Identification and Molecular Interaction Studies of Thyroid Hormone

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Identification and Molecular Interaction studies of Thyroid Hormone Receptor Disruptors among Household Dust Contaminants Jin Zhang, Yaozong Li, Arun A Gupta, Kwangho Nam, and Patrik L. Andersson Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.6b00171 • Publication Date (Web): 13 Jul 2016 Downloaded from http://pubs.acs.org on July 17, 2016

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Chemical Research in Toxicology

Identification and Molecular Interaction studies of Thyroid Hormone Receptor Disruptors among Household Dust Contaminants Jin Zhang†,1, Yaozong Li†,1, Arun A. Gupta†, Kwangho Nam†,*, Patrik L. Andersson†,* †

Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden.

Corresponding Authors *Email: [email protected]. Tel: +46-90-786-5266 *Email: [email protected]. Tel: +46-90-786-6570 Author Contributions 1

J.Z. and Y.L. contributed equally to this work.

ACS Paragon Plus Environment

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Abstract graphics


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ABSTRACT

Thyroid hormone disrupting chemicals (THDCs), often found abundantly in the environment, interfere with normal thyroid hormone signaling and induce physiological malfunctions, possibly by affecting thyroid hormone receptors (THRs). Indoor dust ingestion is a significant human exposure route of THDCs, raising serious concerns for human health. Here we developed a virtual screening protocol based on an ensemble of X-ray crystallographic structures of human THRβ1 and the generalized Born solvation model to identify potential THDCs targeting the human THRβ1 isoform. The protocol was applied to virtually screen an inhouse indoor dust contaminant inventory, yielding 31 dust contaminants as potential THRβ1 binders. Five predicted binders and one negative control were tested using isothermal titration calorimetry, of which four, i.e., 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), bisphenol A (3-chloro-2hydroxypropyl)

(2,3-dihydroxypropyl)

ether

(BADGE-HCl-H2O),

2,2',4,4'-

tetrahydroxybenzophenone (BP2), and 2,4-dichlorophenoxyacetic acid (2,4-D), were identified as THRβ1 binders with binding affinities ranging between 60 µM and 460 µM. Molecular dynamics (MD) simulations were employed to examine potential binding modes of these binders and provided a rationale for explaining their specific recognition by THRβ1. The combination of in vitro binding affinity measurements and MD simulations allowed identification of four new potential THR-targeting THDCs that have been found in household dust. We suggest using the developed structure-based virtual screening protocol to identify and prioritize testing of potential THDCs.


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INTRODUCTION Thyroid hormone (TH) homeostasis is important for macronutrient metabolism, thermogenesis and energy balance, cardiovascular function, and normal brain development in humans and wildlife.1 A broad range of environmental xenobiotics have been reported to disturb normal TH functions by interfering with TH signaling, potentially causing malfunctions in blood circulation, brain development and reproduction.2 These chemicals, referred to collectively as thyroid hormone disrupting chemicals (THDCs), are routinely detected in our environment and some e.g., 2,4,6-tribromophenol, 2,3,4,5,6-pentachlorophenol, and polybrominated diphenylethers (PBDEs) have been found in household dust.3-5 Animal and human studies have indicated that THDCs may pose a threat to human health.6-8 Of particular concern is exposure to these chemicals during critical stages of fetal development, such as during neurological development, which may lead to permanent brain function defects, such as cretinism, permanent hearing losses and structural abnormalities in the brain.6, 7 Indoor dust is a major source for human exposure to THDCs.9 Ingestion of dust containing THDCs, such as bromophenols and perfluoroalkyl and polyfluoroalkyl substances (PFASs), has been associated with alteration of TH levels in humans.5 We have previously identified 485 organic contaminants with different chemical composition in indoor dust.10 These dust contaminants were originated mainly from consumer products or outdoor environments.11 Major groups include flame retardants (e.g., PBDEs), pesticides, plasticizers (e.g., phthalates), PFASs, and classical persistent organic pollutants (e.g., polychlorinated biphenyls (PCBs) and dioxins).9 Among them, PBDEs, PCBs, and PFASs have been reported to cause thyroid-related adverse effects, such as metabolic diseases, reduced fertility and thyroid-related tumors.1 For some compounds, molecular targets of the TH system have been identified. For example, PBDE 181


