title: Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.
abstract: The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.
authors.affiliation: Department of Oncology and Ludwig Center for Cancer Research, University of Lausanne, Lausanne, Switzerland.
authors.forename: Theres
authors.initials: T
authors.lastname: Fagerberg
authors.forename: Vincent
authors.initials: V
authors.lastname: Zoete
authors.forename: Sebastien
authors.initials: S
authors.lastname: Viatte
authors.forename: Petra
authors.initials: P
authors.lastname: Baumgaertner
authors.forename: Pedro M
authors.initials: PM
authors.lastname: Alves
authors.forename: Pedro
authors.initials: P
authors.lastname: Romero
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Speiser
authors.forename: Olivier
authors.initials: O
authors.lastname: Michielin
mesh_terms: D015789:HLA-A2 Antigen / Q000378:metabolism*; D006801:Humans; D058965:MART-1 Antigen / Q000378:metabolism*; D008962:Models, Theoretical; D021281:Quantitative Structure-Activity Relationship; D013602:T-Lymphocytes, Cytotoxic / Q000378:metabolism*
chemical_list: D015789:HLA-A2 Antigen; D058965:MART-1 Antigen