Specific and general HLA-DR binding motifs: comparison of algorithms
Keywords: 
T cell determinant
Class II molecules
Algorithm; MHC
Binding motif
Prediction of binding
Issue Date: 
2000
Publisher: 
Elsevier
ISSN: 
1879-1166
Citation: 
Borras-Cuesta F, Golvano J, Garcia-Granero M, Sarobe P, Riezu-Boj J, Huarte E, et al. Specific and general HLA-DR binding motifs: comparison of algorithms. Hum Immunol 2000 Mar;61(3):266-278.
Abstract
Using panels of peptides well characterized for their ability to bind to HLA DR1, DRB1*1101, or DRB1*0401 molecules, algorithms were deduced to predict binding to these molecules. These algorithms consist of blocks of 8 amino acids containing an amino acid anchor (Tyr, Phe, Trp, Leu, Ile, or Val) at position i and different amino acid combinations at positions i+2 to i+7 depending on the class II molecule. The sensitivity (% of correctly predicted binder peptides) and specificity (% of correctly predicted non-binder peptides) of these algorithms, were tested against different independent panels of peptides and compared to other algorithms reported in the literature. Similarly, using a panel of 232 peptides able to bind to one or more HLA molecules as well as 43 non-binder peptides, we deduced a general motif for the prediction of binding to HLA-DR molecules. The sensitivity and specificity of this general motif was dependent on the threshold score used for the predictions. For a score of 0.1, the sensitivity and specificity were 84.7% and 69.8%, respectively. This motif was validated against several panels of binder and non-binder peptides reported in the literature, as well as against 35, 15-mer peptides from hepatitis C virus core protein, that were synthesized and tested in a binding assay against a panel of 19 HLA-DR molecules. The sensitivities and specificities against these panels of peptides were similar to those attained against the panels used to deduce the algorithm. These results show that comparison of binder and non-binder peptides, as well as correcting for the relative abundance of amino acids in proteins, is a useful approach to deduce performing algorithms to predict binding to HLA molecules.

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