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and 190 and certain PFASs, such as perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), have been shown to target transthyretin (TTR) and disturb TH transportation.12-14 Thyroid hormone receptors (THRs), which are activated by triiodothyronine (T3) and thyroxine (T4) under normal physiological condition,15 are potential molecular targets for THDCs.16 For example, bisphenol A and certain hydroxylated PBDEs (OH-PBDEs) (e.g., 4-OHPBDE 69 and 4-OH-PBDE 121) have been shown to target THR and affect THR-mediated gene expressions.16-18 The importance of THRs in TH homeostasis is well-studied in the field of computer-aided drug discovery,19-22 however only a few in silico based studies are published on the interaction with THRs of environmental pollutants.23-25 Improved knowledge of interactions of contaminants with THRs is essential for identification of THDCs targeting THRs and human health related risk assessments of dust contaminants. Virtual screening (VS) by molecular docking is a high-throughput approach for predicting protein-compound binding poses and identifying and prioritizing bioactive compounds for experimental testing.26-29 VS has been successfully applied to identify potential environmental pollutants targeting, e.g., the estrogen receptor alpha and haloalkane dehalogenases, from large libraries of industrial chemicals.30-32 VS has the potential to reduce costs and animal testing in chemical risk assessment processes by identifying the most hazardous chemicals or the most promising candidates in drug development.33, 34 In the present study, we have developed a VS protocol to identify dust contaminants targeting human THRβ1. In the protocol, two additional approaches were introduced for robust screening of compounds with diverse scaffolds and functional groups. First, instead of relying on a single X-ray structure, an ensemble of protein structures, i.e., the same protein with slightly different ligand binding site (LBS) conformations,35-37 was used to mimic protein flexibility.38 Second, a


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molecular mechanics based method, i.e., the generalized Born surface area (MM-GBSA) method,39 was used to for better estimation of solvation effects, thereby improve the performance of the VS protocol.40 The developed virtual screening model was then applied to screen dust contaminants targeting human THRβ1. THRβ1 was selected among the four possible isoforms of THR (i.e., THRα1, THRα2, THRβ1 and THRβ2),41 because of its importance to normal liver and kidney function, hearing development, and the feedback loop of the hypothalamic-pituitarythyroid axis.42 Disruption of THRβ1 by THDCs has also been suggested to be implicated in inadequate brain development and liver malfunctions.17, 43 Six representative dust contaminants (five predicted binders and one predicted non-binder) were selected for isothermal titration calorimetry (ITC) to determine their binding affinities to THRβ1. MD simulations were then carried out to examine the possible mechanism of THRβ1 ligand recognition and different dynamical behavior of THRβ1 upon binding of diverse binders. MATERIALS AND METHODS The study was conducted by following the procedure (Figure 1): I) development of the VS model, II) initial screening of 485 dust contaminants, yielding 131 initial hits, III) refinement of the initial hits, reducing the number of hits to 31 by ligand-protein interaction analysis, IV) selection of six representative compounds (five predicted binders and one predicted non-binder) based on their structural diversity and environmental relevance, and V) experimental measurement of their binding affinities to THRβ1 using ITC followed by MD simulations. Details of each of these five steps are given below. THRβ1 structure selection and preparation. Among 17 X-ray crystallographic structures of human THRβ1 in complex with various co-ligands available in the RCSB Protein Data Bank (PDB),44 seven structures were selected based on the structural diversity of their co-ligands and


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shape/size of their binding sites of the protein. The PDB ID of each structure is given in Table S1. For each structure, any mutated residue was restored to its corresponding wild-type residue, followed by addition of hydrogen atoms, optimization of hydrogen bonding networks and assignment of the protonation state of each residue to its corresponding protonation state at pH 7.0. The pronated state of each protein residue in or near the LBS was further inspected visually and corrected if needed. The structures were then minimized using the OPLS2005 force field to remove unfavorable high energy contacts.45 The preparation steps were carried out using the Protein Preparation Wizard of the Schrödinger Suite.46 As water molecules can mediate interactions between protein residues and ligands by forming hydrogen bonds with them, their presence can improve virtual screening performance.47-49 However, they can also hinder the identification of ligands by blocking direct interactions with protein.50 In the present work, both possibilities were considered by either including (PDB ID with “-W”) or excluding explicit waters (PDB ID with “-nW”) in any X-ray structure with identified crystal waters in its ligand binding site (LBS). This yielded a total of 12 protein structures (Table S2), which were then used in the development of the VS protocol. Ligand datasets for virtual screening (VS). Two different datasets were used in the study: 1) the THR subset of the Database of Useful Decoys: Enhanced (DUD-E),51 and 2) an indoor dust contaminant inventory we compiled previously.10 The THR subset of the DUD-E comprising 103 THR binders and 7450 in silico generated inactive decoys was used for the development and evaluation of the VS protocol. The dust contaminant inventory contained 485 organic contaminants that humans could potentially be exposed to through household dust. These compounds were preprocessed using the ChemAxon JChem Standardizer.52 For each compound, between 1 and 32 conformers were generated depending on the total number of chiral centers


