Bioinformatics tools for identifying T-cell epitopes

Vladimir Brusic, Darren R. Flower

Research output: Contribution to journalReview article

16 Citations (Scopus)

Abstract

Computer models usefully complement experimentation in the efficient discovery of MHC-binding peptides and T-cell epitopes, and have been applied successfully to predict T-cell epitopes in infectious disease, cancer, autoimmunity and allergy. Prediction methods include binding motifs, quantitative matrices, various artificial intelligence techniques and molecular modelling. Computational modelling should be performed according to strict standards, requiring careful data selection for model building, followed by adequate testing and validation. Many web-based databases and binding prediction programs are now available. Although certain prediction programs are reasonably accurate, at least for some MHC alleles, one cannot guarantee that all models produce results of adequate predictivity and therefore these prediction results should be used with care.

Original languageEnglish
Pages (from-to)18-23
Number of pages6
JournalDrug Discovery Today: BIOSILICO
Volume2
Issue number1
DOIs
Publication statusPublished - Jan 1 2004

ASJC Scopus subject areas

  • Drug Discovery

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