Bioinformatics Advance Access published online on March 1, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl071
1 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597; Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613
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-DQ3.2 Results: Our model predicts DQ3.2
Received October 17, 2005
Revised February 22, 2006
Accepted February 23, 2006
Article
Prediction of HLA-DQ3.2
Joo Chuan Tong 1,
Guang Lan Zhang 2,
Tin Wee Tan 3,
J. Thomas August 4,
Vladimir Brusic 5,
and
Shoba Ranganathan 6 *
ligands: evidence of multiple registers in class II binding peptides
2 Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613
3 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597
4 Division of Biomedical Sciences, John Hopkins Medicine in Singapore, 41 Science Park Road, Lobby C, The Gemini, Singapore 117610; Department of Pharmacology and Molecular Sciences, John Hopkins University School of Medicine, Baltimore, MD, USA
5 Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613; Australian Centre for Plant Functional genomics, School of Land and Food Sciences, and Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia
6 Department of Chemistry and Biomolecular Sciences & Biotechnology Research Institute, Macquarie University, NSW 2109, Australia; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597
Abstract 
complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2 receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2 binding and non-binding peptides obtained from biochemical and functional studies. binding peptides with high accuracy (area under the ROC curve AROC>0.90), compared to experimental data. We investigated the binding patterns of DQ3.2 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.
Associate Editor: Dmitrij Frishman
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