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and their ionization and tautomeric states were determined at pH 7.0 ± 1.0, using the Schrödinger LigPrep53 and the Epik modules. Single structure docking, ensemble docking and MM-GBSA rescoring. The LBS of each THRβ1 structure was described by using a 10 Å cubic grid box centered on its corresponding coligand. The prepared compounds of the DUD-E THR subset were docked individually into each structure. For each conformer, a total of 5,000 poses were generated and ranked using the standard prevision (SP) scoring function of the Schrödinger Glide module.54 The top-ranked 400 poses were selected and subjected to 100 steps of conjugate gradient energy minimizations with a dielectric constant of 2.0. The results of each single structure docking were assessed by determining the area under curve (AUC) value of the receiver operating characteristic (ROC) curve and the enrichment factors (EFs) (Table S2). For each THRβ1 structure, the AUC value of the ROC curve was obtained by plotting the true positive rate (sensitivity) against the false positive rate (1-specficity) (Figure S1). We utilized AUC because its value is insensitive to the ratio of actives versus decoys in the DUD-E dataset.55 Enrichment was characterized by two EFs evaluated at 1% (EF1%) and 10% (EF10%) of the ranked database, respectively.56 Ensemble models were prepared by considering all possible combinations of the single structure docking results, from 2 structures up to all 12 structures. The top 20% of the DUD-E subset ranked by SP docking score was extracted for each single structure docking. Then, for a given combination of structures, the extracted results were combined to determine the overall performance, evaluated by the total number of active compounds identified by that particular combination. In Table S3, the results of the best performing ensemble model for each given number of protein structures are presented. The final ensemble model used for screening dust


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contaminants was selected by considering both the number of active compounds identified and total computational cost of docking. To further improve the VS performance of the selected ensemble model, we applied the MMGBSA method and rescored the ligand-THRβ1 docking poses. The calculation was performed using the Schrödinger Prime module with the VSGB 2.0 implicit solvent model and the OPLS2005 force field.57 During the rescoring, the MM-GBSA energy minimizations were performed by allowing the conformation of ligand to relax while the rest of the complex was held fixed. The MM-GBSA scores were determined for the resultant optimized poses. The results of the MM-GBSA rescoring were also assessed by the number of identified active compounds, AUC values and enrichment factors, and compared with other docking results, as shown in Table S4. Compounds-THRβ1 interaction analysis. The majority of the amino acid residues constituting the LBS of THRβ1 are hydrophobic. In contrast, only a few residues are hydrophilic, some of which have been suggested to play critical roles in the specific recognition of endogenous ligands and pharmaceuticals targeting THRβ1.22 Since hydrophilic residues in LBS can mediate electrostatic interactions with ligands, we reasoned that these residues, if any, may contribute toward specific binding of dust contaminants to THRβ1. We analyzed these interactions for all seven selected X-ray structures and counted the number of H-bonds and salt bridges between each ligand and THRβ1 based on the poses proposed by the MM-GBSA rescoring. These data were used to refine the 131 initial hits identified by the MM-GBSA scoring, yielding 31 final hits (Table S5). Isothermal titration calorimetry (ITC). Binding affinities of six dust contaminants and T3 (positive control) to THRβ1 were measured by using ITC. The six tested dust contaminants


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included five predicted binders and one predicted non-binder as a negative control (Table 1). Their purity, pKa values, and commercial sources are provided in Table S6. The human THRβ1 ligand binding domain (LBD) (6xHis plus Gly209-Asp461) was overexpressed in Escherichia coli BL21 (DE3) host cells by following the procedure outlined by Ye et al.22 The purified THRβ1 LBD was exhaustively dialyzed in phosphate buffer containing 20 mM sodium phosphate, 125 mM NaCl and 1 mM TCEP at pH 8.0. We prepared stock solutions of T3 (20 mM) and the six dust contaminants (100 mM) in dimethyl sulfoxide (DMSO). Before the ITC experiments, all stock solutions were diluted with phosphate buffer to a final level of 5% DMSO, as is a commonly acceptable DMSO concentration for ITC.58, 59 All experiments were conducted at 25°C by injecting aliquots of each compound (in syringe) into THRβ1-LBD buffer solution (in cell) using MicroCal Auto-iTC200 (Malvern Instruments Ltd, Worcester, UK). Initial “buffer scouting” was performed for each compound to avoid buffer mismatch caused by DMSO concentration differences between the cell and syringe. Raw data were collected, corrected for compound heats of dilution and integrated using MicroCal Origin. The ITC results (with “c” value < 5) were fitted to a single-site binding model and fixed stoichiometry parameter (N = 1), allowing estimation of the equilibrium dissociation constant (Kd) and enthalpy change (∆H).60, 61 Molecular dynamics (MD) simulations. The systems for MD simulations were prepared based on the 1Q4X THRβ1 LBD complex structure,62 which was the top performing single protein structure in docking (Table S2), and the poses from the VS protocol were used for the three dust contaminants (2,4,5-T, BADGE-HCl-H2O and BP2; abbreviations given in Table 1). The resulting complex (2,4,5-T system as an example) was solvated in an 85 Å rhombic dodecahedron water box containing 12475 TIP3P water molecules.63 The size of the water box was chosen to allow at least a 10 Å buffer space from each protein atom to the closest boundary


