Structure-Based Understanding of Binding Affinity and Mode of Estrogen Receptor α Agonists and Antagonists

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures

Cancer, a life-disturbing and lethal disease with a high global impact, causes significant economic, social, and health challenges. Breast cancer refers to the abnormal growth of cells originating from breast tissues. Hormone-dependent forms of breast cancer, such as those influenced by estrogen, prompt the exploration of estrogen receptors as targets for potential therapeutic interventions. In this study, we conducted e-QSAR molecular docking and molecular dynamics analyses on a diverse set of inhibitors targeting estrogen receptor alpha (ER-α). The e-QSAR model is based on a genetic algorithm combined with multilinear regression analysis. The newly developed model possesses a balance between predictive accuracy and mechanistic insights adhering to the OECD guidelines. The e-QSAR model pointed out that sp2-hybridized carbon and nitrogen atoms are important atoms governing binding profiles. In addition, a specific combination of H-bond donors and acceptors with carbon, nitrogen, and ring sulfur atoms also plays a crucial role. The results are supported by molecular docking, MD simulations, and X-ray-resolved structures. The novel results could be useful for future drug development for ER-α.

Urolithin A: A promising selective estrogen receptor modulator and 27-hydroxycholesterol attenuator in breast cancer

27-hydroxycholesterol (27-HC) is an oxysterol that acts as an endogenous selective estrogen receptor modulator (SERM), and its adverse effects on breast cancer via the estrogen receptor (ER) have provided new insights into the pathology of cholesterol-linked breast cancer. Our earlier in vitro experiments showed that the methanolic extract of pomegranate could exhibit SERM properties and compete with 27-HC. The major constituents of pomegranate are ellagitannins and ellagic acid, which are converted into urolithins by the colonic microbiota. In recent years, urolithins, especially urolithin A (UA) and urolithin B (UB), have been reported to have a plethora of advantageous effects, including antiproliferative and estrogenic activities. In this study, we attempted to determine the potential of urolithins in antagonizing and counteracting the adverse effects of 27-HC in breast cancer cells. Our findings suggested that UA had an antiproliferative capacity and attenuated the proliferative effects of 27-HC, resulting in subsequent loss of membrane potential and apoptosis in breast cancer cells. Further, UA induced estrogen response element (ERE) transcriptional activity and modulated estrogen-responsive genes, exhibiting a SERM-like response concerning receptor binding. Our in vivo hollow fiber assay results showed a loss of cell viability in breast cancer cells upon UA consumption, as well as a reduction in 27-HC-induced proliferative activity. Additionally, it was shown that UA did not induce uterine proliferation or alter blood biochemical parameters. Based on these findings, we can conclude that UA has the potential to act as a potent estrogen receptor alpha (ERα) modulator and 27-HC antagonist. UA is safe to consume and is very well tolerated. This study further opens up the potential of UA as ER modulator and its benefits in estrogen-dependent tissues.

Species Differences in Response to Binding Interactions of Bisphenol A and its Analogs with the Modeled Estrogen Receptor 1 and In Vitro Reporter Gene Assay in Human and Zebrafish

Adverse impacts associated with the interactions of numerous endocrine-disruptor chemicals (EDCs) with estrogen receptor 1 play a pivotal role in reproductive dysfunction. The predictive studies on these interactions thus are crucial in the risk assessment of EDCs but rely heavily on the accuracy of specific protein structure in three dimensions. As the three-dimensional (3D) structure of zebrafish estrogen receptor 1 (zEsr1) is not available, the 3D structure of zEsr1 ligand-binding domain (zEsr1-LBD) was generated using MODELLER and its quality was assessed by the PROCHECK, ERRAT, ProSA, and Verify-3D tools. After the generated model was verified as reliable, bisphenol A and its analogs were docked on the zEsr1-LBD and human estrogen receptor 1 ligand-binding domain (hESR1-LBD) using the Discovery Studio and Autodock Vina programs. The molecular dynamics followed by molecular docking were simulated using the Nanoscale Molecular Dynamics program and compared to those of the in vitro reporter gene assays. Some chemicals were bound with an orientation similar to that of 17β-estradiol in both models and in silico binding energies showed moderate or high correlations with in vitro results (0.33 ≤ r²  ≤ 0.71). Notably, hydrogen bond occupancy during molecular dynamics simulations exhibited a high correlation with in vitro results (r²  ≥ 0.81) in both complexes. These results show that the combined in silico and in vitro approaches is a valuable tool for identifying EDCs in different species, facilitating the assessment of EDC-induced reproductive toxicity. Environ Toxicol Chem 2022;41:2431-2443. © 2022 SETAC.

