Computational study of binding affinity to nuclear receptors for some cosmetic ingredients

Priority list of potential endocrine-disrupting chemicals in food chemical contaminants: a docking study and in vitro/epidemiological evidence integration

Diet is an important exposure route of endocrine-disrupting chemicals (EDCs), but many unfiltered potential EDCs remain in food. The in silico prediction of EDCs is a popular method for preliminary screening. Potential EDCs in food were screened using Endocrine Disruptome, an open-source platform for inverse docking, to predict the binding probabilities of 587 food chemical contaminants with 18 human nuclear hormone receptor (NHR) conformations. In total, 25 contaminants were bound to multiple NHRs such as oestrogen receptor α/β and androgen receptor. These 25 compounds mainly include pesticides and per- and polyfluoroalkyl substances (PFASs). The prediction results were validated with the in vitro data. The structural features and the crucial amino acid residues of the four NHRs were also validated based on previous literature. The findings indicate that the screening has good prediction efficiency. In addition, the epidemic evidence about endocrine interference of PFASs in food on children was further validated through this screening. This study provides preliminary screening results for EDCs in food and a priority list for in vitro and in vivo research.

Prediction of the endocrine-disrupting ability of 49 per- and polyfluoroalkyl substances: In silico and epidemiological evidence

The toxic effects of per- and polyfluoroalkyl substances (PFASs) on humans are mediated by nuclear hormone receptors (NHRs). However, data on the interaction of PFASs and NHRs is limited. Endocrine Disruptome, an inverse docking tool, was used in this study to simulate the docking of 49 common PFASs with 14 different types of human NHRs. According to the findings, 25 PFASs have a high or moderately high probability of binding to more than five NHRs, with androgen receptor (AR) and mineralocorticoid receptor (MR) being the most likely target NHRs. Molecular docking analyses revealed that the binding modes of PFASs with the two NHRs were similar to those of their corresponding co-crystallized ligands. PFASs, in particular, may disrupt the endocrine system by binding to MR. This finding is consistent with epidemiological research that has linked PFASs to MR-related diseases. Our findings may contribute to a better understanding of the health risks posed by PFASs.

In silico prediction of nuclear receptor binding to polychlorinated dibenzofurans and its implication on endocrine disruption in humans and wildlife

Polychlorinated dibenzofurans (PCDFs) are known to cause endocrine disruption in humans and wildlife but the mechanisms underlying this disruption have not been adequately investigated. In this paper, the susceptibility of the endocrine system to disruption by PCDF congeners via nuclear receptor binding was studied using molecular docking simulation. Findings revealed that some PCDF congeners exhibit high probabilities of binding to androgen receptor in its agonistic and antagonistic conformations. In depth molecular docking analysis of the receptor-ligand complexes formed by PCDFs with androgen receptor in its agonistic and antagonistic conformations showed that, these complexes were stabilized by electrostatic, van der Waals, pi-effect and hydrophobic interactions. It was also observed that PCDF molecules mimic the modes of interaction observed in androgen-testosterone and androgen-bicalutamide complexes, utilizing between 65 and 83% of the amino acid residues used by the co-crystallized ligands for binding. This computational study suggests that some PCDF congeners may act as agonists and antagonists of androgen receptor in humans and wildlife via inapproprate binding to the receptor.

A computational insight into endocrine disruption by polychlorinated biphenyls via non-covalent interactions with human nuclear receptors

Production of polychlorinated biphenyls (PCBs) was banned a long time ago because of their harmful health effects but humans continue to be exposed to residual PCBs in the environment. In this study, the susceptibility of human nuclear receptors to binding by PCBs was investigated using molecular docking simulation. Findings revealed that PCBs belonging to ortho-substituted, mono-ortho-substituted and non-ortho-substituted congeners could bind to agonistic conformations of androgen (AR), estrogen (ER α and ER β), glucocorticoid (GR) and thyroid hormone (TR α and TR β) receptors as well as antagonistic conformation of androgen receptor (AR an) but only ortho-substituted and mono-ortho-substituted PCBs could bind to estrogen receptors in their antagonistic conformations (ER α an and ER β an). Further molecular docking analyses showed that PCBs mimic the modes of interaction observed for the co-crystallized ligands in the crystal structures of the affected receptors, utilizing 81%, 83%, 78%, 60%, 75%, 60%, 86%, 100% and 75% of the amino acid residues utilized by the co-crystallized ligands for binding in AR, AR an, ER α, ER α an, ER β, ER β an, GR, TR α and TR β respectively. This computational study suggests that PCBs may cause endocrine disruption via formation of non-covalent interactions with androgen, estrogen, glucocorticoid and thyroid hormone receptors.

Computational evaluation of interactions between organophosphate esters and nuclear hormone receptors

Organophosphate esters (OPEs) have gained considerable interest from many environmental chemists and toxicologists due to their frequent detection in the environment and potential adverse effects on health. Nuclear hormone receptors (NHRs) were found to mediate many of their adverse effects. However, our knowledge regarding the direct binding and interaction between OPEs and NHRs is limited. In this study, Endocrine Disruptome, an online computational tool based on the technique of inverse docking, was used to calculate the binding affinity score of 25 individual OPEs with 12 different human NHRs. Results showed that 20% of potential binding interactions between the OPEs and NHRs had medium-to-high probabilities. The accuracy, sensitivity and specificity of the predictions were 78.8, 60.0 and 80.9%, respectively. OPEs with a benzene ring were more active than those without, among which, tri-o-tolyl phosphate and tri-m-tolyl phosphate displayed the highest activities, suggesting that they might pose the greatest potential risks for interference with endocrine functions. In addition, the antagonistic conformations of androgen receptor and estrogen receptor β were found to be the two most vulnerable NHR conformations. Our findings can further the understanding about the health risk(s) of OPEs.

Toxicity profiling and prioritization of plant-derived antimalarial agents

Human malaria is the most widespread mosquito-borne life-threatening disease worldwide. In the absence of effective vaccines, prevention and treatment of malaria only depend on prophylaxis and drug-based therapy either in monotherapy or in combination. Unfortunately, the number of available antimalarial drugs presenting different mechanisms of action is rather limited. In addition, the appearance of drug-resistance in the parasite strains impacts the efficacy of the treatments. As a result, there is a crucial need to find new drugs to circumvent resistance problems. In the quest to identify new antimalarial agents a huge number of plant-derived compounds (PDCs) have been investigated. Surprisingly in the in silico PDC screening programs, toxicity filters are either never used or so simple that their interest is limited. In this context, the goal of this study was to show how to take advantage of validated toxicity QSAR models for refining the selection of PDCs. From an original data set of 507 PDCs collected from the literature, the use of toxicity filters for endocrine disruption, developmental toxicity, and hepatotoxicity in conjunction with classical pharmacokinetic filters allowed us to obtain a list of 31 compounds of potential interest. The pros and cons of such a strategy have been discussed.

Expert judgment based multicriteria decision models to assess the risk of pesticides on reproduction failures of grey partridge

A suite of models is proposed for estimating the risk of pesticides against the grey partridge (Perdix perdix) and their clutches. Radio-tracked data of females, description and location of the clutches, and data on the pesticide treatments during the laying periods of the partridges were used as basic information. Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modelling allowed us to characterize the pesticides by their 1-octanol/water partition coefficient (log P), vapour pressure, primary and ultimate biodegradation potential, acute toxicity (LD₅₀) on P. perdix, and endocrine disruption potential. From these physicochemical and toxicological data, the system of integration of risk with interaction of scores (SIRIS) method was used to design scores of risk for pesticides, alone or in mixture. A program, written in R (version 3.1.1), called Simulation of Toxicity in Perdix perdix (SimToxPP), was designed for estimating the risk of substances, considered alone or in mixture, against the grey partridge during breeding. The software tool is flexible enough to simulate realistic in situ scenarios. Different examples of applications are shown. The advantages and limitations of the approach are briefly discussed.

Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors

Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.

Computational study involving identification of endocrine disrupting potential of herbicides: Its implication in TDS and cancer progression in CRPC patients

Several environmental pollutants, including herbicides, act as endocrine disrupting chemicals (EDCs). They can cause cancer, diabetes, obesity, metabolic diseases and developmental problems. Present study was conducted to screen 608 herbicides for evaluating their endocrine disrupting potential. The screening was carried out with the help of endocrine disruptome docking program, http://endocrinedisruptome.ki.si (Kolsek et al., 2013). This program screens the binding affinity of test ligands to 12 major nuclear receptors. As high as 252 compounds were capable of binding to at least three receptors wherein 10 of them showed affinity with at-least six receptors based on this approach. The latter were ranked as potent EDCs. Majority of the screened herbicides were acting as antagonists of human androgen receptor (hAR). A homology modeling approach was used to construct the three dimensional structure of hAR to understand their binding mechanism. Docking results reveal that the most potent antiandrogenic herbicides would bind to hydrophobic cavity of modeled hAR and may lead to testicular dysgenesis syndrome (TDS) on fetal exposure. However, on binding to T877 mutant AR they seem to act as agonists in castration-resistant prostate cancer (CRPC) patients.

A high-throughput, computational system to predict if environmental contaminants can bind to human nuclear receptors

Some pollutants can bind to nuclear receptors (NRs) and modulate their activities. Predicting interactions of NRs with chemicals is required by various jurisdictions because these molecular initiating events can result in adverse, apical outcomes, such as survival, growth or reproduction. The goal of this study was to develop a high-throughput, computational method to predict potential agonists of NRs, especially for contaminants in the environment or to which people or wildlife are expected to be exposed, including both persistent and pseudo-persistent chemicals. A 3D-structure database containing 39 human NRs was developed. The database was then combined with AutoDock Vina to develop a System for Predicting Potential Effective Nuclear Receptors (SPEN), based on inverse docking of chemicals. The SPEN was further validated and evaluated by experimental results for a subset of 10 chemicals. Finally, to assess the robustness of SPEN, its ability to predict potentials of 40 chemicals to bind to some of the most studied receptors was evaluated. SPEN is rapid, cost effective and powerful for predicting binding of chemicals to NRs. SPEN was determined to be useful for screening chemicals so that pollutants in the environment can be prioritized for regulators or when considering alternative compounds to replace known or suspected contaminants with poor environmental profiles.

Binding of Pollutants to Biomolecules: A Simulation Study

A number of cases around the world have been reported where animals were found dead or dying with symptoms resembling a thiamine (vitamin B) deficiency, and for some of these, a link to pollutants has been suggested. Here, we investigate whether biomolecules involved in thiamin binding and transport could be blocked by a range of different pollutants. We used in silico docking of five compound classes (25 compounds in total) to each of five targets (prion protein, ECF-type ABC transporter, thi-box riboswitch receptor, thiamin pyrophosphokinase, and YKoF protein) and subsequently performed molecular dynamics (MD) simulations to assess the stability of the complexes. The compound classes were thiamin analogues (control), pesticides, veterinary medicines, polychlorinated biphenyls, and dioxins, all of which are prevalent in the environment to some extent. A few anthropogenic compounds were found to bind the ECF-type ABC transporter, but none binds stably to prion protein. For the riboswitch, most compounds remained in their binding pockets during 50 ns of MD simulation, indicating that RNA provides a promiscuous binding site. In both YKoF and thiamin pyrophosphokinase (TPK), most compounds remain tightly bound. However, TPK biomolecules undergo pollutant-induced conformational changes. Although most compounds are found to bind to some of these targets, a larger data set is needed along with more quantitative methods like free energy perturbation calculations before firm conclusions can be drawn. This study is in part a test bed for large-scale quantitative computational screening of interactions between biological entities and pollutant molecules.

