2018年7月
Prediction of Protein-compound Binding Energies from Known Activity Data: Docking-score-based Method and its Applications.
Molecular informatics
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- ,
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- 巻
- 37
- 号
- 6-7
- 開始ページ
- e1700120
- 終了ページ
- 記述言語
- 英語
- 掲載種別
- DOI
- 10.1002/minf.201700120
We used protein-compound docking simulations to develop a structure-based quantitative structure-activity relationship (QSAR) model. The prediction model used docking scores as descriptors. The binding free energy was approximated by a weighted average of docking scores for multiple proteins. This approximation was based on a pharmacophore model of receptor pockets and compounds. The weights of the docking scores were restricted to small values to avoid unrealistic weights by a regularization term. Additional outlier elimination improved the results. We applied this method to two groups of targets. The first target was the kinase family. The cross-validation results of 107 kinase proteins showed that the RMSE of predicted binding free energies was 1.1 kcal/mol. The second target was the matrix metalloproteinase (MMP) family, which has been difficult for docking programs. MMPs require metal-binding groups in their inhibitor structures in many cases. A quantum effect contributes to the metal-ligand interaction. Despite this difficulty, the present method worked well for the MMPs. This method showed that the RMSE of predicted binding free energies was 1.1 kcal/mol. In comparison, with the original docking method the RMSE was 1.7 kcal/mol. The results suggest that the present QSAR model should be applied to general target proteins.
- リンク情報
- ID情報
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- DOI : 10.1002/minf.201700120
- PubMed ID : 29442436
- PubMed Central 記事ID : PMC6055825