Predicting Protein-Ligand Interactions Based on Chemical Preference Features with its Application to New D-Amino Acid Oxidase Inhibitor Discovery

Author(s): Mingzhu Zhao, Hao-Teng Chang, Qiang Zhou, Tao Zeng, Chung-Shiuan Shih, Zhi-Ping Liu, Luonan Chen, Dong-Qing Wei

Journal Name: Current Pharmaceutical Design

Volume 20 , Issue 32 , 2014

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In silico prediction of the new drug-target interactions from existing databases is of important value for the drug discovery process. Currently, the amount of protein targets that have been identified experimentally is still very small compared with the entire human proteins. In order to predict protein-ligand interactions in an accurate manner, we have developed a support vector machine (SVM) model based on the chemical-protein interactions from STITCH. New features from ligand chemical space and interaction networks have been selected and encoded as the feature vectors for SVM analysis. Both the 5-fold cross validation and independent test show high predictive accuracy that outperforms the state-of-the-art method based on ligand similarity. Moreover, 91 distinct pairs of features have been selected to rebuild a simplifier model, which still maintains the same performance as that based on all 332 features. Then, this refined model is used to search for the potential D-amino acid oxidase inhibitors from STITCH database and the predicted results are finally validated by our wet experiments. Out of 10 candidates obtained, seven D-amino acid oxidase inhibitors have been verified, in which four are newly found in the present study, and one may have a new application in therapy of psychiatric disorders other than being an antineoplastic agent. Clearly, our model is capable of predicting potential new drugs or targets on a large scale with high efficiency.

Keywords: Protein-ligand interactions, prediction, chemical preference features, SVM, feature selection, fingerprint, MACCS key, D-amino acid oxidase inhibitors.

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Article Details

Year: 2014
Published on: 08 September, 2014
Page: [5202 - 5211]
Pages: 10
DOI: 10.2174/1381612820666140306104823
Price: $65

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