Towards in silico prediction of immunogenic epitopes

Darren R. Flower

Research output: Contribution to journalArticlepeer-review

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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