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of the water box. 0.15 M NaCl was added to neutralize each system and mimic biophysical conditions. For example, the resulting system for 2,4,5-T contained 41494 atoms (3981 protein atoms, 18 ligand atoms, 37425 water atoms, 40 Na+ and 30 Cl- ions). In addition, one apo THRβ1 LBD system was prepared as a reference by removing its ligand from the X-ray structure. Each system was initially energy minimized for 10,000 steps under a series of position restraints and constraints, heated to 300 K and then equilibrated under the constant temperature and pressure conditions for 1 ns using the CHARMM program (version 38a1).64 50 ns production MD simulation was performed for each system using the NAMD program (version 2.9).65 The temperature of each simulated system was maintained at its target value by using the Langevin thermostat with 5 ps-1 friction coefficient. The pressure was controlled by the Nosé-Hoover Langevin piston method with 200 ps piston period and 100 ps piston decay time. A 2 fs integration time step was used and any bond involving hydrogens was constrained to their corresponding force field bond lengths. Non-bonded van der Waals energies were evaluated by smoothly turning off their interaction energies using a switching function at a distance between 9 Å and 11 Å, and electrostatic interactions were evaluated using the particle mesh Ewald summation (PME) method.66 In all simulations, the CHARMM36 force field with the CMAP correction was used to present the THRβ1 LBD,64,

67

TIP3P for water molecules, and the

CGenFF36 force field for the three dust contaminants.68 Details of the partial optimization of the dust contaminant force field parameters are provided in the Supporting Information. RESULTS AND DISCUSSION Ensemble docking model with MM-GBSA rescoring. The reliability of the VS using Glide was examined by comparing the predicted poses of actual co-ligands found in the 12 human THRβ1 X-ray structures to their corresponding X-ray structures.54 The results showed that the


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docking simulation reproduced the actual binding poses with root-mean-squared-deviation (RMSD) lower than 2 Å for all compounds (Table S2).69 The DUD-E THR subset was then subjected to the Glide docking. The VS performance of (single structure) docking was compared between the 12 structures by examining the AUC value, total number of active compounds identified, and EF1% and EF10% values (Table S2) and 1Q4X-W showed the best performance with AUC of 0.863, good early enrichment (EF1% = 32.410 and EF10% = 7.180), and a total of 92 identified binders (out of 103 binders). The results showed that no single structure was able to identify all 103 binders with high AUC value. Thus, we combined different structures as an ensemble of structural models in order to identify a broader range of compounds with a high true positive versus false positive discrimination ratio. We examined all possible combinations of the ensemble structures from 2 structures up to all 12 structures and compared their performances based on the total number of identified binders (see Materials and Methods). Table S3 presents the best performing ensemble model for each given number of structures. The ensemble model with four structures (i.e., 1N46nW, 1Q4X-nW, 1R6G-nW, and 1XZX-nW) was chosen as our “final” ensemble model to identify dust contaminants targeting THRβ1. This model was chosen not only because it was one of the top models tested, successfully identifying 95 compounds out of the total 103 binders, but also because it showed reasonable computational cost, which is essential for high-throughput VS against large compound library. By comparison, the 1Q4X-W model, which is the top single structure model, only covered 75 binders when considering the top 20% hits (Table S4). Finally, the MM-GBSA method was applied to refine the selected ensemble docking model and its result, which yielded an AUC value that increased to 0.865 from 0.804 and EF10% to 7.478 from


