Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

Human cytochrome P450 3A4 (CYP3A4) is a major drug-metabolizing enzyme responsible for the metabolism of ∼50% of clinically used drugs and is often involved in drug–drug interactions. It exhibits atypical binding and kinetic behavior toward many ligands. Binding of ligands to CYP3A4 is a complex process. Recent studies from both crystallography and biochemistry suggested the existence of a peripheral ligand-binding site at the enzyme surface. However, the stability of the ligand bound at this peripheral site and the possibility of discovering new CYP3A4 ligands based on this site remain unclear. In this study, we employed a combination of molecular docking, multiparalleled molecular dynamics (MD) simulations, virtual screening, and experimental bioassay to investigate these issues. Our results revealed that the binding mode of progesterone (PGS), a substrate of CYP3A4, in the crystal structure was not stable and underwent a significant conformational change. Through Glide docking and MD refinement, it was found that PGS was able to stably bind at the peripheral site via contacts with Phe215, Phe219, Phe220, and Asp214. On the basis of the refined peripheral site, virtual screening was then performed against the Enamine database. A total of three compounds were finally found to have inhibitory activity against CYP3A4 in both human liver microsome and recombinant human CYP3A4 enzyme assays, one of which showed potent inhibitory activity with IC50 lower than 1 μM and two of which exhibited moderate inhibitory activity with IC50 values lower than 10 μM. The findings not only presented the dynamic behavior of PGS at the peripheral site but also demonstrated the first indication of discovering CYP3A4 inhibitors based on the peripheral site.

Structure-based prediction of sites of metabolism (SOMs) mediated by cytochrome P450s (CYPs) is of great interest in drug discovery and development. However, protein flexibility and active site water molecules remain a challenge for accurate SOM prediction. CYP2C19 is one of the major drug-metabolizing enzymes and has attracted considerable attention because of its polymorphism and capability of metabolizing ∼7% clinically used drugs. In this study, we systematically evaluated the effects of protein flexibility and active site water molecules on SOM prediction for CYP2C19 substrates. Multiple conformational sampling techniques including GOLD flexible residues sampling, molecular dynamics (MD) and tCONCOORD side-chain sampling were adopted for assessing the influence of protein flexibility on SOM prediction. The prediction accuracy could be significantly improved when protein flexibility was considered using the tCONCOORD sampling method, which indicated that the side-chain conformation was important for accurate prediction. However, the inclusion of the crystallographic or MD-derived water molecule(s) does not necessarily improve the prediction accuracy. Finally, a combination of docking results with SMARTCyp was found to be able to increase the SOM prediction accuracy.

Cytochrome P450 2C19 (CYP2C19) is one of 57 drug metabolizing enzymes in humans and is responsible for the metabolism of ∼7–10% of drugs in clinical use. Recently omeprazole-based analogues were reported to be the potent inhibitors of CYP2C19 and have the potential to be used as the tool compounds for studying the substrate selectivity of CYP2C19. However, the binding modes of these compounds with CYP2C19 remain to be elucidated. In this study, a combination of molecular docking, molecular dynamics (MD), and MM/GBSA calculations was employed to systematically investigate the interactions between these compounds and CYP2C19. The binding modes of these analogues were analyzed in detail. The results indicated that the inclusion of explicit active site water molecules could improve binding energy prediction when the water molecules formed a hydrogen bonding network between the ligand and protein. We also found that the effect of active site water molecules on binding free energy prediction was dependent on the ligand binding modes. Our results unravel the interactions of these omeprazole-based analogues with CYP2C19 and might be helpful for the future design of potent CYP2C19 inhibitors with improved metabolic properties.

