Epitopes described in "The utility and limitations of current Web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection."

Article Authors:Francisco A Chaves; Alvin H Lee; Jennifer L Nayak; Katherine A Richards; Andrea J Sant
Article Title:The utility and limitations of current Web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection.
Reference Detail
Reference ID:1025011
Abstract:The ability to track CD4 T cells elicited in response to pathogen infection or vaccination is critical because of the role these cells play in protective immunity. Coupled with advances in genome sequencing of pathogenic organisms, there is considerable appeal for implementation of computer-based algorithms to predict peptides that bind to the class II molecules, forming the complex recognized by CD4 T cells. Despite recent progress in this area, there is a paucity of data regarding the success of these algorithms in identifying actual pathogen-derived epitopes. In this study, we sought to rigorously evaluate the performance of multiple Web-available algorithms by comparing their predictions with our results--obtained by purely empirical methods for epitope discovery in influenza that used overlapping peptides and cytokine ELISPOTs--for three independent class II molecules. We analyzed the data in different ways, trying to anticipate how an investigator might use these computational tools for epitope discovery. We come to the conclusion that currently available algorithms can indeed facilitate epitope discovery, but all shared a high degree of false-positive and false-negative predictions. Therefore, efficiencies were low. We also found dramatic disparities among algorithms and between predicted IC(50) values and true dissociation rates of peptide-MHC class II complexes. We suggest that improved success of predictive algorithms will depend less on changes in computational methods or increased data sets and more on changes in parameters used to "train" the algorithms that factor in elements of T cell repertoire and peptide acquisition by class II molecules.
Affiliations:Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY 14642, USA.
Reference Type:Literature
PubMed ID:22467652
Journal:J Immunol
Journal Volume:188
Article Pages:4235-48
Journal ISSN:0022-1767
Article Chemical List:Epitopes;Histocompatibility Antigens Class II;Peptides
Article MeSH List:Algorithms; Animals; CD4-Positive T-Lymphocytes; Computer Simulation; Epitopes(genetics; immunology); Histocompatibility Antigens Class II(genetics; immunology); Humans; Infection(genetics; immunology); Internet; Mice; Mice, Transgenic; Peptides(genetics; immunology); Sequence Analysis, DNA(methods)
Curation Last Updated:2015-07-30 20:46:10