Molecular docking, molecular dynamics simulation, and structure-based 3D-QSAR studies on the aryl hydrocarbon receptor agonistic activity of hydroxylated polychlorinated biphenyls

Investigating PCB degradation by indigenous fungal strains isolated from the transformer oil-contaminated site: degradation kinetics, Bayesian network, artificial neural networks, QSAR with DFT, molecular docking, and molecular dynamics simulation

The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian network (BN), quantitative structure-activity relationship-density functional theory (QSAR-DFT), artificial neural network (ANN), molecular docking (MD), and molecular dynamics stimulation (MS) of PCB biodegradation, i.e., PCB-10, PCB-28, PCB-52, PCB-138, PCB-153, and PCB-180 in the soil system using fungi isolated from the transformer oil-contaminated sites. Results revealed that the efficacy of PCB biodegradation best fits the first-order kinetics (R2 ≥ 0.93). The consortium treatment (29.44-74.49%) exhibited more efficient degradation of PCBs than those of Aspergillus tamarii sp. MN69 (27.09-71.25%), Corynespora cassiicola sp. MN69 (23.76-57.37%), and Corynespora cassiicola sp. MN70 (23.09-54.98%). 3'-Methoxy-2, 4, 4'-trichloro-biphenyl as an intermediate derivative was detected in the fungal consortium treatment. The BN analysis predicted that the biodegradation efficiency of PCBs ranged from 11.6 to 72.9%. The ANN approach showed the importance of chemical descriptors in decreasing order, i.e., LUMO > MW > IP > polarity no. > no. of chlorine > Wiener index > Zagreb index > HOMU > Pogliani index > APE in PCB removal. Furthermore, the QSAR-DFT model between the chemical descriptors and rate constant (log K) exhibited a high fit and good robustness of R2 = 99.12% in predicting ability. The MD and MS analyses showed the lowest binding energy through normal mode analysis (NMA), implying stability in the interactions of the docked complexes. These findings provide crucial insights for devising strategies focused on natural attenuation, holding substantial potential for mitigating PCB contamination within the environment.

Pyrene and its derivatives increase lung adverse effects by activating aryl hydrocarbon receptor transcription

Derivatives of polycyclic aromatic hydrocarbons (PAHs) pose significant threat to environment and human health due to their widespread and potential hazards. However, adverse effects and action mechanisms of PAH derivatives on human health have not been attempted yet. Herein, we chose pyrene and its derivatives (1-hydroxypyrene, 1-nitropyrene, and 1-methylpyrene) to investigate adverse effect mechanism to human lungs using in vitro and in vivo methods. Results showed that pyrene derivatives have higher lung health risks than original pyrene. They can activate AhR, subsequently affecting expression of downstream target genes CYP1A1 and CYP1B1. The binding energies of pyrene and its derivatives ranged from -16.07 to -27.25 kcal/mol by molecular dynamics simulations, implying that pyrene and its derivatives acted as agonists of AhR and increased adverse effects on lungs. Specifically, 1-nitropyrene exhibited stabler binding conformation and stronger AhR expression. In addition, sensitivity of pyrene and its derivatives to AhR activation was attributed to type and number of key amino acids in AhR, that is, pyrene (Leu293), 1-nitropyrene (Cys333, Met348, and Val381), 1-hydroxypyrene (Leu293 and Phe287), and 1-methylpyrene (Met348). In summary, we provide a universal approach for understanding action mechanisms of PAH derivatives on human health, and their adverse effects should be taken seriously.

An in silico insight on the mechanistic aspects of gelsenicine toxicity: A reverse screening study pointing to the possible involvement of acetylcholine binding receptor

Gelsedine-type alkaloids are highly toxic plant secondary metabolites produced by shrubs belonging to the Gelsemium genus. Gelsenicine is one of the most concerning gelsedine-type alkaloids with a lethal dose lower than 1 mg/Kg in mice. Several reported episodes of poisoning in livestock and fatality cases in humans due to the usage of Gelsemium plants extracts were reported. Also, gelsedine-type alkaloids were found in honey constituting a potential food safety issue. However, their toxicological understanding is scarce and the molecular mechanism underpinning their toxicity needs further investigations. In this context, an in silico approach based on reverse screening, docking and molecular dynamics successfully identified a possible gelsenicine biological target shedding light on its toxicodynamics. In line with the available crystallographic data, it emerged gelsenicine could target the acetylcholine binding protein possibly acting as a partial agonist against α7 nicotinic acetylcholine receptor (AChR). Overall, these results agreed with evidence previously reported and prioritized AChR for further dedicated analysis.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

