Protein-engineered molecules carrying GAD65 epitopes and targeting CD35 selectively down-modulate disease-associated human B lymphocytes.

Type 1 diabetes mellitus is an autoimmune metabolic disorder characterized by chronic hyperglycemia, the presence of autoreactive T and B cells and autoantibodies against self-antigens. A membrane-bound enzyme on the pancreatic beta-cells, glutamic acid decarboxylase 65 (GAD65), is one of the main autoantigens in type 1 diabetes. Autoantibodies against GAD65 are potentially involved in beta-cell destruction and decline of pancreatic functions. The human complement receptor type 1 (CD35) on B and T lymphocytes has a suppressive activity on these cells. We hypothesized that it may be possible to eliminate GAD65-specific B cells from type 1 diabetes patients by using chimeric molecules, containing an anti-CD35 antibody, coupled to peptides resembling GAD65 B/T epitopes. These molecules are expected to selectively bind the anti-GAD65 specific B cells by the co-cross-linking of the immunoglobulin receptor and CD35 and to deliver a suppressive signal. Two synthetic peptides derived from GAD65 protein (GAD65 epitopes) and anti-CD35 monoclonal antibody were used for the construction of two chimeras. The immunomodulatory activity of the engineered antibodies was tested in vitro using peripheral blood mononuclear cells (PBMCs) from type 1 diabetes patients. A reduction in the number of anti-GAD65 IgG antibody-secreting plasma cells and increased percentage of apoptotic B lymphocytes was observed after treatment of these PBMCs with the engineered antibodies. The constructed chimeric molecules are able to selectively modulate the activity of GAD65-specific B lymphocytes and the production of anti-GAD65 IgG autoantibodies by co-cross-linking of the inhibitory CD35 and the B cell antigen receptor (BCR). This treatment presents a possible way to alter the autoimmune nature of these cells.

Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction.

MOTIVATION: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. RESULTS: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 peptides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r(pred) = 0.593 and r(pred) = 0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. AVAILABILITY: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHCPred.

Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201.

A set of 102 peptides with affinity for the class I MHC HLA-A0201 molecule was subjected to three-dimensional quantitative structure-affinity relationship (3D QSAR) studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). A test set of 50 peptides was used to determine the predictive value of the models. The CoMFA models gave q(2) and r(2)pred below 0.5. The best CoMSIA model has q(2) = 0.542 and r(2)pred = 0.679, and includes hydrophobic, steric, and H-bond donor fields. The hydrophobic interactions play a dominant role in peptide-MHC molecule binding. CoMSIA coefficient contour maps were used to analyze the structural features of the peptides accounting for the affinity in terms of the three positively contributing physicochemical properties: local hydrophobicity, steric bulk and hydrogen-bond-donor ability.