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トポロジカル自己相関関数を用いた蛋白質の構造安定性とキナーゼ及びプロテアーゼ阻害の予測
https://doi.org/10.18997/00003676
https://doi.org/10.18997/00003676fe6229d6-93b2-4260-9eaf-0ef9a428ad68
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
D-114_jou_k_250.pdf (5.4 MB)
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| Item type | 学位論文 = Thesis or Dissertation(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2013-03-19 | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||
| 資源タイプ | doctoral thesis | |||||||
| タイトル | ||||||||
| タイトル | Topological Autocorrelations for Prediction of Protein Conformational Stability and Kinase and Protease Inhibitions | |||||||
| 言語 | en | |||||||
| タイトル | ||||||||
| タイトル | トポロジカル自己相関関数を用いた蛋白質の構造安定性とキナーゼ及びプロテアーゼ阻害の予測 | |||||||
| 言語 | ja | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 著者 |
Fernandez, Llamosa Michael
× Fernandez, Llamosa Michael
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | The annotation of protein structure and function from sequence and the prediction of compound’s activity from sketch representations are fundamental goals in bio- and chemoinformatics. In the present study, fast and accurate predictors of protein conformational stability and kinase and protease inhibitions were built from graph representations of proteins and ligands. Firstly, Amino Acid Sequence Autocorrelation (AASA) vectors were computed from the Cα-carbon linear graph representation of a large dataset of protein mutants (>1000) from Protherm database.Genetic algorithm-optimized support vector machines (GA-SVM) were trained with AASA vectors to predict the real ΔΔG values with squared correlation coefficient of 0.45 and classify ΔΔG signs with accuracy of 80%. The stable mutants in the test set were recognized with accuracies of 70%. Secondly, AASA vectors and ligand’s autocorrelation features were computed from the linear graph representation of kinase and protease and from 2D molecular graphs collected from ProLINT database. SVMs trained with concatenated autocorrelation matrices yielded test set accuracies > 80% for kinase and protease targets. The inhibition predictors perform homogenously along the different kinase and protease families and ligands’ scaffolds. The predictors from sequences and sketch representations of ligands are online available at:http://gibk21.bse.kyutech.ac.jp/llamosa/ddG-AASA/ddG_AASA.html http://gibk21.bse.kyutech.ac.jp/AUTOkinI/SVMpredictor.html http://gibk21.bse.kyutech.ac.jp/AUTOprotI/SVMpredictor.html | |||||||
| 目次 | ||||||||
| 内容記述タイプ | TableOfContents | |||||||
| 内容記述 | Chapter 1. Introduction||Chapter 2. Datasets and Computational Methods||Chapter 3. Modeling of Protein Conformational Stability||Chapter 4. Modeling of Kinase Inhibition||Chapter 5. Modeling of Protease Inhibition||Chapter 6. Summary and Future Perspectives | |||||||
| 備考 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 九州工業大学博士学位論文 学位記番号:情工博甲第250号 学位授与年月日:平成23年3月25日 | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | QSAR | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Drug design | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Support vector machines | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Genetic algorithm | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Protein design | |||||||
| アドバイザー | ||||||||
| 皿井, 明倫 | ||||||||
| 学位授与番号 | ||||||||
| 学位授与番号 | 甲第250号 | |||||||
| 学位名 | ||||||||
| 学位名 | 博士（情報工学） | |||||||
| 学位授与年月日 | ||||||||
| 学位授与年月日 | 2011-03-25 | |||||||
| 学位授与機関 | ||||||||
| 学位授与機関識別子Scheme | kakenhi | |||||||
| 学位授与機関識別子 | 17104 | |||||||
| 学位授与機関名 | 九州工業大学 | |||||||
| 学位授与年度 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 平成22年度 | |||||||
| 出版タイプ | ||||||||
| 出版タイプ | VoR | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| ID登録 | ||||||||
| ID登録 | 10.18997/00003676 | |||||||
| ID登録タイプ | JaLC | |||||||