Molecular dynamics simulations depict structural motions of the whole human aryl hydrocarbon receptor influencing its binding of ligands and HSP90

The aryl hydrocarbon receptor (AhR) has broad biological functions when its ligands activate it; the non-binding interactions with AhR have not been fully elucidated due to the absence of a complete tridimensional (3D) structure. Therefore, utilization of the whole 3D structure from Homo sapiens AhR by in silico studies will allow us to better study and analyze the binding mode of its full and partial agonists, and antagonists, as well as its interaction with the HSP90 chaperone. The 3D AhR structure was obtained from I-TASSER and subjected to molecular dynamics (MD) simulations to obtain different structural conformations and determine the most populated AhR conformer by clustering analyses. The AhR-3D structures selected from MD simulations and those from clustering analyses were used to achieve docking studies with some of its ligands and protein-protein docking with HSP90. Once the AhR-3D structure was built, its Ramachandran maps and energy showed a well-qualified 3D model. MD simulations showed that the per-Arnt-Sim homology (PAS) PAS A, PAS B, and Q domains underwent conformational changes, identifying the conformation when agonists were binding also, and HSP90 was binding near the PAS A, PAS B, and Q domains. However, when antagonists are binding, HSP90 does not bind near the PAS A, PAS B, and Q domains. These studies show that the complex agonist-AhR-HSP90 can be formed, but this complex is not formed when an antagonist is binding. Knowing the conformations when the ligands bind to AHR and the behavior of HSP90 allows for an understanding of its activity.Communicated by Ramaswamy H. Sarma.

Promotion of Bladder Cancer Cell Metastasis by 2-Mercaptobenzothiazole via Its Activation of Aryl Hydrocarbon Receptor Transcription: Molecular Dynamics Simulations, Cell-Based Assays, and Machine Learning-Driven Prediction

2-Mercaptobenzothiazole (MBT) is an industrial chemical widely used for rubber products, corrosion inhibitors, and polymer materials with multiple environmental and exposure pathways. A growing body of evidence suggests its potential bladder cancer (BC) risk as a public health concern; however, the molecular mechanism remains poorly understood. Herein, we demonstrate the activation of the aryl hydrocarbon receptor (AhR) by MBT and reveal key events in carcinogenesis associated with BC. MBT alters conformational changes of AhR ligand binding domain (LBD) as revealed by 500 ns molecular dynamics simulations and activates AhR transcription with upregulation of AhR-target genes CYP1A1 and CYP1B1 to approximately 1.5-fold. MBT upregulates the expression of MMP1, the cancer cell metastasis biomarker, to 3.2-fold and promotes BC cell invasion through an AhR-mediated manner. MBT is further revealed to induce differentially expressed genes (DEGs) most enriched in cancer pathways by transcriptome profiling. The exposure of MBT at environmentally relevant concentrations induces BC risk via AhR signaling disruption, transcriptome aberration, and malignant cell metastasis. A machine learning-based model with an AUC value of 0.881 is constructed to successfully predict 31 MBT analogues. Overall, we provide molecular insight into the BC risk of MBT and develop an effective tool for rapid screening of AhR agonists.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

Toxicity inhibition strategy of microplastics to aquatic organisms through molecular docking, molecular dynamics simulation and molecular modification

In the present study, the combined toxic effect of microplastics and their additives (five) on aquatic organisms (zebrafish) was studied using full factorial design method, molecular docking, and molecular dynamics (MD) simulation technology. The aquatic toxicity control programmer was designed to improve the optimal combination of plasticizer and microplastics based on the design of environment-friendly phthalic acid ester (PAE) derivatives. First, a total of 64 groups of microplastic-additives were designed using the full factorial design method. Next, the microplastic-additives and aquatic receptor protein were docked together, and the binding energy of these complexes was calculated using the MD simulation method. The results revealed that the aquatic toxicity effects of different microplastic-additive combinations were variable; therefore, the optimal combination of microplastics exhibiting the lowest aquatic toxicity effect could be screened out. Base on the analyzing the bonding effect and surrounded amino acid residues between the microplastic additives and receptor protein, the main driving forces for the binding of the microplastic-additive and the protein were hydrophobic force, hydrogen bonding force and electrostatic force. The main effects and the second-order interaction of the microplastic-additives combination were analyzed using the fixed-effect model. The main additives that affect the aquatic toxicity of the microplastics can be known. In addition, based on the MD simulation of the molecular replacement of PAE derivatives, the optimal level of component combination of low aquatic toxicity effect of microplastics was constructed.

