title: Towards in silico prediction of immunogenic epitopes.
abstract: As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments - data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures - offer hope for the future.
authors.affiliation: Edward Jenner Institute for Vaccine Research, Compton, RG20 7NN, Berkshire, UK. darren.flower@jenner.ac.uk
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower
mesh_terms: D000818:Animals; D017951:Antigen Presentation / Q000276:immunology; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry / Q000276:immunology*; D006801:Humans; D018448:Models, Immunological; D008958:Models, Molecular; D011485:Protein Binding / Q000276:immunology
chemical_list: D018984:Epitopes, T-Lymphocyte