Prediction of Protein Folding Rates from Structural Topology and Complex Network Properties

• Item type: Trans(1)
• 公開日: 2010-05-12
• タイトル: Prediction of Protein Folding Rates from Structural Topology and Complex Network Properties
• タイトル / 言語: en
• タイトル: Prediction of Protein Folding Rates from Structural Topology and Complex Network Properties
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Original Papers
• 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: Bioinformatics Center, Institute for Chemical Research, Kyoto University / Department of Biochemistry and Molecular Biology, Monash University
• 著者所属: PRESTO, Japan Science and Technology Agency
• 著者所属: Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University
• 著者所属: Department of Biochemistry and Molecular Biology, Monash University
• 著者所属: Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology
• 著者所属: Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University / Department of Biochemistry and Molecular Biology, Monash University
• 著者所属(英): en
• 著者所属(英): PRESTO, Japan Science and Technology Agency
• 著者所属(英): en
• 著者所属(英): Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University
• 著者所属(英): en
• 著者所属(英): Department of Biochemistry and Molecular Biology, Monash University
• 著者所属(英): en
• 著者所属(英): Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者名: Jiangning, Song; Kazuhiro, Takemoto; Hongbin, Shen; Hao, Tan; M.MichaelGromiha; Tatsuya, Akutsu
• 著者名(英): Jiangning, Song; Kazuhiro, Takemoto; Hongbin, Shen; Hao, Tan; M., MichaelGromiha; Tatsuya, Akutsu
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: As a fundamental biological problem, revealing the protein folding mechanism remains to be one of the most challenging problems in structural bioinformatics. Prediction of protein folding rate is an important step towards our further understanding of the protein folding mechanism and the complex sequencestructure-function relationship. In this article, we develop a novel approach to predict protein folding rates for two-state and multi-state protein folding kinetics, which combines a variety of structural topology and complex network properties that are calculated from protein three-dimensional structures. To take into account the specific correlations between network properties and protein folding rates, we define two different protein residue contact networks, based on two different scales Protein Contact Network (PCN) and Long-range Interaction Network (LIN) to characterize the corresponding network features. The leave-one-out cross-validation (LOOCV) tests indicate that this integrative strategy is more powerful in predicting the folding rates from 3D structures, with the Pearson's Correlation Coefficient (CC) of 0.88, 0.90 and 0.90 for two-state, multi-state and combined protein folding kinetics, which provides an improved performance compared with other prediction work. This study provides useful insights which shed light on the network organization of interacting residues underlying protein folding process for both two-state and multi-state folding kinetics. Moreover, our method also provides a complementary approach to the current folding rate prediction algorithms and can be used as a powerful tool for the characterization of the foldomics protein data. The implemented webserver (termed PRORATE) is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/folding/.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: As a fundamental biological problem, revealing the protein folding mechanism remains to be one of the most challenging problems in structural bioinformatics. Prediction of protein folding rate is an important step towards our further understanding of the protein folding mechanism and the complex sequencestructure-function relationship. In this article, we develop a novel approach to predict protein folding rates for two-state and multi-state protein folding kinetics, which combines a variety of structural topology and complex network properties that are calculated from protein three-dimensional structures. To take into account the specific correlations between network properties and protein folding rates, we define two different protein residue contact networks, based on two different scales Protein Contact Network (PCN) and Long-range Interaction Network (LIN) to characterize the corresponding network features. The leave-one-out cross-validation (LOOCV) tests indicate that this integrative strategy is more powerful in predicting the folding rates from 3D structures, with the Pearson's Correlation Coefficient (CC) of 0.88, 0.90 and 0.90 for two-state, multi-state and combined protein folding kinetics, which provides an improved performance compared with other prediction work. This study provides useful insights which shed light on the network organization of interacting residues underlying protein folding process for both two-state and multi-state folding kinetics. Moreover, our method also provides a complementary approach to the current folding rate prediction algorithms and can be used as a powerful tool for the characterization of the foldomics protein data. The implemented webserver (termed PRORATE) is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/folding/.
• 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. 40-53, 発行日 2010-05-12
• 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