Identification of viable TCDD access pathways to human AhR PAS-B ligand binding domain

Unintentionally released in the environment as by-products of industrial activities, dioxins, exemplified by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), represent a primary concern for human health. Exposure to these chemicals is known to produce a broad spectrum of adverse effects, including cancer. The main mechanism of action of TCDD in humans involves binding to the Aryl hydrocarbon Receptor (AhR). Although qualitatively established, TCDD capture by the AhR remains poorly characterized at the molecular level. Starting from a recently developed structural model of the human AhR PAS-B domain, in this work we attempt the identification of viable TCDD access pathways to the human AhR ligand binding domain by means of molecular dynamics. Based on the result of metadynamics simulations, we identify two main regions that may potentially serve as access paths for TCDD. For each path, we characterize the residues closely interacting with TCDD, thereby suggesting a possible mechanism for TCDD capture. Our results are reviewed and discussed in the light of the available information about Human AhR structure and functions.

In silico exploration of hydroxylated polychlorinated biphenyls as estrogen receptor β ligands by 3D-QSAR, molecular docking and molecular dynamics simulations

Hydroxylated polychlorinated biphenyls (HO-PCBs), as the major metabolites of PCBs, have been reported to act as estrogen receptor β (ERβ) agonists. However, the chemical-biological interactions governing their activities toward ERβ have not been elucidated. Therefore, three dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations, to the best of our knowledge, for the first time were performed to explore the correlation between the structures and activities. The best 3D-QSAR model presented higher predictive ability (R²_cv=0.543, R²_pred=0.5793/R²_cv=0.543, R²_pred=0.6795) based on comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), respectively. At the same time, the derived contour maps indicated the important structural features required for improving the activity. Furthermore, molecular docking studies and MD simulations predicted the binding mode and the interactions between the ligand and the receptor. All the results would lead to a better understanding of the specific mechanism of HO-PCBs on estrogen receptor β (ERβ).Communicated by Ramaswamy H. Sarma.

Theoretical investigation of AhR binding property with relevant structural requirements for AhR-mediated toxicity of polybrominated diphenyl ethers

Polybrominated diphenyl ethers (PBDEs) are more frequently suspected with the induction of toxicity via signal transduction pathway of cytosolic aryl hydrocarbon receptor (AhR), the initial binding to which is assumed to be an essential prerequisite during the ligand-dependent activation. However, the AhR binding property and associated toxicity of PBDEs is yet to be clearly known for lacking insights into the structural requirements at molecular level. To understand the AhR binding property of PBDEs, the ligand binding domain (LBD) of AhR was simulatively developed on homologous protein after basic validation of geometrical rationality and the binding interaction profile was visually described using molecular docking approach. For AhR binding, the offset or edge-on π-π stackings with aromatic motifs including Phe289, Phe345 and His285 were shown to be structurally required whereas the electrostatic attraction validated for AhR binding to dioxins might be less effective for 2,2',3,4,4'-pentabromodiphenyl ether (BDE-85). Besides the demands of less steric hindrance from alanines and weak formulation of hydrogen bonds, the dispersion force through large contact and polarization of S-π electrons seemed to be impactful when BDE-85 were closer to Cys327, Met334 or Met342. With theoretical computation of AhR binding energies, the more significant correlativity with bioassays was derived especially for the lowly/moderately brominated congeners, and could be used to predict the AhR binding affinity on certain degree. The informative results would thus not only help well understand the molecular basis of AhR-mediated toxicity but give an approach for accelerative evaluation of AhR binding and toxicity of PBDEs.

Computational prediction of cytochrome P450 inhibition and induction

Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.

