioinformatics, vol. 21, no. 9, pp. 2140–2141, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. P. A. Reche, J.-P. Glutting, H. Zhang, and E. L. Reinherz, “Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles,” Immunogenetics, vol. 56, no. 6, pp. 405–419, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Sette, L. Adorini, E. Appella et al., “Structural requirements for the interaction between peptide antigens and I-Ed molecules,” The Journal of Immunology, vol. 143, no. 10, pp. 3289–3294, 1989. View at Google Scholar · View at Scopus
  17. M. Nielsen, S. Justesen, O. Lund, C. Lundegaard, and S. Buus, “NetMHCIIpan-2.0—improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure,” Immunome Research, vol. 6, no. 1, article 9, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Nielsen and O. Lund, “NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction,” BMC Bioinformatics, vol. 10, article 296, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. G. L. Zhang, D. S. DeLuca, D. B. Keskin et al., “MULTIPRED2: a computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles,” Journal of Immunological Methods, vol. 374, no. 1-2, pp. 53–61, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. X.-Y. Cheng, W.-J. Huang, S.-C. Hu et al., “A global characterization and identification of multifunctional enzymes,” PLoS ONE, vol. 7, no. 6, Article ID e38979, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. Q. Zou, Z. Wang, X. Guan, B. Liu, Y. Wu, and Z. Lin, “An approach for identifying cytokines based on a novel ensemble classifier,” BioMed Research International, vol. 2013, Article ID 686090, 11 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. W. Shen, S. Zhang, and H. Wong, “An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles,” Proteome Science, vol. 11, p. S15, 2013. View at Publisher · View at Google Scholar
  23. L. Zhang, Y. Chen, H.-S. Wong, S. Zhou, H. Mamitsuka, and S. Zhu, “TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules,” PLoS ONE, vol. 7, no. 2, Article ID e30483, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. P. A. Reche and E. L. Reinherz, “Prediction of peptide-MHC binding using profiles,” Methods in Molecular Biology, vol. 409, pp. 185–200, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. F. Guo, S. C. Li, L. Wang, and D. Zhu, “Protein-protein binding site identification by enumerating the configurations,” BMC Bioinformatics, vol. 13, article 158, 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. F. Guo, S. C. Li, and L. Wang, “Protein-protein binding sites prediction by 3D structural similarities,” Journal of Chemical Information and Modeling, vol. 51, no. 12, pp. 3287–3294, 2011. View at Publisher · View at Google Scholar · View at Scopus