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6.109, respectively (Table S4). The ROC curve of the model clearly showed the improvement of EF10% and AUC after the MM-GBSA rescoring (Figure S1). Virtual screening of household dust contaminants. Identification of potential binders of THRβ1 was achieved in two steps (Figure 1). In the first step, the VS protocol described above was applied to a recently compiled inventory of 485 indoor dust contaminants.10 A total of 131 compounds (top 20% hits) were identified using the developed VS protocol. For each dust contaminant, all stereoisomers and protonation states, in the range of pH 7.0±1.0, were considered during the virtual screening. Each stereoisomer and protonation form was docked into the THRβ1 structures and the top scored stereoisomer/protonation form was selected for subsequent MM-GBSA calculation. For instance, BADGE-HCl-H2O has four stereoisomers. Among them, the RR stereoisomer has scored the top docking score (Table S7), and selected for the MM-GBSA rescoring and MD simulation. In selecting protonation form, we have calculated the energetic penalty for each protonation state based on its population estimated using the Epik module available in the Schrödinger program70 and the penalty was considered in the docking score. For example, triclosan has a pKa of 7.9. The compound can exist as both the protonated and deprotonated form at pH 7.0±1.0. We selected the protonated form for later MM-GBSA rescoring, as the form has a more negative docking score and lower energy penalty (Table S8). In the second step, ligand-THRβ1 interactions were analyzed for all ligand-THRβ1 complexes, including the 7 X-ray structures (Table S1), to identify protein residues that form hydrogen bonds and/or salt bridge interactions with the bound ligands (see Materials and Method for details). Our rational for this step was two-fold. First, since most endogenous THR binders are recognized via specific hydrophilic interactions, this additional step would aid in selecting chemicals that can form such specific interactions. Second, because hydrophobic residues


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dominate the binding pocket of THRβ1, it is possible that our VS energy function underestimates hydrophilic interaction energies. This would then bias our selection toward certain chemicals that are more hydrophobic. With the aid of the second step, we intended to remedy the bias, thereby to improve selectivity between specific versus non-specific binders. Based on the analysis, we identified four hydrophilic residues, i.e. Arg282, Arg320, Asn331, and His435 in the binding pocket of THRβ1. We refined the 131 initial hits by analyzing their electrostatic interactions with these four residues and 31 dust contaminants displayed at least one electrostatic interaction with any of the hydrophilic residues (Table S5). The 31 potential binders can be classified into four major groups: 1) PFASs (10), 2) hydroxylated and/or halogenated aromatic compounds (14), 3) phenoxyacetic acids (3), and 4) miscellaneous (4) (Table S5). Interestingly, the present two-step screening protocol successfully identified known THR binders, such as bisphenol A (Ki = 200 µM),17 tetrabromobisphenol A (TBBPA, EC50 ≈ 100 µM),71 and four PFASs72 including perfluorooctane sulfonate (IC50 =13 µM)72, 73, perfluorononanoic acid (IC50 = 12 µM), perfluoroundecanoic acid (IC50 = 15 µM), and perfluoroheptanoic acid (IC50 = 9 µM), which indicated the selectivity of the present screening protocol. Measuring binding affinities of THDCs to THRβ1. Based on the VS results, six dust contaminants (five predicted binders and one predicted non-binder) (Table 1) were selected for measurement of binding affinities to THRβ1 by ITC. These represent different chemical structures, applications, and are environmentally relevant contaminants. The derived ITC thermograms (Figure 2) show clearly that four out of the five predicted THDCs bound to the THRβ1 LBD, with Kd values between 60 µM and 463 µM (Table 1), which can be compared with a typical hit rate around 40% for commonly practiced VS campaigns.74, 75 The results also


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revealed the lack of response for triclosan (the predicted non-binder), suggesting that the developed VS protocol can distinguish binders from non-binders. The derived thermodynamic data revealed that the binding of the four confirmed hits was largely enthalpy driven (Table S9). The docking poses and ligand-THRβ1 interactions of the potential THDCs were compared with the co-crystal pose of T3 (Figure S2). We also measured the binding affinity of the positive control T3 (Kd = 1.6 µM). Our value for T3 is comparable to the previously reported affinity of T3 to THRα1 (Kd = 1.8 µM), measured using a similar approach as ours.59 The similarity between the measured binding affinities of T3 may be due to only a single residue difference in their ligand-binding pockets, namely Ser277 in THRα1 and Asn331 in THRβ1.76 The present value, however, is lower than the affinity (0.26 nM)22 reported previously based on a radio ligand binding assay, which may be due to the ITC protocol that uses only the monomer and the LBD of the THR.59, 77 In the Supporting Information, a more thorough discussion on the difference of the two measurements is provided. In addition, we compared the ITC measured binding free energy (∆G) of the active compounds to their corresponding docking and MM-GBSA scores in Figure S3. Despite displaying reasonable correlation between the two sets of data, the scoring results were mainly used in the present study to prioritize the dust contaminants for the experimental testing, and not for the prediction of binding free energies. One

