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Computational binding assays of antigenic peptides

  • Vladimir Brusic*
  • , John Zeleznikow
  • *Corresponding author for this work

Research output: Journal PublicationArticlepeer-review

18 Citations (Scopus)

Abstract

Computer models can be combined with laboratory experiments for the efficient determination of (i) peptides that bind MHC molecules and (ii) T- cell epitopes. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures. This requires the definition of standards and experimental protocols for model application. We describe the requirements for validation and assessment of computer models. The utility of combining accurate predictions with a limited number of laboratory experiments is illustrated by practical examples. These include the identification of T-cell epitopes from IDDM-, melanoma- and malaria-related antigens by combining computational and conventional laboratory assays. The success rate in determining antigenic peptides, each in the context of a specific HLA molecule, ranged from 27 to 7 1%, while the natural prevalence of MHC-binding peptides is 0.1-5%.

Original languageEnglish
Pages (from-to)313-324
Number of pages12
JournalLetters in Peptide Science
Volume6
Issue number5-6
DOIs
Publication statusPublished - 1999
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Free Keywords

  • Antigenic peptides
  • Computer models
  • HLA
  • MHC
  • Prediction
  • T-cell epitopes

ASJC Scopus subject areas

  • Biochemistry

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