7,7″-Dimethoxyagastisflavone Inhibits Proinflammatory Cytokine Release and Inflammatory Cell Recruitment through Modulating ERα Signaling

Acute systemic inflammatory diseases, including sepsis, usually result in cytokine disorder and multiple-organ failure. 7,7″-Dimethoxyagastisflavone (DMGF), a biflavonoid isolated from the needles of Taxus x media var. Hicksii, has previously been evaluated for its antiproliferative and antineoplastic effects in cancer cells. In this study, the effects of DMGF on the cytokine production and cell migration of inflammatory macrophages were investigated. The inhibition of cytokine and chemokine production by DMGF in LPS-treated macrophages was analyzed by a multiplex cytokine assay. Then, the integrin molecules used for cell adhesion and regulators of actin polymerization were observed by RT-PCR and recorded using confocal imaging. The DMGF interaction with estrogen receptor α (ERα) was modeled structurally by molecular docking and validated by an ERα reporter assay. DMGF inhibited TNF-α, IL-1β, and IL-6 production in LPS-induced macrophages. DMGF also inhibited inflammatory macrophage migration by downregulating the gene and protein expression of adhesion molecules (LFA-1 and VLA4) and regulators of actin assembly (Cdc42-Rac1 pathway). DMGF might interact with the ligand-binding domain of ERα and downregulate its transcriptional activity. These results indicated that DMGF effectively inhibited the production of proinflammatory cytokines and the recruitment of inflammatory cells through downregulating ERα signaling.

ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists

Estrogen receptors alpha and beta (ERα and ERβ) are responsible for breast cancer metastasis through their involvement of clinical outcomes. Estradiol and hormone replacement therapy targets both ERs, but this often leads to an increased risk of breast and endometrial cancers as well as thromboembolism. A major challenge is posed for the development of compounds possessing ER subtype specificity. Herein, we present a large-scale classification structure-activity relationship (CSAR) study of inhibitors from the ChEMBL database which consisted of an initial set of 11,618 compounds for ERα and 7,810 compounds for ERβ. The IC50 was selected as the bioactivity unit for further investigation and after the data curation process, this led to a final data set of 1,593 and 1,281 compounds for ERα and ERβ, respectively. We employed the random forest (RF) algorithm for model building and of the 12 fingerprint types, models built using the PubChem fingerprint was the most robust (Ac of 94.65% and 92.25% and Matthews correlation coefficient (MCC) of 89% and 76% for ERα and ERβ, respectively) and therefore selected for feature interpretation. Results indicated the importance of features pertaining to aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. Finally, the model was deployed as the publicly available web server called ERpred at http://codes.bio/erpred where users can submit SMILES notation as the input query for prediction of the bioactivity against ERα and ERβ.

