title: Prediction of HLA-DQ3.2beta ligands: evidence of multiple registers in class II binding peptides.
abstract: BACKGROUND
  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-DQ3.2beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2beta binding and non-binding peptides obtained from biochemical and functional studies.
  
  
  RESULTS
  Our model predicts DQ3.2beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2beta 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.
authors.affiliation: Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597, Singapore.
authors.forename: Joo Chuan
authors.initials: JC
authors.lastname: Tong
authors.forename: Guang Lan
authors.initials: GL
authors.lastname: Zhang
authors.forename: Tin Wee
authors.initials: TW
authors.lastname: Tan
authors.forename: J Thomas
authors.initials: JT
authors.lastname: August
authors.forename: Vladimir
authors.initials: V
authors.lastname: Brusic
authors.forename: Shoba
authors.initials: S
authors.lastname: Ranganathan
mesh_terms: D000906:Antibodies / Q000737:chemistry / Q000276:immunology; D001665:Binding Sites; D003198:Computer Simulation; D030562:Databases, Protein; D018604:Epitope Mapping / Q000379:methods*; D006683:HLA-DQ Antigens / Q000737:chemistry* / Q000276:immunology*; D007118:Immunoassay / Q000379:methods; D008956:Models, Chemical; D018448:Models, Immunological; D010455:Peptides / Q000737:chemistry* / Q000276:immunology*; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D000906:Antibodies; D006683:HLA-DQ Antigens; D010455:Peptides