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

Authors
  • Noguchi, Hideki
  • Kato, Ryuji
  • Hanai, Taizo
  • Matsubara, Yukari
  • Honda, Hiroyuki
  • Brusic, Vladimir
  • Kobayashi, Takeshi
Type
Published Article
Journal
Journal of Bioscience and Bioengineering
Publisher
Elsevier
Publication Date
Jan 01, 2002
Volume
94
Issue
3
Pages
264–270
Identifiers
PMID: 16233301
Source
Medline
License
Unknown

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-DRB1*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 > or = 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.

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