We describe Rosetta-based computational protocols for predicting the three-dimensional structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally-determined structures as well as (1) energetic calculations to minimize loops, (2) docking methodology to refine the VL–VH relative orientation, and (3) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody–antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully-automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody–antigen docking. Tasks can be completed in under a day by using public supercomputers.
Background & aims: The pathogenesis of celiac disease (CD) is thought to be driven by a transglutaminase 2 (TG2)-dependent inflammatory CD4 + T-cell response in the gut towards deamidated gluten peptides in the context of disease-associated HLA-DQ molecules. We aimed to gain insight into the antigen presentation process underlying this mucosal immune response. Methods: We generated monoclonal antibodies (mAbs) specific for the peptide-MHC (pMHC) complex HLA-DQ2.5 and the immunodominant gluten epitope DQ2.5-glia-α1a using phage display. Using these mAbs we assessed gluten peptide presentation in freshly prepared single-cell suspensions of patient intestinal biopsies. Results: The mAbs allowed specific detection of in vivo generated pMHC complexes on the cells of gut biopsies from CD patients consuming gluten. Surprisingly, we identified B cells and plasma cells (PCs) as the most abundant cells presenting DQ2.5-glia-α1a in the inflamed mucosa. Further, we demonstrate that a group of these PCs expresses B-cell receptors (BCRs) specific for either gluten peptides or the autoantigen TG2. MHC class II (MHCII) expression was not restricted to these specific PCs associated with CD, but was observed at an average of 30% of the gut PCs both in CD patients as well as in non-inflamed tissue. Conclusions: A population of PCs in the gut expresses MHCII and is the most abundant cell type presenting the immunodominant gluten peptide DQ2.5-glia-α1a. These results suggest an important and previously unappreciated role of PCs in the gut as antigen presenting cells (APCs). PCs may thus be responsible for promoting and sustaining intestinal inflammation such as in CD.
We describe Rosetta-based computational protocols for predicting the three-dimensional structure of an antibody from sequence and then docking the antibody-antigen complexes. Antibody modeling leverages canonical loop conformations to graft large segments from experimentally-determined structures as well as (1) energetic calculations to minimize loops, (2) docking methodology to refine the V L -V H relative orientation, and (3) de novo prediction of the elusive CDR H3 loop. To alleviate model uncertainty, antibody-antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fullyautomated via the ROSIE web server or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 2,500 CPU-hours for antibody modeling and 250 CPU-hours for antibody-antigen docking. Both tasks can be completed in under a day by using public supercomputers.
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