compound

was

identified

as

false

positive

by

ITC,

i.e.,

1-((2,4-

dichlorophenyl)carbamoyl)cyclopropanecarboxylic acid (cyclanilide). The false identification of cyclanilide may have been due to the negligence of intramolecular strain energy in the VS. Cyclanilide is structurally very similar to T3, but its cyclopropanyl group may have high strain energy, which prevents the formation of the proposed pose in the LBS of THRβ1. Poor buffer solubility of bisphenol A (3-chloro-2-hydroxypropyl) (2,3-dihydroxypropyl) ether (BADGE-


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HCl-H2O) could be due to its incomplete saturation of the THRβ1 LBD, allowing only an approximate estimation of its affinity (Figure 2). Binding pose examination by all-atom MD simulations. Three compounds (2,4,5-T, BADGE-HCl-H2O, and BP2) were selected for MD simulations based on their structural variation and the ITC data (Figure 3). Overall, all three compounds were stably bound near their corresponding poses predicted by the VS docking procedure during the entire MD simulations (Figure S5). Most notably, the MD-averaged structure of 2,4,5-T, which was the highest affinity binder among the six tested chemicals (Table 1), was nearly identical to its original pose (Figure 3B). Three types of interactions seemed to contribute to the stable binding of 2,4,5-T. Firstly, the carboxyl group of 2,4,5-T engaged in salt-bridged interactions with the two arginine residues (Arg282 and Arg316) in the THRβ1 binding pocket,78 holding the ligand in place throughout the entire MD simulations (Figure 3B). Secondly, chlorine atoms at the 2 and 5 positions of the phenoxy-group (here named Cl2 and Cl5, respectively; Figure S6) formed hydrophobic contacts with Ile275, Ala279, Met310, and Asn331, thereby restricting the motion of the ligand during the MD simulations. Thirdly, Cl5 formed a hydrogen-bond like interaction with Ser314 (the detailed interaction is shown in Figure S6). We suggest that this interaction, together with hydrophobic contact of Cl5 with Met310, contributed to the higher affinity binding of 2,4,5-T (Kd=60 µM) than 2,4-dichlorophenoxyacetic acid (2,4-D) (Kd=463 µM), which differ by only a single chlorine atom. For the remaining two compounds (BP2 and BADGE-HCl-H2O), the MD simulations showed ligand orientations that were slightly displaced relative to their predicted poses (Figure 3C and D). Analysis of the MD trajectories suggested that the displacement in both cases was partly caused by rotations around the two bonds at the central carbon atom (Figure S7), which may not


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be captured accurately in the VS docking. Nevertheless, the two ligands still bound in the binding pocket, likely stabilized by hydrophobic interaction between the two benzyl moieties and the hydrophobic residues in the THRβ1 LBD. In the Supporting Information, we provide a detailed analysis of the MD trajectories, including the distribution of the two rotatable bonds and the RMSD of ligands and protein backbone atoms. Environmental and toxicological implications of the identified THDCs. Four new potential THDCs targeting THRβ1 were identified in this study. To the best of our knowledge, none of these contaminants have previously been reported to bind to THR. It can be concluded that they are weak binders (Kd = 60-460 µM) in the range of previously reported THR binders, such as OH-PCBs (Ki = 30-90 µM)79, bisphenol A (Ki = 200 µM),17 and tetrabromobisphenol A (TBBPA, EC50 ≈ 100 µM).71 Exposure to weak THR binders e.g. bisphenol A during pregnancy has been associated to reduced TH levels in pregnant women and decreased thyroid-stimulating hormone in male neonates.80 Thus, these contaminants warrant further analysis as humans may be chronically exposed at doses which are similar or significantly higher than human T3 levels. Typical levels in human serum of T3 is 0.9-2.8 nM81 whereas levels of the contaminants in human urine of the general population have been reported to be 167 nM (37 ppb) for 2,4-D82, 220 nM (54.3 ng/ml) for BP283, and 8.7 nM (3.4 ng/ml) for BADGE-HCl-H2O84. Among the four contaminants, 2,4,5-T showed the highest affinity to THRβ1. This compound has been used as a herbicide for broad-leaved plants and found in dust samples collected from private homes and daycare centers, such as in North Carolina, USA.85 2,4,5-T has been confirmed to bind to transthyretin and disturb TH transportation.10 The compound is known to be associated with the development of hypothyroidism, and prostate and skin cancer.86-88 Exposure to 2,4,5-T has been shown to be linked to increased occurrence of gestational diabetes mellitus in