AhR Agonist Activity Confirmation of Polyhalogenated Carbazoles (PHCZs) Using an Integration of in Vitro, in Vivo, and in Silico Models

Polyhalogenated carbazoles (PHCZs) are a kind of rising environmental pollutant that has been reported to pose high risk to human beings and the natural environment. PHCZs are of a similar molecular structure with of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and exhibited some dioxin-like toxicity. Dioxin-like compounds (DLCs) are banned by the Stockholm Convention due to their potential adverse impacts to the environment and public health. However, a few efforts have been made toward the regulation of PHCZs. Herein, we initiated multimodels to systematically determine and validate the dioxin-like effects of PHCZs on a large scale. Transgenic zebrafish line-Tg(cyp1a-12DRE:EGFP) and stably transfected HepG2 cell line with luciferase reporter plasmids were used to screen and evaluate the aryl hydrocarbon receptor (AhR) agonist effects of the target PHCZs, which were then verified by application of molecular docking and expression levels of AhR downstream genes. Results of the two bioassays showed that most of the tested PHCZs could pose dioxin-like AhR agonist effects, change the expression levels of AhR downstream genes, and interact with AhR in accordance with TCDD. In summary, data presented here can help to guide the safe use and regulation of PHCZs.

Understanding the toxic potencies of xenobiotics inducing TCDD/TCDF-like effects

Toxic potencies of xenobiotics such as halogenated aromatic hydrocarbons inducing 2,3,7,8-tetrachlorodibenzo-p-dioxin/2,3,7,8-tetrachlorodibenzofuran (TCDD/TCDF)-like effects were investigated by quantitative structure-toxicity relationships (QSTR) using their aryl hydrocarbon receptor (AhR) binding affinity data. A descriptor pool was created using the SPARTAN 10, DRAGON 6.0 and ADMET 8.0 software packages, and the descriptors were selected using QSARINS (v.2.2.1) software. The QSTR models generated for AhR binding affinities of chemicals with TCDD/TCDF-like effects were internally and externally validated in line with the Organization of Economic Co-operation and Development (OECD) principles. The TCDD-based model had six descriptors from DRAGON 6.0 and ADMET 8.0, whereas the TCDF-based model had seven descriptors from DRAGON 6.0. The predictive ability of the generated models was tested on a diverse group of chemicals including polychlorinated/brominated biphenyls, dioxins/furans, ethers, polyaromatic hydrocarbons with fused heterocyclic rings (i.e. phenoxathiins, thianthrenes and dibenzothiophenes) and polyaromatic hydrocarbons (i.e. halogenated naphthalenes and phenanthrenes) with no AhR binding data. For the external set chemicals, the structural coverage of the generated models was 90% and 89% for TCDD and TCDF-like effects, respectively.

The tertiary structures of porcine AhR and ARNT proteins and molecular interactions within the TCDD/AhR/ARNT complex

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that can be activated by structurally diverse synthetic and natural chemicals, including toxic environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). In the present study, homology models of the porcine AhR-ligand binding domain (LBD) and the porcine aryl hydrocarbon receptor nuclear translocator-ligand binding domain (ARNT-LBD) were created on the basis of structures of closely related respective proteins i.e., human Hif-2α and ARNT. Molecular docking of TCDD to the porcine AhR-LBD model revealed high binding affinity (-8.8kcal/mol) between TCDD and the receptor. Moreover, formation of the TCDD/AhR-LBD complex was confirmed experimentally with the use of electrophoretic mobility shift assay (EMSA). It was found that TCDD (10nM, 2h of incubation) not only bound to the AhR in the porcine granulosa cells but also activated the receptor. The current study provides a framework for examining the key events involved in the ligand-dependent activation of the AhR.

Molecular docking and 3D-QSAR studies on the glucocorticoid receptor antagonistic activity of hydroxylated polychlorinated biphenyls

The glucocorticoid receptor (GR) antagonistic activities of hydroxylated polychlorinated biphenyls (HO-PCBs) were recently characterised. To further explore the interactions between HO-PCBs and the GR, and to elucidate structural characteristics that influence the GR antagonistic activity of HO-PCBs, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed. Comparative molecular similarity indices analysis (CoMSIA) was performed using both ligand- and receptor-based alignment schemes. Results generated from the receptor-based model were found to be more satisfactory, with q(2) of 0.632 and r(2) of 0.931 compared with those from the ligand-based model. Some internal validation strategies (e.g. cross-validation analysis, bootstrapping analysis and Y-randomisation) and an external validation method were used respectively to further assess the stability and predictive ability of the derived model. Graphical interpretation of the model provided some insights into the structural features that affected the GR antagonistic activity of HO-PCBs. Molecular docking studies revealed that some key residues were critical for ligand-receptor interactions by forming hydrogen bonds (Glu540) and hydrophobic interactions with ligands (Ile539, Val543 and Trp577). Although CoMSIA sometimes depends on the alignment of the molecules, the information provided is beneficial for predicting the GR antagonistic activities of HO-PCB homologues and is helpful for understanding the binding mechanisms of HO-PCBs to GR.

