Towards in silico prediction of immunogenic epitopes

Darren R. Flower

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)667-674
Number of pages8
JournalTrends in Immunology
Volume24
Issue number12
Early online date26 Nov 2003
DOIs
Publication statusPublished - Dec 2003

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