Identification of the drug-binding residues on the surface of proteins is a vital step in drug discovery and it is important for understanding protein function. Most previous researches are based on the structural inf...Identification of the drug-binding residues on the surface of proteins is a vital step in drug discovery and it is important for understanding protein function. Most previous researches are based on the structural information of proteins, but the structures of most proteins are not available. So in this article, a sequence-based method was proposed by combining the support vector machine (SVM)-based ensemble learning and the improved position specific scoring matrix (PSSM). In order to take the local environment information of a drug-binding site into account, an improved PSSM profile scaled by the sliding window and smoothing window was used to improve the prediction result. In addition, a new SVM-based ensemble learning method was developed to deal with the imbalanced data classification problem that commonly exists in the binding site predictions. When performed on the dataset of 985 drug-binding residues, the method achieved a very promising prediction result with the area under the curve (AUC) of 0.9264. Furthermore, an independent dataset of 349 drug- binding residues was used to evaluate the pre- diction model and the prediction accuracy is 84.68%. These results suggest that our method is effective for predicting the drug-binding sites in proteins. The code and all datasets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/Ensem_DBS.zip.展开更多
Current FDA-approved kinase inhibitors cause diverse adverse effects,some of which are due to the me-chanism-independent effects of these drugs.Identifying these mechanism-independent interactions could improve drug s...Current FDA-approved kinase inhibitors cause diverse adverse effects,some of which are due to the me-chanism-independent effects of these drugs.Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing.Here,we develop iDTPnd(integrated Drug Target Predictor with negative dataset),a computational approach for large-scale discovery of novel targets for known drugs.For a given drug,we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites.To facilitate assessment of unintended targets,iDTPnd also provides a docking-based interaction score and its statistical significance.We confirm the interactions of sorafenib,imatinib,dasatinib,sunitinib,and pazopanib with their known targets at a sensitivity of 52%and a specificity of 55%.We also validate 10 predicted novel targets by using in vitro experiments.Our results suggest that proteins other than kinases,such as nuclear receptors,cytochrome P450,and MHC class I molecules,can also be physiologically relevant targets of kinase inhibitors.Our method is general and broadly applicable for the identification of protein–small molecule interactions,when sufficient drug–target 3D data are available.The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.展开更多
文摘Identification of the drug-binding residues on the surface of proteins is a vital step in drug discovery and it is important for understanding protein function. Most previous researches are based on the structural information of proteins, but the structures of most proteins are not available. So in this article, a sequence-based method was proposed by combining the support vector machine (SVM)-based ensemble learning and the improved position specific scoring matrix (PSSM). In order to take the local environment information of a drug-binding site into account, an improved PSSM profile scaled by the sliding window and smoothing window was used to improve the prediction result. In addition, a new SVM-based ensemble learning method was developed to deal with the imbalanced data classification problem that commonly exists in the binding site predictions. When performed on the dataset of 985 drug-binding residues, the method achieved a very promising prediction result with the area under the curve (AUC) of 0.9264. Furthermore, an independent dataset of 349 drug- binding residues was used to evaluate the pre- diction model and the prediction accuracy is 84.68%. These results suggest that our method is effective for predicting the drug-binding sites in proteins. The code and all datasets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/Ensem_DBS.zip.
基金supported by funding from King Abdullah University of Science and Technology,Office of Sponsored Research(Grant No.FCC/1/1976-25).
文摘Current FDA-approved kinase inhibitors cause diverse adverse effects,some of which are due to the me-chanism-independent effects of these drugs.Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing.Here,we develop iDTPnd(integrated Drug Target Predictor with negative dataset),a computational approach for large-scale discovery of novel targets for known drugs.For a given drug,we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites.To facilitate assessment of unintended targets,iDTPnd also provides a docking-based interaction score and its statistical significance.We confirm the interactions of sorafenib,imatinib,dasatinib,sunitinib,and pazopanib with their known targets at a sensitivity of 52%and a specificity of 55%.We also validate 10 predicted novel targets by using in vitro experiments.Our results suggest that proteins other than kinases,such as nuclear receptors,cytochrome P450,and MHC class I molecules,can also be physiologically relevant targets of kinase inhibitors.Our method is general and broadly applicable for the identification of protein–small molecule interactions,when sufficient drug–target 3D data are available.The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.