Exploring the Potential of ToxCast Data in Supporting Read-Across for Evaluation of Food Chemical Safety

The intention of this study was to determine the utility of high-throughput screening (HTS) data, as exemplified by ToxCast and Tox21, for application in toxicological read-across in food-relevant chemicals. Key questions were addressed on the extent to which the HTS data could provide information enabling (1) the elucidation of underlying bioactivities associated with apical toxicological outcomes, (2) the closing of existing toxicological data gaps, and (3) the definition of the boundaries of chemical space across which bioactivity could reliably be extrapolated. Results revealed that many biological targets apparently activated within the chemical groupings lack, at this time, validated toxicity pathway associations. Therefore, as means of providing proof-of-principle, a comparatively well-characterized end point-estrogenicity-was selected for evaluation. This was facilitated through the preparation of two exploratory case studies, focusing upon groupings of paraben-gallates and pyranone-type compounds (notably flavonoids). Within both, the HTS data were seen to reflect estrogenic potencies in a manner which broadly corresponded to established structure-activity group relationships, with parabens and flavonoids displaying greater estrogen receptor affinity than benzoate esters and alternative pyranone-containing molecules, respectively. As such, utility in the identification of out-of-domain compounds was demonstrated, indicating potential for application in addressing point (3) as detailed above.

Computer-Aided Ligand Discovery for Estrogen Receptor Alpha

Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERβ, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERβ isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.

Estrogen receptor-positive (ER+) breast cancer treatment: Are multi-target compounds the next promising approach?

Endocrine therapy is currently the main therapeutic approach for estrogen receptor-positive (ER⁺) breast cancer, the most frequent subtype of breast cancer in women worldwide. For this subtype of tumors, the current clinical treatment includes aromatase inhibitors (AIs) and anti-estrogenic compounds, such as Tamoxifen and Fulvestrant, being AIs the first-line treatment option for post-menopausal women. Moreover, the recent guidelines also suggest the use of these compounds by pre-menopausal women after suppressing ovaries function. However, besides its therapeutic efficacy, the prolonged use of this type of therapies may lead to the development of several adverse effects, as well as, endocrine resistance, limiting the effectiveness of such treatments. In order to surpass this issues and clinical concerns, during the last years, several studies have been suggesting alternative therapeutic approaches, considering the function of aromatase, ERα and ERβ. Here, we review the structural and functional features of these three targets and their importance in ER⁺ breast cancer treatment, as well as, the current treatment strategies used in clinic, emphasizing the importance of the development of multi-target compounds able to simultaneously modulate these key targets, as a novel and promising therapeutic strategy for this type of cancer.

Homology models of mouse and rat estrogen receptor-α ligand-binding domain created by in silico mutagenesis of a human template: molecular docking with 17ß-estradiol, diethylstilbestrol, and paraben analogs

Crystal structures exist for human, but not rodent, estrogen receptor-α ligand-binding domain (ERα-LBD). Consequently, rodent studies involving binding of compounds to ERα-LBD are limited in their molecular-level interpretation and extrapolation to humans. Because the sequences of rodent and human ERα-LBDs are > 95% identical, we expected their 3D structures and ligand binding to be highly similar. To test this hypothesis, we used the human ERα-LBD structure (PDB 3UUD) as a template to produce rat and mouse homology models. Employing the rodent models and human structure, we generated docking poses of 23 Group A ligands (17ß-estradiol, diethylstilbestrol, and 21 paraben analogs) in AutoDock Vina for interspecies comparisons. Ligand RMSDs (Å) (median, 95% CI) were 0.49 (0.21-1.82) (human-mouse) and 1.19 (0.22-1.82) (human-rat), well below the 2.0-2.5 Å range for equivalent docking poses. Numbers of interspecies ligand-receptor residue contacts were highly similar, with Sorensen Sc (%) = 96.8 (90.0-100) (human-mouse) and 97.7 (89.5-100) (human-rat). Likewise, numbers of interspecies ligand-receptor residue contacts were highly correlated: Pearson r = 0.913 (human-mouse) and 0.925 (human-rat). Numbers of interspecies ligand-receptor atom contacts were even more tightly correlated: r = 0.979 (human-mouse) and 0.986 (human-rat). Pyramid plots of numbers of ligand-receptor atom contacts by residue exhibited high interspecies symmetry and had Spearman r s = 0.977 (human-mouse) and 0.966 (human-rat). Group B ligands included 15 ring-substituted parabens recently shown experimentally to exhibit decreased binding to human ERα and to exert increased antimicrobial activity. Ligand efficiencies calculated from docking ligands into human ERα-LBD were well correlated with those derived from published experimental data (Pearson partial r p = 0.894 and 0.918; Groups A and B, respectively). Overall, the results indicate that our constructed rodent ERα-LBDs interact with ligands in like manner to the human receptor, thus providing a high level of confidence in extrapolations of rodent to human ligand-receptor interactions.

Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

Many chemicals that disrupt endocrine function have been linked to a variety of adverse biological outcomes. However, screening for endocrine disruption using in vitro or in vivo approaches is costly and time-consuming. Computational methods, e.g., quantitative structure-activity relationship models, have become more reliable due to bigger training sets, increased computing power, and advanced machine learning algorithms, such as multilayered artificial neural networks. Machine learning models can be used to predict compounds for endocrine disrupting capabilities, such as binding to the estrogen receptor (ER), and allow for prioritization and further testing. In this work, an exhaustive comparison of multiple machine learning algorithms, chemical spaces, and evaluation metrics for ER binding was performed on public data sets curated using in-house cheminformatics software (Assay Central). Chemical features utilized in modeling consisted of binary fingerprints (ECFP6, FCFP6, ToxPrint, or MACCS keys) and continuous molecular descriptors from RDKit. Each feature set was subjected to classic machine learning algorithms (Bernoulli Naive Bayes, AdaBoost Decision Tree, Random Forest, Support Vector Machine) and Deep Neural Networks (DNN). Models were evaluated using a variety of metrics: recall, precision, F1-score, accuracy, area under the receiver operating characteristic curve, Cohen's Kappa, and Matthews correlation coefficient. For predicting compounds within the training set, DNN has an accuracy higher than that of other methods; however, in 5-fold cross validation and external test set predictions, DNN and most classic machine learning models perform similarly regardless of the data set or molecular descriptors used. We have also used the rank normalized scores as a performance-criteria for each machine learning method, and Random Forest performed best on the validation set when ranked by metric or by data sets. These results suggest classic machine learning algorithms may be sufficient to develop high quality predictive models of ER activity.

A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening

The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of in vitro assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system are unable to be replicated in an in vitro environment. In the current study, a workflow is presented for complementing in vitro testing results with in silico and in vitro techniques to identify inactive parents that may produce active metabolites. A case study applying this workflow involved investigating the influence of metabolism for over 1,400 chemicals considered inactive across18 in vitro assays related to the estrogen receptor (ER) pathway. Over 7,500 first-generation and second-generation metabolites were generated for these in vitro inactive chemicals using an in silico software program. Next, a consensus model comprised of four individual quantitative structure activity relationship (QSAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for ~8-10% of metabolites in each generation, with these metabolites linked to 259 in vitro inactive parent chemicals. Metabolites were enriched in substructures consisting of alcohol, aromatic, and phenol bonds relative to their inactive parent chemicals, suggesting these features are potentially favorable for ER-binding. The workflow presented here can be used to identify parent chemicals that can be potentially bioactive, to aid confidence in high throughput risk screening.

Structural insight into the antagonistic action of diarylheptanoid on human estrogen receptor alpha

