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Optimizing peptide matrices for identifying T‐cell antigens
Author(s) -
Precopio Melissa L.,
Butterfield Tiffany R.,
Casazza Joseph P.,
Little Susan J.,
Richman Douglas D.,
Koup Richard A.,
Roederer Mario
Publication year - 2008
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.20646
Subject(s) - computational biology , antigen , peptide , biology , computer science , immunology , biochemistry
Mapping T‐cell epitopes for a pathogen or vaccine requires a complex method for screening hundreds to thousands of peptides with a limited amount of donor sample. We describe an optimized deconvolution process by which peptides are pooled in a matrix format to minimize the number of tests required to identify peptide epitopes. Four peptide pool matrices were constructed to deconvolute the HIV‐specific T‐cell response in three HIV‐infected individuals. ELISpot assays were used to map peptide antigens. Many HIV peptides were mapped in all three individuals. However, there were several challenges and limitations associated with the deconvolution process. Peptides that induced low‐frequency responses or were masked by peptide competition within a given pool were not identified, because they did not meet the threshold criteria for a positive response. Also, amino acid sequence variation limited the ability of this method to map autologous HIV peptides. Alternative analysis strategies and revisions to the original matrix optimizations are presented that address ways to increase peptide identification. This optimized deconvolution method allows for efficient mapping of T‐cell peptide epitopes. It is rapid, powerful, efficient, and unrestricted by HLA type. Published 2008 Wiley‐Liss, Inc.