Abstract
Disruption of epigenetic processes to eradicate tumor cells is among the most promising interventions for cancer control. EZH2 (Enhancer of zeste homolog 2), a catalytic component of polycomb repressive complex 2 (PRC2), methylates lysine 27 of histone H3 to promote transcriptional silencing and is an important drug target for controlling cancer via epigenetic processes. In the present study, we have developed various predictive models for modeling the inhibitory activity of EZH2. Binary and multiclass models were built using SVM, random forest and XGBoost methods. Rigorous validation approaches including predictiveness curve, Y-randomization and applicability domain (AD) were employed for evaluation of the developed models. Eighteen descriptors selected from Boruta methods have been used for modeling. For binary classification, random forest and XGBoost achieved an accuracy of 0.80 and 0.82, respectively, on external test set. Contrastingly, for multiclass models, random forest and XGBoost achieved an accuracy of 0.73 and 0.75, respectively. 500 Y-randomization runs demonstrate that the models were robust and the correlations were not by chance. Evaluation metrics from predictiveness curve show that the selected eighteen descriptors predict active compounds with total gain (TG) of 0.79 and 0.59 for XGBoost and random forest, respectively. Validated models were further used for virtual screening and molecular docking in search of potential hits. A total of 221 compounds were commonly predicted as active with above the set probability threshold and also under the AD of training set. Molecular docking revealed that three compounds have reasonable binding energy and favorable interactions with critical residues in the active site of EZH2. In conclusion, we highlighted the potential of rigorously validated models for accurately predicting and ranking the activities of lead molecules against cancer epigenetic targets. The models presented in this study represent the platform for development of EZH2 inhibitors.
Highlights
Epigenetic mechanisms are crucial for normal development and maintenance of tissue-specific gene expression [1]
Polycomb repressive complex 2 (PRC2) is a histone methyltransferase complex composed of core subunits Enhancer of zeste homolog 2 (EZH2), EED, Suz12 and Rbbp4, forming a stable and enzymatically active methyltransferase complex [2,3,4,5,6]
The binary and multiclass models were trained using three machine learning methods (SVM, random forest and XGBOOST) along with all descriptors set and descriptors selected from Boruta’s method [29]
Summary
Epigenetic mechanisms are crucial for normal development and maintenance of tissue-specific gene expression [1]. Pharmaceuticals 2021, 14, 699 gene function and malignant cellular transformation [1,2]. Polycomb repressive complex 2 (PRC2) is a histone methyltransferase complex composed of core subunits EZH2, EED, Suz and Rbbp, forming a stable and enzymatically active methyltransferase complex [2,3,4,5,6]. Enhancer of zeste homolog 2 (EZH2) is the catalytic component of polycomb repressive complex 2 (PRC2), that exhibits an intrinsic trimethylation activity on lysine. Different types of EZH2 inhibitors have been developed; most of them are under evaluation in clinical trials [11,12,13,14]
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