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women during pregnancy.89 2,4,5-T has also been reported as a weak estrogen receptor antagonist in reporter cell lines.90 The compound was a component of the defoliant Agent Orange deployed during the Vietnam War, exposure to which has been shown to be associated with the development of hypothyroidism, autoimmune thyroiditis and other endocrine disorders, however, which compounds in Agent Orange cause these diseases is not well understood.91 With a similar structure and application to 2,4,5-T, 2,4-D is still in use and among the most frequently detected herbicides in the USA.92 Although 2,4-D did not show any interaction with the estrogen, androgen, or aromatase/steroidogenesis pathways as evaluated in several in vitro assays,93 the compound has been reported to increase the prevalence of hypothyroidism in male applicators.88 The level of 2,4-D in their urine was detected to be 410-2500 ppb.82 2,4-D has been detected in dust samples from homes and daycare centers in North Carolina, Northern and Central California and Ohio, USA.94, 95 Both 2,4,5-T and 2,4-D are classified as a “possible human carcinogen” by the International Agency for Research on Cancer (IARC).96 BADGE-HCl-H2O is an additive or starting agent in personal care products, food packages and material coatings. The concentrations of BADGEs in the USA urine samples were 3-4 folds higher than the corresponding concentrations of bisphenol A.84 It was found in indoor dust samples from the USA, China, Korea, and Japan (444 ng/g).97 Nevertheless, we have not found any reports in the literature on thyroid related adverse effects of this compound. BP2 is a high volume production chemical98 and a representative of the benzophenone-type UV absorbers used in cosmetics and plastic products.99, 100 It was also found in household dust samples from the USA (49.8 ng/g) and East Asian countries (20.1 ng/g).101 The compound has been reported to disturb the transport of thyroid hormones by binding to transthyretin,10 and it inhibits human thyroid peroxidase and causes endometriosis.102,

103

In vivo experiments have


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shown that the compound induces weak anti-thyroidal activity in both rodent studies and an amphibian metamorphosis assay.104 BP2 has also been reported as an agonist for both human and fish (rainbow trout) estrogen receptors (ERα and ERβ).105 CONCLUSIONS Here, we developed a robust VS protocol to identify and prioritize potential THDCs targeting THRβ1 for further in vitro or in vivo toxicological evaluation of their thyroid-related adverse effects. The VS protocol was applied to screen an indoor dust contaminants inventory and the results suggested 31 dust contaminants including musk compounds, PFASs, and bisphenol A derivatives (Table S5) as potential binders to THRβ1. Five representative potential binders were selected and tested in vitro for binding to THRβ1 by ITC. The experiments confirmed that four compounds, i.e., 2,4,5-T, BADGE-HCl-H2O, BP2, and 2,4-D, are weak binders to THRβ1. Their potential binding modes were also validated and refined using MD simulations. The developed VS protocol showed to be capable to identify potential THDCs and we suggest further experimental testing and analysis of the top ranked dust contaminants including the four novel THR binders. ASSOCIATED CONTENT Supporting Information. Details of the crystal structures used in the study, the VS performance and enrichment of the models using single crystal structure, the ensemble models and ‘best’ ensemble models rescored with MM-GBSA; analysis of interactions between compounds and the hydrophilic residues, the compounds tested using ITC; force field parameters of the THRβ1 binders, comparison between docking poses of THR disrupters and co-crystal pose of T3, superposition of existing crystal structures, figures describing MD simulation results, and 3D-coordinates of ligand-THRβ1


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complexes used for the MD simulations can be found in the Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org. Notes The authors declare no competing financial interest. ACKNOWLEDGMENT The MM-GBSA calculations and MD simulations were conducted using the resources provided by the Swedish National Infrastructure for Computing (SNIC) at High Performance Computing Center North (HPC2N) and National Supercomputing Center (NSC). The THRβ1LBD was expressed by the Protein Expertise Platform, Umeå University. Funding Sources This study was financed by the MiSSE project through grants from the Swedish Research Council for the Environment, Agricultural Sciences and Spatial Planning (Formas) (210-2012131), by Umeå University, by the Kempe foundation, and by the Swedish Research Council (VR) (521-2011-6427 and 2015-04114).

