Prediction of HLA-DQ3.2β ligands: Evidence of multiple registers in class II binding peptides

Joo Chuan Tong, Guang Lan Zhang, Tin Wee Tan, J. Thomas August, Vladimir Brusic, Shoba Ranganathan

Research output: Journal PublicationArticlepeer-review

41 Citations (Scopus)


Motivation: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. Methods: We describe a structure-based prediction model for modeling peptide-DQ3.2β complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2β receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2β binding and non-binding peptides obtained from biochemical and functional studies. Results: Our model predicts DQ3.2β binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve AROC > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2β peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

Original languageEnglish
Pages (from-to)1232-1238
Number of pages7
Issue number10
Publication statusPublished - 15 May 2006
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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