Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules

Hideki Noguchi, Ryuji Kato, Taizo Hanai, Yukari Matsubara, Hiroyuki Honda, Vladimir Brusic, Takeshi Kobayashi

Research output: Contribution to journalArticle

66 Citations (Scopus)

Abstract

Elucidating the interaction between major histocompatibility complex (MHC) molecules and antigenic peptides is fundamental to better understanding of the processes involved in immune responses and for the development of innovative immunotherapies. In the present study, hidden Markov models (HMM) were combined with the successive state splitting (SSS) algorithm for optimization of the HMM structure, to predict peptide binders to the human MHC class II molecule HLA-DRBI*0101. The predictive performance of our model (S-HMM) was compared with fully connected HMM and artificial neural network (ANN) methods using the relative operating characteristic (ROC) analysis. The S-HMM predictions had values of ROC≥0.85 which was at least as good, or better than the comparison methods. In addition, S-HMM is trained on positive data only and does not require exhaustive data preprocessing, such as peptide alignment. Our results demonstrated that S-HMM combines the high accuracy of predictions with the simplicity of implementation and is therefore useful for analyzing MHC class II binding peptides. In particular the S-HMM may be trained using only positive data and, the preprocessing of training data, such as peptide alignment and the selection of binding cores, is not required in this method.

Original languageEnglish
Pages (from-to)264-270
Number of pages7
JournalJournal of Bioscience and Bioengineering
Volume94
Issue number3
DOIs
Publication statusPublished - Jan 1 2002

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Keywords

  • Binding peptides
  • Bioinformatics
  • Hidden Markov model
  • MHC class II
  • Successive state splitting

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

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