Computational methods for prediction of T-cell epitopes - A framework for modelling, testing, and applications

Vladimir Brusic, Vladimir B. Bajic, Nikolai Petrovsky

Research output: Contribution to journalArticle

121 Citations (Scopus)

Abstract

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes.

Original languageEnglish
Pages (from-to)436-443
Number of pages8
JournalMethods
Volume34
Issue number4
DOIs
Publication statusPublished - Dec 2004
Externally publishedYes

Fingerprint

T-Lymphocyte Epitopes
Computational methods
Testing
Peptide T
Molecular Models
Autoimmunity
Computer Simulation
Allergies
Communicable Diseases
Hypersensitivity
Molecular modeling
Alleles
Databases
Hidden Markov models
Identification (control systems)
Neural networks
Neoplasms
MHC binding peptide
Experiments

ASJC Scopus subject areas

  • Molecular Biology

Cite this

Computational methods for prediction of T-cell epitopes - A framework for modelling, testing, and applications. / Brusic, Vladimir; Bajic, Vladimir B.; Petrovsky, Nikolai.

In: Methods, Vol. 34, No. 4, 12.2004, p. 436-443.

Research output: Contribution to journalArticle

Brusic, Vladimir ; Bajic, Vladimir B. ; Petrovsky, Nikolai. / Computational methods for prediction of T-cell epitopes - A framework for modelling, testing, and applications. In: Methods. 2004 ; Vol. 34, No. 4. pp. 436-443.
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