Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Vladimir Brusic, George Rudy, Margo Honeyman, Jürgen Hammer, Leonard Harrison

Research output: Contribution to journalArticle

243 Citations (Scopus)

Abstract

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction→peptide alignment→ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binders were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the indentification of potential immunotherapeutic peptides. Availability: Software and data are available from the authors upon request. Contact: vladimir@@@wehi.edu.au.

Original languageEnglish
Pages (from-to)121-130
Number of pages10
JournalBioinformatics
Volume14
Issue number2
DOIs
Publication statusPublished - Jan 1 1998

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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