Epitope Prediction of Antigen Protein Using Attention-based LSTM Network

• Item type: Journal(1)
• 公開日: 2021-04-15
• タイトル: Epitope Prediction of Antigen Protein Using Attention-based LSTM Network
• タイトル / 言語: en
• タイトル: Epitope Prediction of Antigen Protein Using Attention-based LSTM Network
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] B-cell epitope prediction, protein, amino acid sequence, epitope, LSTM, attention
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Future Corporation
• 著者所属: Future Corporation
• 著者所属: Future Corporation
• 著者所属: Future Corporation
• 著者所属: FunPep Co., Ltd.
• 著者所属: FunPep Co., Ltd.
• 著者所属: Department of Health Development, Osaka University Graduate School of Medicine
• 著者所属(英): en
• 著者所属(英): Future Corporation
• 著者所属(英): en
• 著者所属(英): Future Corporation
• 著者所属(英): en
• 著者所属(英): Future Corporation
• 著者所属(英): en
• 著者所属(英): Future Corporation
• 著者所属(英): en
• 著者所属(英): FunPep Co., Ltd.
• 著者所属(英): en
• 著者所属(英): FunPep Co., Ltd.
• 著者所属(英): en
• 著者所属(英): Department of Health Development, Osaka University Graduate School of Medicine
• 著者名: Toshiaki, Noumi; Seiichi, Inoue; Haruka, Fujita; Kugatsu, Sadamitsu; Makoto, Sakaguchi; Akiko, Tenma; Hironori, Nakagami
• 著者名(英): Toshiaki, Noumi; Seiichi, Inoue; Haruka, Fujita; Kugatsu, Sadamitsu; Makoto, Sakaguchi; Akiko, Tenma; Hironori, Nakagami
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the subregions (epitope regions) of antigen proteins. These antibodies can inhibit the functioning of antigen proteins. Predicting epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production. However, prediction accuracy requires improvement. The conventional epitope region prediction methods have focused only on the target sequence in the amino acid sequences of an entire antigen protein and have not thoroughly considered its sequence and features as a whole. In the present paper, we propose a deep learning method based on long short-term memory with an attention mechanism to consider the characteristics of a whole antigen protein in addition to the target sequence. The proposed method achieves better accuracy compared with the conventional method in the experimental prediction of epitope regions using the data from the immune epitope database.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.321; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the subregions (epitope regions) of antigen proteins. These antibodies can inhibit the functioning of antigen proteins. Predicting epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production. However, prediction accuracy requires improvement. The conventional epitope region prediction methods have focused only on the target sequence in the amino acid sequences of an entire antigen protein and have not thoroughly considered its sequence and features as a whole. In the present paper, we propose a deep learning method based on long short-term memory with an attention mechanism to consider the characteristics of a whole antigen protein in addition to the target sequence. The proposed method achieves better accuracy compared with the conventional method in the experimental prediction of epitope regions using the data from the immune epitope database.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.321; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 62, 号 4, 発行日 2021-04-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764