Selection of peptides that bind to the HLA-A2.1 molecule by molecular modelling

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Title
Selection of peptides that bind to the HLA-A2.1 molecule by molecular modelling
Author(s)
Jong-Seok Lim; Seung Moak Kim; Hee Gu Lee; Ki Young Lee; T J Kwon; Kil Hyoun Kim
Bibliographic Citation
Molecular Immunology, vol. 33, no. 2, pp. 221-230
Publication Year
1996
Abstract
Cytotoxic T lymphocytes recognize antigenic peptides in association with major histocompatibility complex class I proteins. Although a large set of class I binding peptides has been described, it is not yet easy to search for potentially antigenic peptides without synthesis of a panel of peptides, and subsequent binding assays. In order to predict HLA-A2.1-restricted antigenic epitopes, a computer model of the HLA-A2.1 molecule was established using X-ray crystallography data. In this model nonameric peptide sequences were aligned. In a molecular dynamics (MD) simulation with two sets of peptides known to be presented by HLA-A2.1, it was important to know the anchor amino acid residue preference and the distance between the anchor residues. We show here that the peptides bound to the HLA-A2.1 model structure possess a side chain of C-terminal anchor residue oriented into the binding groove with different distances between the two anchor residues from 15 to 21?. We also synthesized a set of nonamer peptides containing amino acid sequences of Hepatitis B virus protein that were selected on the basis of previously described HLA-A2.1 specific motifs. When results obtained from the MD simulation were compared with functional binding assays using the TAP-deficient cell line T2, it was evident that the MD simulation method improves prediction of the HLA-A2.1 binding epitope sequence. These results suggest that this approach can provide a way to predict peptide epitopes and search for antigenic regions in sequences in a variety of antigens without screening a large number of synthetic peptides.
Keyword
epitope predictionMHC binding peptidecomputer modeling
ISSN
0161-5890
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/0161-5890(95)00065-8
Type
Article
Appears in Collections:
Division of Biomedical Research > Immunotherapy Research Center > 1. Journal Articles
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