Computational Identification of Discriminating Features of Pathogenic and Symbiotic Type III Secreted Effector Proteins

• Item type: Trans(1)
• 公開日: 2010-12-22
• タイトル: Computational Identification of Discriminating Features of Pathogenic and Symbiotic Type III Secreted Effector Proteins
• タイトル / 言語: en
• タイトル: Computational Identification of Discriminating Features of Pathogenic and Symbiotic Type III Secreted Effector Proteins
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Original Paper
• 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: Division of Biostatistics, Kurume University School of Medicine／Division of Infectious Diseases, Kurume University School of Medicine／Life Science Systems Department, Fujitsu Kyushu Systems Ltd.
• 著者所属: Division of Biostatistics, Kurume University School of Medicine
• 著者所属: Biostatistics Center, Kurume University
• 著者所属(英): en
• 著者所属(英): Division of Biostatistics, Kurume University School of Medicine / Division of Infectious Diseases, Kurume University School of Medicine / Life Science Systems Department, Fujitsu Kyushu Systems Ltd.
• 著者所属(英): en
• 著者所属(英): Division of Biostatistics, Kurume University School of Medicine
• 著者所属(英): en
• 著者所属(英): Biostatistics Center, Kurume University
• 著者名: Koji, Yahara; Ying, Jiang; Takashi, Yanagawa
• 著者名(英): Koji, Yahara; Ying, Jiang; Takashi, Yanagawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Type III secretion systems (T3SS) deliver bacterial proteins, or “effectors”, into eukaryotic host cells, inducing physiological responses in the hosts. Effector proteins have been considered virulence factors of pathogenic bacteria, but T3SSs have now been found in symbiotic bacteria as well. Whether any physicochemical difference exists between the two types of effectors remains unknown. In this work, we combined computational statistical and machine learning methods to identify features that could be responsible for the difference. For computational statistical method we used generalized Bayesian information criterion and kernel logistic regression, and for machine learning method we used support vector machine. It was clearly shown that differences in amino acid composition exist between pathogenic and symbiotic effector proteins. All identified discriminating features were those of amino acid composition and average residue weight, and their classification performance could be nearly identical to that using all physicochemical features, with sensitivity and specificity of over 80%. Further analysis on the seven discriminating features by graphical modeling revealed three dominant features among them. Moreover, amino acid regions that were distinctive for the seven features were explored by sliding window analysis. This study provides a methodological basis and important insights into the functional differences between pathogenic and symbiotic T3SS effectors.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Type III secretion systems (T3SS) deliver bacterial proteins, or “effectors”, into eukaryotic host cells, inducing physiological responses in the hosts. Effector proteins have been considered virulence factors of pathogenic bacteria, but T3SSs have now been found in symbiotic bacteria as well. Whether any physicochemical difference exists between the two types of effectors remains unknown. In this work, we combined computational statistical and machine learning methods to identify features that could be responsible for the difference. For computational statistical method we used generalized Bayesian information criterion and kernel logistic regression, and for machine learning method we used support vector machine. It was clearly shown that differences in amino acid composition exist between pathogenic and symbiotic effector proteins. All identified discriminating features were those of amino acid composition and average residue weight, and their classification performance could be nearly identical to that using all physicochemical features, with sensitivity and specificity of over 80%. Further analysis on the seven discriminating features by graphical modeling revealed three dominant features among them. Moreover, amino acid regions that were distinctive for the seven features were explored by sliding window analysis. This study provides a methodological basis and important insights into the functional differences between pathogenic and symbiotic T3SS effectors.
• 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: AA12177013
• 書誌情報: IPSJ Transactions on Bioinformatics（TBIO）; 巻 3, p. 95-107, 発行日 2010-12-09
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6679
• 出版者 / 言語: ja
• 出版者: 情報処理学会