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

研究成果: ジャーナルへの寄稿記事

66 引用 (Scopus)

抄録

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.

元の言語英語
ページ(範囲)264-270
ページ数7
ジャーナルJournal of Bioscience and Bioengineering
94
発行部数3
DOI
出版物ステータス出版済み - 1 1 2002
外部発表Yes

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Hidden Markov models
Major Histocompatibility Complex
Peptides
Molecules
Neural Networks (Computer)
Immunotherapy
Model structures
Binders
Neural networks

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

これを引用

Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. / Noguchi, Hideki; Kato, Ryuji; Hanai, Taizo; Matsubara, Yukari; Honda, Hiroyuki; Brusic, Vladimir; Kobayashi, Takeshi.

:: Journal of Bioscience and Bioengineering, 巻 94, 番号 3, 01.01.2002, p. 264-270.

研究成果: ジャーナルへの寄稿記事

Noguchi, Hideki ; Kato, Ryuji ; Hanai, Taizo ; Matsubara, Yukari ; Honda, Hiroyuki ; Brusic, Vladimir ; Kobayashi, Takeshi. / Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. :: Journal of Bioscience and Bioengineering. 2002 ; 巻 94, 番号 3. pp. 264-270.
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