Prediction of Cross-Recognition of Peptide-HLA A2 by Melan-A-Specific Cytotoxic T Lymphocytes Using Three-Dimensional Quantitative Structure-Activity Relationships |
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| Authors: | Theres Fagerberg Vincent Zoete Sebastien Viatte Petra Baumgaertner Pedro M. Alves Pedro Romero Daniel E. Speiser Olivier Michielin |
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| Affiliation: | 1. Department of Oncology and Ludwig Center for Cancer Research, University of Lausanne, Lausanne, Switzerland.; 2. Swiss Institute of Bioinformatics, Quartier Sorge – Bâtiment Génopode, Lausanne, Switzerland.; 3. National Center of Competence in Research (NCCR) Molecular Oncology, Epalinges, Switzerland.; 4. Multidisciplinary Oncology Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland.; University of Edinburgh, United Kingdom, |
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| 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 (q2) is used as the fitness criterion and all obtained models are evaluated based on their q2 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. |
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