Human aromatase, also known as cytochrome P450 19A1, specifically catalyzes the conversion of androgens to estrogens, and therefore represents an important drug target for the treatment of breast cancer. Recently, azole compounds previously used as agricultural fungicides and antimycotic drugs were reported to exhibit potent inhibitory activity against aromatase. However, the molecular mechanism of these azole compounds against aromatase remains unclear. In this study, a combination of molecular docking and several types of molecular dynamics (MD) simulations including conventional MD, random acceleration MD and steered MD, was employed to investigate the interactions of aromatase with letrozole and imazalil, two azole compounds with distinct inhibitory activities against aromatase. The binding modes of these two inhibitors were obtained by molecular docking and refined by MD simulation. The binding free energies were calculated based on the MD snapshots by using the MM-GBSA method and were found to be in agreement with the relative potency of the experimental binding affinities. Our results further demonstrated that these inhibitors had different favorable unbinding pathways in aromatase, and the unbinding manners differed in their favorable dissociation routes. Several residues lining the pathways were found important for the inhibitor egress. These findings would be helpful not only for understanding the inhibitory mechanism of azole compounds against aromatase, but also for designing new aromatase inhibitors.

•Structure-based method was used for SOM prediction of CYP2A6 substrates.•Effects of CYP2A6 protein flexibility on SOM prediction for its substrates were explored.•The snapshot structures from MD simulations exhibited higher prediction accuracy than the crystal structures.•Prediction accuracy for the low Km substrates is comparable to that by ligand-based methods.Structure-based prediction for the site of metabolism (SOM) of a compound metabolized by human cytochrome P450s (CYPs) is highly beneficial in drug discovery and development. However, the flexibility of the CYPs’ active site remains a huge challenge for accurate SOM prediction. Compared with other CYPs, the active site of CYP2A6 is relatively small and rigid. To address the impact of the flexibility of CYP2A6 active site residues on the SOM prediction for substrates, in this work, molecular dynamics (MD) simulations and molecular docking were used to predict the SOM of 96 CYP2A6 substrates. Substrates with known SOM were docked into the snapshot structures from MD simulations and the crystal structures of CYP2A6. Compared to the crystal structures, the protein structures obtained from MD simulations showed more accurate prediction for SOM. Our results indicated that the flexibility of the active site of CYP2A6 significantly affects the SOM prediction results. Further analysis for the 40 substrates with definite Km values showed that the prediction accuracy for the low Km substrates is comparable to that by ligand-based methods.

Farnesol X receptor (FXR) is a member of the metabolic nuclear receptor (NR) superfamily of regulatory proteins. FXR was recognized to be a transcriptional sensor for bile acids, and now it has been shown that activating FXR has important roles in controlling bile acid homeostasis, lipoprotein and glucose metabolism, and hepatic regeneration. For the sake of discovering new, potent non-steroidal FXR ligands, we have established a virtual screening workflow by using Phase Shape and induced fit docking (IFD). Phase shape was performed based on a combination of shape-only and atom types or pharmacophore modes. The results indicated that the pharmacophore mode yielded the best result for our system. The best receptor model was chosen by evaluating the cross-IFD models induced by three crystal structures 3DCT, 3FLI and 3OKI. The Enamine database was screened by the proposed workflow and 50 molecules were selected and purchased for bioassays. Among them, two compounds were found to be the new, potent FXR ligands in cell-based assay.Discovery of two new, potent FXR ligands by ligand- and receptor-based methods.