Estrogen receptor α (ER α) is an important therapeutic target in the regulation of ligand dependent signaling in breast cancer. The current study investigates the anti-estrogenic potential of the Diarylheptanoid, 5-hydroxy-7-(4-hydroxy-3 methoxyphenyl)-1-phenyl-3-heptanone (DAH) in silico. Rigid Docking analysis of DAH at the ligand binding domain (LBD) of ER α showed hydrogen bond interactions with Arg394 and Glu353 at the active site, similar to the positive controls 4-Hydroxy Tamoxifen (4-OHT) and Fulvestrant (FUL). The protein and the protein-DAH complexes were further analyzed using molecular dynamics simulations for a time scale of 50 ns using GROMACS. Root mean square fluctuation (RMSF) analysis showed large fluctuations at the N-terminal region of Helices (H) 3, 9 and at the C-terminal region of H11, which could be involved in the antagonistic conformational change. Interestingly, H12 appeared to move away from the ligand binding pocket and occupy the co-activator binding groove at the LBD of ER α. Secondary structure analysis of the protein upon binding of DAH and CUR showed structural change from α-helix to Turn conformation at H4. We hypothesize that this structural change at H4, similar to the positive control, could hinder the activity of AF-2 by blocking the binding of co-activator. These conformational changes in ER α indicate an anti-estrogenic and therapeutic potential of the DAH.

3D-QSAR study of steroidal and azaheterocyclic human aromatase inhibitors using quantitative profile of protein-ligand interactions

Aromatase is a member of the cytochrome P450 superfamily responsible for a key step in the biosynthesis of estrogens. As estrogens are involved in the control of important reproduction-related processes, including sexual differentiation and maturation, aromatase is a potential target for endocrine disrupting chemicals as well as breast cancer therapy. In this work, 3D-QSAR combined with quantitative profile of protein-ligand interactions was employed in the identification and characterization of critical steric and electronic features of aromatase-inhibitor complexes and the estimation of their quantitative contribution to inhibition potency. Bioactivity data on pIC₅₀ values of 175 steroidal and 124 azaheterocyclic human aromatase inhibitors (AIs) were used for the 3D-QSAR analysis. For the quantitative description of the effects of the hydrophobic contact and nitrogen-heme-iron coordination on aromatase inhibition, the hydrophobicity density field model and the smallest dual descriptor Δf(r) ^S were introduced, respectively. The model revealed that hydrophobic contact and nitrogen-heme-iron coordination primarily determines inhibition potency of steroidal and azaheterocyclic AIs, respectively. Moreover, hydrogen bonds with key amino acid residues, in particular Asp309 and Met375, and interaction with the heme-iron are required for potent inhibition. Phe221 and Thr310 appear to be quite flexible and adopt different conformations according to a substituent at 4- or 6-position of steroids. Flexible docking results indicate that proper representation of the residues' flexibility is critical for reasonable description of binding of the structurally diverse inhibitors. Our results provide a quantitative and mechanistic understanding of inhibitory activity of steroidal and azaheterocyclic AIs of relevance to adverse outcome pathway development and rational drug design.

Computational biochemical investigation of the binding energy interactions between an estrogen receptor and its agonists

We present the energy profiles of estrogen receptor–agonist ligand interactions in atomic detail using a quantum biochemical approach.

High-Affinity Nucleic-Acid-Based Receptors for Steroids

Artificial receptors for hydrophobic molecules usually have moderate affinities and limited selectivities. We describe three new classes of high affinity hydrophobic receptors for nonaromatic steroids based on deoxyribonucleotides, obtained through five high stringency selections coupled with tailored counter-selections. The isolation of multiple classes of high affinity steroid receptors demonstrates the surprising breadth of moderately sized hydrophobic binding motifs (<40 nucleotides) available to natural nucleic acids. Studies of interactions with analogs indicate that two classes, four-way junctions and 4XG_N motifs, comprise receptors with shapes that prevent binding of specific steroid conjugates used in counter-selections. Furthermore, they strongly prefer nonhydroxylated steroid cores, which is typical for hydrophobic receptors. The third new class accommodates hydroxyl groups in high-affinity, high-selectivity binding pockets, thus reversing the preferences of the first two classes. The high-affinity binding of aptamers to targets efficiently inhibits double-helix formation in the presence of the complementary oligonucleotides. The high affinity of some of these receptors and tailored elimination of binding through counter-selections ensures that these new aptamers will enable clinical chemistry applications.

In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods

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.