Fuzzy neural network-based prediction of the motif for MHC class II binding peptides

Hideki Noguchi, Taizo Hanai, Hiroyuki Honda, Leonard C. Harrison, Takeshi Kobayashi

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Characterizing the interaction between major histocompatibility complex (MHC) molecules and antigenic peptides is critical for understanding immunity and developing immunotherapies for autoimmune diseases and cancer. To identify the peptide binding motif and predict peptides that bind to the human MHC classII molecule HLA-DR4(*0401), we applied a fuzzy neural network (FNN) capable of extracting the relationship between input and output. Analysis of the peptide binding motif revealed that the hydrophilicity of the position 1 residue located on the N-terminal side of the nonamer (9mer) was the most important variable and that the van der Waals volume and hydrophilicity of the position 6 residue and the hydrophilicity of the position 7 residue were also important variables. The estimation accuracy (AROC value) was high and the binding motif extracted from the FNN agreed with that derived experimentally. This study demonstrates that FNN modeling allows candidate antigenic peptides to be selected without the need for further experiments.

Original languageEnglish
Pages (from-to)227-231
Number of pages5
JournalJournal of Bioscience and Bioengineering
Volume92
Issue number3
DOIs
Publication statusPublished - Jan 1 2001
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

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