Cytochrome P450 inhibitory promiscuity of a drug has potential effects on the occurrence of clinical drug–drug interactions. Understanding how a molecular property is related to the P450 inhibitory promiscuity could help to avoid such adverse effects. In this study, an entropy-based index was defined to quantify the P450 inhibitory promiscuity of a compound based on a comprehensive data set, containing more than 11,500 drug-like compounds with inhibition against five major P450 isoforms, 1A2, 2C9, 2C19, 2D6, and 3A4. The results indicated that the P450 inhibitory promiscuity of a compound would have a moderate correlation with molecular aromaticity, a minor correlation with molecular lipophilicity, and no relations with molecular complexity, hydrogen bonding ability, and TopoPSA. We also applied an index to quantify the susceptibilities of different P450 isoforms to inhibition based on the same data set. The results showed that there was a surprising level of P450 inhibitory promiscuity even for substrate specific P450, susceptibility to inhibition follows the rank-order: 1A2 > 2C19 > 3A4 > 2C9 > 2D6. There was essentially no correlation between P450 inhibitory potency and specificity and minor negative trade-offs between P450 inhibitory promiscuity and catalytic promiscuity. In addition, classification models were built to predict the P450 inhibitory promiscuity of new chemicals using support vector machine algorithm with different fingerprints. The area under the receiver operating characteristic curve of the best model was about 0.9, evaluated by 5-fold cross-validation. These findings would be helpful for understanding the mechanism of P450 inhibitory promiscuity and improving the P450 inhibitory selectivity of new chemicals in drug discovery.

Adverse side effects of drug–drug interactions induced by human cytochrome P450 (CYP) inhibition is an important consideration, especially, during the research phase of drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP isoform. In this study, inhibitor predicting models were developed for five major CYP isoforms, namely 1A2, 2C9, 2C19, 2D6, and 3A4, using a combined classifier algorithm on a large data set containing more than 24,700 unique compounds, extracted from PubChem. The combined classifiers algorithm is an ensemble of different independent machine learning classifiers including support vector machine, C4.5 decision tree, k-nearest neighbor, and naïve Bayes, fused by a back-propagation artificial neural network (BP-ANN). All developed models were validated by 5-fold cross-validation and a diverse validation set composed of about 9000 diverse unique compounds. The range of the area under the receiver operating characteristic curve (AUC) for the validation sets was 0.764 to 0.815 for CYP1A2, 0.837 to 0.861 for CYP2C9, 0.793 to 0.842 for CYP2C19, 0.839 to 0.886 for CYP2D6, and 0.754 to 0.790 for CYP3A4, respectively, using the new developed combined classifiers. The overall performance of the combined classifiers fused by BP-ANN was superior to that of three classic fusion techniques (Mean, Maximum, and Multiply). The chemical spaces of data sets were explored by multidimensional scaling plots, and the use of applicability domain improved the prediction accuracies of models. In addition, some representative substructure fragments differentiating CYP inhibitors and noninhibitors were characterized by the substructure fragment analysis. These classification models are applicable for virtual screening of the five major CYP isoforms inhibitors or can be used as simple filters of potential chemicals in drug discovery.

Over the past decade, absorption, distribution, metabolism, and excretion (ADME) property evaluation has become one of the most important issues in the process of drug discovery and development. Since in vivo and in vitro evaluations are costly and laborious, in silico techniques had been widely used to estimate ADME properties of chemical compounds. Traditional prediction methods usually try to build a functional relationship between a set of molecular descriptors and a given ADME property. Although traditional methods have been successfully used in many cases, the accuracy and efficiency of molecular descriptors must be concerned. Herein, we report a new classification method based on substructure pattern recognition, in which each molecule is represented as a substructure pattern fingerprint based on a predefined substructure dictionary, and then a support vector machine (SVM) algorithm is applied to build the prediction model. Therefore, a direct connection between substructures and molecular properties is built. The most important substructure patterns can be identified via the information gain analysis, which could help to interpret the models from a medicinal chemistry perspective. Afterward, this method was verified with two data sets, one for blood-brain barrier (BBB) penetration and the other for human intestinal absorption (HIA). The results demonstrated that the overall predictive accuracies of the best HIA model for the training and test sets were 98.5 and 98.8%, and the overall predictive accuracies of the best BBB model for the training and test sets were 98.8 and 98.4%, which confirmed the reliability of our method. In the additional validations, the predictive accuracies were 94 and 69.5% for the HIA and the BBB models, respectively. Moreover, some of the representative key substructure patterns which significantly correlated with the HIA and BBB penetration properties were also presented.