title: A structure-based approach for prediction of MHC-binding peptides.
abstract: Identification of immunodominant peptides is the first step in the rational design of peptide vaccines aimed at T-cell immunity. The advances in sequencing techniques and the accumulation of many protein sequences without the purified protein challenge the development of computer algorithms to identify dominant T-cell epitopes based on sequence data alone. Here, we focus on antigenic peptides recognized by cytotoxic T cells. The selection of T-cell epitopes along a protein sequence is influenced by the specificity of each of the processing stages that precede antigen presentation. The most selective of these processing stages is the binding of the peptides to the major histocompatibility complex molecules, and therefore many of the predictive algorithms focus on this stage. Most of these algorithms are based on known binding peptides whose sequences have been used for the characterization of binding motifs or profiles. Here, we describe a structure-based algorithm that does not rely on previous binding data. It is based on observations from crystal structures that many of the bound peptides adopt similar conformations and placements within the MHC groove. The algorithm uses a structural template of the peptide in the MHC groove upon which peptide candidates are threaded and their fit to the MHC groove is evaluated by statistical pairwise potentials. It can rank all possible peptides along a protein sequence or within a suspected group of peptides, directing the experimental efforts towards the most promising peptides. This approach is especially useful when no previous peptide binding data are available.
authors.affiliation: Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem 91120, Israel. yaelal@md.huji.ac.il
authors.forename: Yael
authors.initials: Y
authors.lastname: Altuvia
authors.forename: Hanah
authors.initials: H
authors.lastname: Margalit
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006680:HLA Antigens / Q000276:immunology / Q000378:metabolism*; D006801:Humans; D010446:Peptide Fragments / Q000276:immunology / Q000378:metabolism*; D011237:Predictive Value of Tests; D011485:Protein Binding / Q000276:immunology; D013329:Structure-Activity Relationship
chemical_list: D006680:HLA Antigens; D010446:Peptide Fragments