ABBREVIATIONS 2,4,5-T, 2,4,5-trichlorophenoxyacetic acid; 2,4-D, 2,4-dichlorophenoxyacetic acid; AUC, area under curve; BADGE-HCl-H2O, bisphenol A (3-chloro-2-hydroxypropyl) (2,3-dihydroxypropyl) ether;

BP2,

2,2',4,4'-tetrahydroxybenzophenone;

Cyclanilide,

1-((2,4-

dichlorophenyl)carbamoyl)cyclopropanecarboxylic acid; DMSO, dimethyl sulfoxide; DUD-E, database of useful decoys: Enhanced; EC50, half maximal effective concentration; EF,


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enrichment factor; ER, estrogen receptor; IARC, international agency for research on cancer; IC50, half maximal inhibitory concentration; ITC, isothermal titration calorimetry; LBD, ligand binding domain; LBS, ligand binding site; MD, molecular dynamics; MM-GBSA, molecular mechanics-generalized

Born

surface

area;

OH-PBDE,

hydroxylated

polybrominated

diphenylether; PBDE, polybrominated diphenylether; PCB, polychlorinated biphenyl; PDB, protein

data

bank;

PFAS,

perfluoroalkyl

and

polyfluoroalkyl

substance;

PFOA,

perfluorooctanoic acid; PFOS, perfluorooctanesulfonic acid; PME, particle mesh Ewald; RMSD, root mean squared deviation; ROC, receiver operating characteristic; SP, standard precision; T3, triiodothyronine;

T4,

thyroxine;

TBBPA,

tetrabromobisphenol

A;

TCEP,

tris(2-

carboxyethyl)phosphine; TH, thyroid hormone; THDC, thyroid hormone disrupting chemical; THR, thyroid hormone receptor; TTR, transthyretin; VS, virtual screening; ∆G, binding free energy; ∆H, enthalpy change;


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Table 1. Studied compounds with chemical abstract services (CAS) registry numbers, abbreviations, industrial application, prediction from the virtual screening protocol and binding affinities (Kd) determined using isothermal titration calorimetry. Compounds (abbreviations)

CAS No.

Applications

Prediction Kd (µM)a

triiodothyronine (T3)

689302-3

Positive control

-

1.6

2,4,5-trichlorophenoxyacetic acid (2,4,5-T)

93-76-5

Herbicide

Active

60

bisphenol A (3-chloro-2-hydroxypropyl) (2,3- 227947- Plasticizer dihydroxypropyl) ether (BADGE-HCl-H2O) 06-0

Active

87.5b

2,2',4,4'-tetrahydroxybenzophenone (BP2)

131-555

UV filter

Active

200

2,4-dichlorophenoxyacetic acid (2,4-D)

94-75-7

Herbicide

Active

463

1-((2,4113136- Herbicide dichlorophenyl)carbamoyl)cyclopropanecarboxylic 77-9 acid (Cyclanilide)

Active

ND

5-chloro-2-(2,4-dichlorophenoxy)phenol (Triclosan)c

338034-5

Antimicrobial Inactive agent (Negative control)

ND

a

ND refers to non-detected affinity; bThe binding affinity of BADGE-HCl-H2O was only an approximate value, probably due to poor solubility in the ITC buffer; cTriclosan was tested as a negative control. It was identified among the 131 initial hits but showed no interaction with the four hydrophilic residues (Figure 1).


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Figure 1. Scheme adopted in this study with details of the virtual screening (VS) model development process, the isothermal titration calorimetry (ITC) validation study and the molecular dynamics (MD) study of ligand-protein interactions. ssDock model: single structure docking model; esDock model: ensemble structure docking model. In brackets are the numbers of hits at different steps.


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Figure 2. Molecular structures of active compounds and thermograms of their binding to THRβ1 determined by isothermal titration calorimetry. The abbreviations of compounds are defined in Table 1. For BADGE-HCl-H2O, the dissociation constant (Kd) was an approximate value likely due to its poor solubility in the ITC buffer.


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Figure 3. Comparison of docking and MD-averaged poses of potential THR disrupters: (B) 2,4,5T, (C) BP2, and (D) BADGE-HCl-H2O. In (A), the THRβ1 ligand binding domain is shown in cartoon, and the location of the ligand binding pocket is indicated with the two helices (H3 and H5), a binder (2,4,5-T, pink ball-and-stick) and two arginine residues (Arg282 and Arg316). In (B)-(D), MD-averaged poses (from the last 10 ns) are shown in stick with different colors, and VS proposed poses in gray stick, respectively. In (B), the electrostatic interactions between ligands and residues are shown in purple dash.


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Identification and Molecular Interaction Studies of Thyroid Hormone

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