T-cell epitope prediction with combinatorial peptide libraries. |
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Authors: | Myong-Hee Sung Yingdong Zhao Roland Martin Richard Simon |
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Institution: | Molecular Statistics and Bioinformatics Section, Biometric Research Branch, National Cancer Institute, National Institutes of Health, 6130 Executive Boulevard, EPN 8146, MSC 7434, Bethesda, MD 20892-7434, USA. |
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Abstract: | T cell receptors (TCR) recognize antigenic peptides in complex with the major histocompatibility complex (MHC) molecules and this trimolecular interaction initiates antigen-specific signaling pathways in the responding T lymphocytes. For the study of autoimmune diseases and vaccine development, it is important to identify peptides (epitopes) that can stimulate a given TCR. The use of combinatorial peptide libraries has recently been introduced as a powerful tool for this purpose. A combinatorial library of n-mer peptides is a set of complex mixtures each characterized by one position fixed to be a specified amino acid and all other positions randomized. A given TCR can be fingerprinted by screening a variety of combinatorial libraries using a proliferation assay. Here, we present statistical models for elucidating the recognition profile of a TCR using combinatorial library proliferation assay data and known MHC binding data. |
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