Towards in silico prediction of immunogenic epitopes.
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.
Flower
Darren R
DR
Edward Jenner Institute for Vaccine Research, Compton, RG20 7NN, Berkshire, UK. darren.flower@jenner.ac.uk
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
D018984:Epitopes, T-Lymphocyte