Rational Selection of the 3D Structure of Biomacromolecules for Molecular Docking Studies on the Mechanism of Endocrine Disruptor Action

Molecular modeling has become an essential tool in predicting and simulating endocrine disrupting effects of chemicals. A key prerequisite for successful application of molecular modeling lies in the correctness of 3D structure for biomacromolecules to be simulated. To date, there are several databases that can provide the experimentally-determined 3D structures. However, commonly, there are many challenges or disadvantageous factors, e.g., (a) lots of 3D structures for a given biomacromolecular target in the protein database; (b) the quality variability for those structures; (c) belonging to different species; (d) mutant amino acid residue in key positions, and so on. Once an inappropriate 3D structure of a target biomacromolecule was selected in molecular modeling, the accuracy and scientific nature of the modeling results could be inevitably affected. In this article, based on literature survey and an analysis of the 3D structure characterization of biomacromolecular targets belonging to the endocrine system in protein databases, six principles were proposed to guide the selection of the appropriate 3D structure of biomacromolecules. The principles include considering the species diversity, the mechanism of action, whether there are mutant amino acid residues, whether the number of protein chains is correct, the degree of structural similarity between the ligand in 3D structure and the target compounds, and other factors, e.g., the experimental pH conditions of the structure determined process and resolution.

The Quantum Nature of Drug-Receptor Interactions: Deuteration Changes Binding Affinities for Histamine Receptor Ligands

In this article we report a combined experimental and computational study concerning the effects of deuteration on the binding of histamine and two other histaminergic agonists to 3H-tiotidine-labeled histamine H2 receptor in neonatal rat astrocytes. Binding affinities were measured by displacing radiolabeled tiotidine from H2 receptor binding sites present on cultured neonatal rat astrocytes. Quantum-chemical calculations were performed by employing the empirical quantization of nuclear motion within a cluster model of the receptor binding site extracted from the homology model of the entire H2 receptor. Structure of H2 receptor built by homology modelling is attached in the supporting information (S1 Table) Experiments clearly demonstrate that deuteration affects the binding by increasing the affinity for histamine and reducing it for 2-methylhistamine, while basically leaving it unchanged for 4-methylhistamine. Ab initio quantum-chemical calculations on the cluster system extracted from the homology H2 model along with the implicit quantization of the acidic N-H and O-H bonds demonstrate that these changes in the binding can be rationalized by the altered strength of the hydrogen bonding upon deuteration known as the Ubbelohde effect. Our computational analysis also reveals a new mechanism of histamine binding, which underlines an important role of Tyr250 residue. The present work is, to our best knowledge, the first study of nuclear quantum effects on ligand receptor binding. The ligand H/D substitution is relevant for therapy in the context of perdeuterated and thus more stable drugs that are expected to enter therapeutic practice in the near future. Moreover, presented approach may contribute towards understanding receptor activation, while a distant goal remains in silico discrimination between agonists and antagonists based on the receptor structure.

Prediction of the endocrine disruption profile of pesticides

Numerous manmade chemicals released into the environment can interfere with normal, hormonally regulated biological processes to adversely affect the development and reproductive functions of living species. Various in vivo and in vitro tests have been designed for detecting endocrine disruptors, but the number of chemicals to test is so high that to save time and money, (quantitative) structure-activity relationship ((Q)SAR) models are increasingly used as a surrogate for these laboratory assays. However, most of them focus only on a specific target (e.g. estrogenic or androgenic receptor) while, to be more efficient, endocrine disruption modelling should preferentially consider profiles of activities to better gauge this complex phenomenon. In this context, an attempt was made to evaluate the endocrine disruption profile of 220 structurally diverse pesticides using the Endocrine Disruptome simulation (EDS) tool, which simultaneously predicts the probability of binding of chemicals on 12 nuclear receptors. In a first step, the EDS web-based system was successfully applied to 16 pharmaceutical compounds known to target at least one of the studied receptors. About 13% of the studied pesticides were estimated to be potential disruptors of the endocrine system due to their high predicted affinity for at least one receptor. In contrast, about 55% of them were unlikely to be endocrine disruptors. The simulation results are discussed and some comments on the use of the EDS tool are made.