Access Path to the Ligand Binding Pocket May Play a Role in Xenobiotics Selection by AhR

Understanding of multidrug binding at the atomic level would facilitate drug design and strategies to modulate drug metabolism, including drug transport, oxidation, and conjugation. Therefore we explored the mechanism of promiscuous binding of small molecules by studying the ligand binding domain, the PAS-B domain of the aryl hydrocarbon receptor (AhR). Because of the low sequence identities of PAS domains to be used for homology modeling, structural features of the widely employed HIF-2α and a more recent suitable template, CLOCK were compared. These structures were used to build AhR PAS-B homology models. We performed molecular dynamics simulations to characterize dynamic properties of the PAS-B domain and the generated conformational ensembles were employed in in silico docking. In order to understand structural and ligand binding features we compared the stability and dynamics of the promiscuous AhR PAS-B to other PAS domains exhibiting specific interactions or no ligand binding function. Our exhaustive in silico binding studies, in which we dock a wide spectrum of ligand molecules to the conformational ensembles, suggest that ligand specificity and selection may be determined not only by the PAS-B domain itself, but also by other parts of AhR and its protein interacting partners. We propose that ligand binding pocket and access channels leading to the pocket play equally important roles in discrimination of endogenous molecules and xenobiotics.

Study of novel pyrazolo[3,4-d]pyrimidine derivatives as selective TgCDPK1 inhibitors: molecular docking, structure-based 3D-QSAR and molecular dynamics simulation

We explored the structural features that have an impact on TgCDPK1 activity and TgCDPK1/Src selectivity by multi-computational methods with different statistical models.

Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes

An adverse outcome pathway (AOP) describes the causal linkage between initial molecular events and an adverse outcome at individual or population levels. Whilst there has been considerable momentum in AOP development, far less attention has been paid to how AOPs might be practically applied for different regulatory purposes. This paper proposes a scientific confidence framework (SCF) for evaluating and applying a given AOP for different regulatory purposes ranging from prioritizing chemicals for further evaluation, to hazard prediction, and ultimately, risk assessment. The framework is illustrated using three different AOPs for several typical regulatory applications. The AOPs chosen are ones that have been recently developed and/or published, namely those for estrogenic effects, skin sensitisation, and rodent liver tumor promotion. The examples confirm how critical the data-richness of an AOP is for driving its practical application. In terms of performing risk assessment, human dosimetry methods are necessary to inform meaningful comparisons with human exposures; dosimetry is applied to effect levels based on non-testing approaches and in vitro data. Such a comparison is presented in the form of an exposure:activity ratio (EAR) to interpret biological activity in the context of exposure and to provide a basis for product stewardship and regulatory decision making.

Prediction of binding affinities of PCDDs, PCDFs and PCBs using docking-based Comparative Molecular Similarity Indices Analysis

Polychlorinated Dibenzodioxins (PCDDs), Dibenzofurans (PCDFs) and Biphenyls (PCBs) are industrial compounds or byproducts that can cause toxic effects after binding to aryl hydrocarbon receptor (AhR). But the mechanism about PCDDs, PCDFs and PCBs binding to AhR is unclear. To study the interaction and significant amino acid residues in binding of PCDDs, PCDFs and PCBs to AhR, a docking-based Comparative Molecular Similarity Indices Analysis (CoMSIA) was performed on a set of structurally diverse PCDDs, PCDFs and PCBs with known binding affinities. The docking-based CoMSIA model (non-cross-validated regression coefficient of 0.942 and cross-validated regression coefficient of 0.768) was developed and compared with previous report, the presented docking-based CoMSIA model showed good robustness and predictive performance. The obtained docking conformations and predictive CoMSIA model could provide clues to understand key residues and interactions between receptor and compounds of interest.