Use of computational modeling approaches in studying the binding interactions of compounds with human estrogen receptors

Neuroactive Steroids, Toll-like Receptors, and Neuroimmune Regulation: Insights into Their Impact on Neuropsychiatric Disorders

Pregnane neuroactive steroids, notably allopregnanolone and pregnenolone, exhibit efficacy in mitigating inflammatory signals triggered by toll-like receptor (TLR) activation, thus attenuating the production of inflammatory factors. Clinical studies highlight their therapeutic potential, particularly in conditions like postpartum depression (PPD), where the FDA-approved compound brexanolone, an intravenous formulation of allopregnanolone, effectively suppresses TLR-mediated inflammatory pathways, predicting symptom improvement. Additionally, pregnane neurosteroids exhibit trophic and anti-inflammatory properties, stimulating the production of vital trophic proteins and anti-inflammatory factors. Androstane neuroactive steroids, including estrogens and androgens, along with dehydroepiandrosterone (DHEA), display diverse effects on TLR expression and activation. Notably, androstenediol (ADIOL), an androstane neurosteroid, emerges as a potent anti-inflammatory agent, promising for therapeutic interventions. The dysregulation of immune responses via TLR signaling alongside reduced levels of endogenous neurosteroids significantly contributes to symptom severity across various neuropsychiatric disorders. Neuroactive steroids, such as allopregnanolone, demonstrate efficacy in alleviating symptoms of various neuropsychiatric disorders and modulating neuroimmune responses, offering potential intervention avenues. This review emphasizes the significant therapeutic potential of neuroactive steroids in modulating TLR signaling pathways, particularly in addressing inflammatory processes associated with neuropsychiatric disorders. It advances our understanding of the complex interplay between neuroactive steroids and immune responses, paving the way for personalized treatment strategies tailored to individual needs and providing insights for future research aimed at unraveling the intricacies of neuropsychiatric disorders.

Estrogenic flavonoids and their molecular mechanisms of action

Flavonoids are a major group of phytoestrogens associated with physiological effects, and ecological and social impacts. Although the estrogenic activity of flavonoids was reported by researchers in the fields of medical, environmental and food studies, their molecular mechanisms of action have not been comprehensively reviewed. The estrogenic activity of the respective classes of flavonoids, anthocyanidins/anthocyanins, 2-arylbenzofurans/3-arylcoumarins/α-methyldeoxybenzoins, aurones/chalcones/dihydrochalcones, coumaronochromones, coumestans, flavans/flavan-3-ols/flavan-4-ols, flavanones/dihydroflavonols, flavones/flavonols, homoisoflavonoids, isoflavans, isoflavanones, isoflavenes, isoflavones, neoflavonoids, oligoflavonoids, pterocarpans/pterocarpenes, and rotenone/rotenoids, was summarized through a comprehensive literature search, and their structure-activity relationship, biological activities, signaling pathways, and applications were discussed. Although the respective classes of flavonoids contained at least one chemical mimicking estrogen, the mechanisms varied, such as those with estrogenic, anti-estrogenic, non-estrogenic, and biphasic activities, and additional activities through crosstalk/bypassing, which exert biological activities through cell signaling pathways. Such mechanistic variations of estrogen action are not limited to flavonoids and are observed among other broad categories of chemicals, thus this group of chemicals can be termed as the "estrogenome". This review article focuses on the connection of estrogen action mainly between the outer and the inner environments, which represent variations of chemicals and biological activities/signaling pathways, respectively, and form the basis to understand their applications. The applications of chemicals will markedly progress due to emerging technologies, such as artificial intelligence for precision medicine, which is also true of the study of the estrogenome including estrogenic flavonoids.

Identification of active and inactive agonists/antagonists of estrogen receptor based on Tox21 10K compound library: Binomial analysis and structure alert

Endocrine disrupting chemicals can mimic, block, or interfere with hormones in organisms and subsequently affect their development and reproduction, which has raised significant public concern over the past several decades. To investigate (quantitative) structure-activity relationship, 8280 compounds were compiled from the Tox21 10K compound library. The results show that 50% activity concentrations of agonists are poorly related to that of antagonists because many compounds have considerably different activity concentrations between the agonists and antagonists. Analysis on the chemical classes based on mode of action (MOA) reveals that estrogen receptor (ER) is not the main target site in the acute toxicity to aquatic organisms. Binomial analysis of active and inactive ER agonists/antagonists reveals that ER activity of compounds is dominated by octanol/water partition coefficient and excess molar refraction. The binomial equation developed from the two descriptors can classify well active and inactive ER chemicals with an overall prediction accuracy of 73%. The classification equation developed from the molecular descriptors indicates that estrogens react with the receptor through hydrophobic and π-n electron interactions. At the same time, molecular ionization, polarity, and hydrogen bonding ability can also affect the chemical ER activity. A decision tree developed from chemical structures and their applications reveals that many hormones, proton pump inhibitors, PAHs, progestin, insecticides, fungicides, steroid and chemotherapy medications are active ER agonists/antagonists. On the other hand, many monocyclic/nonaromatic chain compounds and herbicides are inactive ER compounds. The decision tree and binomial equation developed here are valuable tools to predict active and inactive ER compounds.

Receptor-Bound Perfluoroalkyl Carboxylic Acids Dictate Their Activity on Human and Mouse Peroxisome Proliferator-Activated Receptor γ

In in vitro cell assays, nominal concentrations of a test chemical are most frequently used in the description of its dose-response curves. Although the biologically effective concentration (BEC) is considered as the most relevant dose metric, in practice, it is very difficult to measure. In this work, we attempted to determine the BEC of long-chain perfluoroalkyl carboxylic acids (PFCAs) in peroxisome proliferator-activated receptor γ (PPARγ) activity assays. In both adipogenesis and transcriptional activity assays with human and mouse cells, PPARγ activity of 7 PFCAs first increased and then decreased with their carbon chain length. The binding affinity of these PFCAs with the ligand-binding domain of PPARγ was measured by fluorescence competitive binding assay and showed very poor correlation with their receptor activity (r² = 0.002-0.047). Internal concentrations of the PFCAs in the cells were measured by LC-MS/MS. Although their correlation with the receptor activity increased significantly, it is still low (r² = 0.41-0.82). Using the binding affinity constant, internal concentration, and PPARγ concentration measured by immunoassays, concentrations of receptor-bound PFCAs in cells were calculated, which exhibited excellent correlation with PPARγ activity in both adipogenesis and transcriptional activity assays (r² = 0.91-0.93). These results demonstrate that the concentration of receptor-bound PFCA is the BEC that dictates its activity on human and mouse PPARγ in cell assays. In the absence of any direct detection method, our approach can be used to calculate the target-site concentration of other ligands.

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.

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.

Prediction of Estrogenic Bioactivity of Environmental Chemical Metabolites

The US Environmental Protection Agency's (EPA) Endocrine Disruptor Screening Program (EDSP) is using in vitro data generated from ToxCast/Tox21 high-throughput screening assays to assess the endocrine activity of environmental chemicals. Considering that in vitro assays may have limited metabolic capacity, inactive chemicals that are biotransformed into metabolites with endocrine bioactivity may be missed for further screening and testing. Therefore, there is a value in developing novel approaches to account for metabolism and endocrine activity of both parent chemicals and their associated metabolites. We used commercially available software to predict metabolites of 50 parent compounds, out of which 38 chemicals are known to have estrogenic metabolites, and 12 compounds and their metabolites are negative for estrogenic activity. Three ER QSAR models were used to determine potential estrogen bioactivity of the parent compounds and predicted metabolites, the outputs of the models were averaged, and the chemicals were then ranked based on the total estrogenicity of the parent chemical and metabolites. The metabolite prediction software correctly identified known estrogenic metabolites for 26 out of 27 parent chemicals with associated metabolite data, and 39 out of 46 estrogenic metabolites were predicted as potential biotransformation products derived from the parent chemical. The QSAR models estimated stronger estrogenic activity for the majority of the known estrogenic metabolites compared to their parent chemicals. Finally, the three models identified a similar set of parent compounds as top ranked chemicals based on the estrogenicity of putative metabolites. This proposed in silico approach is an inexpensive and rapid strategy for the detection of chemicals with estrogenic metabolites and may reduce potential false negative results from in vitro assays.