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.
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics