Emerging Polar Phenolic Disinfection Byproducts Are High-Affinity

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Ecotoxicology and Human Environmental Health

Emerging Polar Phenolic Disinfection Byproducts Are High-Affinity Human Transthyretin Disruptors: an in Vitro and in Silico Study Xianhai Yang, Wang Ou, Yue Xi, Jingwen Chen, and Huihui Liu Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 22 May 2019 Downloaded from http://pubs.acs.org on May 29, 2019

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Environmental Science & Technology

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Emerging Polar Phenolic Disinfection Byproducts Are High-Affinity Human

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Transthyretin Disruptors: an in Vitro and in Silico Study

3 4

Xianhai Yang*,†,‡, Wang Ou†, Yue Xi†, Jingwen Chen§, Huihui Liu*,†

5 6

†Jiangsu

Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental

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and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

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‡Nanjing

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Republic of China, Nanjing 210042, China

Institute of Environmental Science, Ministry of Ecology and Environment of the People’s

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§Key

Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School

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of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China

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ABSTRACT

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Phenolic disinfection byproducts (phenolic-DBPs) have been identified in recent years. However,

14

the toxicity data for phenolic-DBPs are scarce, hampering their risk assessment and development of

15

regulations on acceptable concentration of phenolic-DBPs in water. In this study, the binding potency

16

and underlying interaction mechanism between human transthyretin (hTTR) and five groups of

17

representative

18

hydroxybenzaldehydes, 3,5-dihalo-4-hydroxybenzoic acids, halo-salicylic acids) were determined and

19

probed by competitive fluorescence displacement assay integrated with in silico methods. Experimental

20

results

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hydroxybenzaldehydes have high binding affinity with hTTR. The hTTR binding potency of the

22

chemicals with electron-withdrawing groups on their molecular structures were higher than that with

23

electron-donor groups. Molecular modeling methods were used to decipher the binding mechanism

24

between model compounds and hTTR. The results documented that ionic pair, hydrogen bonding and

25

hydrophobic interactions were dominant interactions. Finally, a mechanism-based model for predicting

26

the hTTR binding affinity was developed. The determination coefficient (R2), leave-one-out cross

27

validation Q2 (Q2LOO), bootstrapping coefficient (Q2BOOT), external validation coefficient (Q2EXT) and

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concordance correlation coefficient (CCC) of the developed model met the acceptable criteria (Q2 >

29

0.600, R2 > 0.700, CCC > 0.850), implying that the model had good goodness-of-fit, robustness and

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external prediction performances. All the results indicated that the phenolic-DBPs have the hTTR

31

disrupting effects, and further studies are needed to investigate their other mechanism of endocrine

32

disruption.

phenolic-DBPs

implied

that

(2,4,6-trihalo-phenols,

2,4,6-trihalo-phenols,

2,6-dihalo-4-nitrophenols,

2,6-dihalo-4-nitrophenols


and

3,5-dihalo-4-

3,5-dihalo-4-

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INTRODUCTION

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Disinfection byproducts (DBPs) are formed by the reaction of chemical disinfectants (e.g.

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chlorine, chlorine dioxide, chloramine and ozone) with natural organic matter, anthropogenic

36

contaminants and halogen, in the disinfection processes for drinking water, swimming pool water,

37

wastewater, aquaculture water, etc.1-6 Since 1970s, over 700 DBPs have been identified.7,8

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However, it was estimated that this number of DBPs represents merely a fraction of the total

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organic halogen generated in the disinfection processes.9 In addition, only ~ 100 DBPs have been

40

evaluated by systematic toxicological analyses,10 meaning there are great knowledge gaps for the

41

toxicological information of DBPs.11 Thus, further studies are necessary to identify emerging

42

DBPs, test their toxicity, and probe underlying toxic mechanisms of action.

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Recently, dozens of polar phenolic DBPs (phenolic-DBPs) included halogenated-phenols,

44

halogenated-4-nitrophenol,

halogenated-4-hydroxybenzaldehydes,

halogenated-4-

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hydroxybenzoic acids, halogenated-salicylic acids are identified.5,12-16 Although the toxicity

46

information for those DBPs are scarce up to now, an increasing concern is being given because

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their toxicity profile is distinct with the commonly known aliphatic halogenated DBPs. For

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example, Yang et al.,13 and Pan et al.,17 evaluated the developmental toxicity of these emerging

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pollutants on marine polychaete Platynereis dumerilii. Their results indicated that the

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developmental toxicity of phenolic-DBPs was higher than that of aliphatic halogenated DBPs.

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Moreover, this law was also observed in the studies of algae toxicity,18,19 and CHO cells

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cytotoxicity15. Besides the observed toxicology effects, it is still unknown about what are other

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potential adverse biological effects for those emerging DBPs? What are the underlying molecular

54

mechanisms of action? The limited information has hampered their risk assessment and

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development of regulations on acceptable concentration of the emerging DBPs in water.

56

It was reported that DBPs could be absorbed into human bodies through several routes, such

57

as ingestion, dermal absorption, inhalation,20-22 which resulted in many DBPs had been detected

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in human blood, urinary, alveolar air samples, etc.4,23-25 The DBPs in human sera may bind to

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various serum proteins. It is well known that many serum proteins are critical deliverers of 3 ACS Paragon Plus Environment


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endocrine hormones.26,27 Thus, DBPs binding to the serum proteins may result in the decrease of

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available protein binding sites for the endocrine signaling molecules, even altering their

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homeostasis. However, research for this point has not been reported. Although no previous study

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documented that the phenolic-DBPs has been detected in human blood, it has been reported that

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the phenolic-DBPs could permeate across human skin.28 Besides, halo-phenols from other sources

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were detected in human blood.29-31 Thus, it is a conceivable hypothesis that phenolic-DBPs could

66

find their way into our blood. In this regard, it is of vital importance to determine the potential

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binding ability of the emerging phenolic-DBPs to serum proteins, as well as to reveal underlying

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mechanism of binding action.

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As mentioned above, there are big data gaps for more than 85% (~600) identified DBPs, let

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alone the new DBPs discovered continually. Due to time and cost limitations, the biological in

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vivo and/or in vitro testing of all potentially DBPs is unrealistic. It is therefore necessary to employ

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an efficient method to analyze their capacity to induce adverse biological effects and reveal

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underlying mechanisms. In silico method are considered as a fast, cost-efficiently and powerful

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tool in predicting the potential toxicity and deciphering the mechanism of action.32-36 To date,

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only few in silico models were developed to predict the toxicity of DBPs. The endpoints included

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the developmental toxicity, carcinogenicity, mutagenicity, reactive toxicities, genotoxicity.11,37,38

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However, no predictive models are available for the endpoints of serum proteins binding.

78

Therefore, development of a predictive model is also meaningful to fill the data gap of other

79

phenolic-DBPs with similar structures on their serum proteins binding potency.

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In this study, the human transthyretin (hTTR) binding potency of phenolic-DBPs was

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investigated by in vitro integrated with in silico methods. The reasons why hTTR is selected as

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model serum proteins are: (a) hTTR is the major thyroid hormones (THs) carrier in the brain and

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fetal tissue and it is also responsible for transporting THs across protective physiological barriers,

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such as the placenta and the blood−brain barrier.39 Disrupting the hTTR transport process may

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result in transporting the pollutant to normally inaccessible sites of action and eliciting deleterious

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health effects;40 (b) previous experimental results indicated that the most potent hTTR binders 4 ACS Paragon Plus Environment

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were aromatic, hydroxylated, and halogenated.41 In view of the molecular structure, the phenolic-

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DBPs may be potential hTTR binders. Herein, we first determined the binding affinity of 17

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phenolic-DBPs with hTTR by competitive fluorescence displacement assay. Then, the underlying

90

binding mechanisms were deciphered using molecular modeling methods. Lastly, a mechanism-

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based quantitative structure-activity relationship (QSAR) model was developed and validated.

92 93

MATERIALS AND METHODS

94

Reagents and Chemicals

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A total of 17 phenolic-DBPs including five 2,4,6-trihalo-phenols, two 2,6-dihalo-4-

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nitrophenols, three 3,5-dihalo-4-hydroxybenzaldehydes, five halo-salicylic acids, two 3,5-dihalo-

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4-hydroxybenzoic acids were selected as model compounds (Table 1). Furthermore, in order to

98

clarify the influence of substituent groups on the hTTR binding affinity, other 6 compounds

99

resembling the structure of 2,4,6-tribromophenol were also selected. The model compounds are

100

dissolved in dimethyl sulfoxide (DMSO). Thyroxine (T4) is dissolved in 10 mM NaOH solution.

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hTTR and 8-Anilino-1-naphthalenesulfonate ammonium (ANSA) were prepared in 100 mM

102

NaCl/50mM Tris–HCl, pH 7.40 ± 0.02. All chemicals and solvents are analytical grade. The

103

molecular structures and reagent purchase information of chemicals and solvents were listed in

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Figure S2 and Table S1 of Supporting Information, respectively.

105

ANSA-based competitive fluorescence displacement assay

106

Competitive fluorescence displacement assay have been considered as a powerful and

107

promising tool to assess the hTTR binding capacity of compounds by scientific community,26,42-

108

45

109

to justify the selection of ANSA-based competitive fluorescence displacement assay was listed in

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Text S1. We determined the binding constant (Kd,ligand) and 50% inhibition concentration (IC50) of

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model compounds with hTTR by employing a modified procedure. Fluorescence measurements

112

were carried out by an INESA 970CRT fluorescence spectrometer (Shanghai Instrument Electric

113

Analytical Instrument Co., Ltd, China). For each test, three independent repeats were performed.

and Organization for Economic Cooperation and Development (OECD).46,47 The explanation



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Direct fluorescent ligand binding measurement was performed to measure the binding constant

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(Kd,probe) of ANSA with hTTR. Different volumes of ANSA were added from the stock solution

116

to 1.82 μM hTTR (total volume 2,000 μL) to obtain the required concentration. After being

117

allowed to stand for 5 min at room temperature, the fluorescence emission spectrum of each

118

solution was recorded. The excitation wavelength and emission wavelength of fluorescence

119

spectrophotometer are set at 380 nm and 470 nm respectively. The Kd,probe value of ANSA with

120

hTTR was 2280 ± 257 nM, calculated by nonlinear regression curve-fitting of the binding data

121

using the Graphpad Prism software (Figure S3).

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Competitive binding assay was carried out to measure the binding ability of T4 and other

123

compounds with hTTR. 0.5 μM hTTR and 50 μM ANSA were mixed in a total volume of 2,000

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μL and incubated for 5 min at room temperature. Then, the ligand was continually added every

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five minutes, guaranteed total volume change is less than 3%. The solvent effect was investigated

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and the results indicated that there is no effect on the fluorescence intensity (Fλ) of the system

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when DMSO concentration is below 5% (Figure S4). The Fλ at 470 nm after ligand addition was

128

measured. The relative fluorescence intensity (RFλ) of each ligand concentration was expressed

129

as:

130

RFλ 

F 0 F i

(1)

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where Fλ-0 and Fλ-i are the maximum fluorescence intensity without and with i-ligand

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concentration of model compounds, respectively. Then, the average relative fluorescence

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intensity of three independent repeats for each compound was plotted as a function of ligand

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concentration. The competition curves were fitted with a sigmoidal model to derive an IC50 value

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by Origin (Northampton, MA, USA). Then the binding constants Kd,ligand of the compounds with

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hTTR were calculated as:

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K d,ligand 

IC50,ligand  K d,probe

(2)

[ ANSA]

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where IC50,ligand is the half-maximal inhibitory concentration of model compounds; [ANSA] is the

139

concentration of ANSA. In order to reduce the bias in different laboratories and compare with the


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data in other literatures, we also selected the logarithm of the relative competitive potential with

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T4 to evaluate a chemical binding capacity (logRP).

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log RP  log

IC50,T4

(3)

IC50,ligand

143

where IC50,T4 and IC50,ligand are the half-maximal inhibitory concentration of T4 and model

144

compounds, respectively.

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Molecular modeling

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Molecular modeling was used to reveal underlying interaction mechanisms between model

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compounds and hTTR. The CDOCKER protocol in Discovery Studio 2.5.5 (Accelrys Software

148

Inc.) was adopted to determine the initial potential bioactive conformations of the model

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compounds binding to hTTR. The hTTR crystal structure was obtained from RCSB Protein Data

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Bank (http://www.rcsb.org/pdb/home/home.do) with PDB ID 1ICT (3 Å). The ionizable function

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groups in both hTTR and model compounds were protonated or deprotonated under pH = 7.40

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condition.48

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The conformation determined by molecular docking was further optimized by molecular

154

dynamic (MD) simulation. All the calculations related to the MD simulation and the topology,

155

and coordinate files were generated by using AMBER 12 package and AMBER tools 12.49 R.E.D.

156

Server Development with restrained electrostatic potentials (RESP) method were used to derive

157

the atomic partial charges of model compounds.50, The atom types, bonds and angle parameters

158

of ligands and complex were built using GAFF Force Field and ff12SB Force Field,

159

respectively.51 Each complex was neutralized by the counter ion (e.g. Na+) and was solvated into

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a 9.0 Å cuboid TIP3P water box.52 The total number of atoms for 23 simulated systems were listed

161

in Table S2.

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The MD simulations were performed with a time step of 2 fs. The long-range electrostatics

163

was treatment by Particle Mesh Ewald (PME) with a non-bonded cutoﬀ of 8 Å. Hydrogen atoms

164

in each complex were constrained with the SHAKE algorithm.53 During the MD simulations, each

165

complex was minimized with 2000 cycles of steepest descent and conjugates gradient



166

minimization, respectively, and was gradually heated from 0 to 300K in the NPT ensemble over

167

a period of 50 ps. Finally, 5 ns MD simulations were performed. The atom coordinates were saved

168

every 10 ps (100 frames/ns).54 Based on the results from MD simulation, we analyzed the binding

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pattern of DBPs in the activity site of hTTR. The binding pattern includes orientation of ionizable

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function groups and noncovalent interaction.48,54 The orientation of ionizable function groups was

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illustrated by Discovery Studio 2.5.5. The ionic pair interaction, halogen bonds, hydrogen bonds

172

were analyzed by an in-house program. For ionic pair interaction, we analyzed the oxygen−

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nitrogen distances (dO−N) between the ionized groups in the model compounds and the -NH3+

174

group of Lys 15 in hTTR. If there exist stable ionic pair interaction, the dO−N is ≤ 5 Å.55 For

175

hydrogen bond, the criteria are: a) the distance between a hydrogen atom and an electron acceptor

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atom (dA···H) is < their sum of van der Waals radii; b) the D-H···A angle is > 135°.56 For halogen

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bond, the criteria are: a) the distance between halogen atom and electron donor atom (dX···D) is
140°.57 The hydrophobic interaction

179

was illustrated by LigPlot+ program.58

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QSAR modeling

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According to the interaction mechanism analysis results, fifteen molecular descriptors were

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selected to describe the interactions of the model compounds with hTTR (Table S3). Fourteen

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chemical form adjusted quantum chemical descriptors were selected to characterize the hydrogen

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bonding and ionic pair interaction.48,54 The logarithm of the n-octanol/water distribution

185

coefficient (logD) characterizes the hydrophobic interaction.59 The model compounds’ geometry

186

were optimized at the B3LYP/6-31+G(d,p) level using Gaussian 16 program package.60 Then, the

187

quantum chemical descriptors were extracted from the Gaussian 16 output files. logD were

188

calculated by the Calculator Plugins from MarvinSketch 15.6.29.0, 2015, ChemAxon

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(http://www.chemaxon.com) at the pH = 7.40 condition.

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In the QSAR modeling, the observed data set was divided into a training set of 18 compounds

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and a validation set of 5 compounds at random. Stepwise multiple linear regression (MLR)

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analysis was used to select the predictive variable and construct the QSAR model employing the 8 ACS Paragon Plus Environment

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SPSS 19.0 software. The internal and external prediction performances, applicability domain of

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derived model were assessed by following the OECD QSAR model validation guideline.61,62 The

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determination coefficient (R2), leave-one out cross validation Q2 (Q2LOO) and bootstrapping

196

coefficient (Q2BOOT) were used to evaluate the goodness-of-fit and robustness. The external

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predictive ability was assessed by external validation coefficient (Q2EXT) and the concordance

198

correlation coefficient (CCC) of the validation set. The Y-scrambling technique was adopted to

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check the chance correlation of model. In addition, the root mean square error (RMSE), standard

200

errors (SE), and the mean absolute error (MAE) were also used to evaluate the prediction error.

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The variance inflation factor (VIF) was calculated and used to quantify the severity of

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multicollinearity.63

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The applicability domain (AD) of the developed model was assessed using the Euclidean

204

distance-based method and the Williams plot. Euclidean distance-based method was incorporated

205

into

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(http://ambit.sourceforge.net/download_ambitdiscovery.html). Williams plot of standardized

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residuals (δ) versus leverage values (h) was illustrated, in which compounds with the absolute

208

values of standardized residual |δ| > 3 were recognized as outliers. The definition of δ and h were

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detailed in our previous studies.64

the

AMBIT

Discovery

(version

0.04)

210 211

RESULTS AND DISCUSSION

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hTTR binding potency of phenolic-DBPs

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The fluorescence spectra and fluorescence displacement curve of T4 and model compounds are

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listed in Figures 1, S5 and S6. The calculated binding constants Kd,ligand, IC50 and logRP values of

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model compounds are shown in Table 1. The hTTR binding potency of 2,4,6-trichlorophenol and

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2,4,6-tribromophenol was tested in previous study. The logRP values were -0.314 ± 0.0314 and -

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0.47765, 0.267 ± 0.0358 and 0.30166/ 0.47767/ 0.62568 for 2,4,6-trichlorophenol and 2,4,6-

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tribromophenol in our work and previous study, respectively, indicating the logRP values tested

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here are agreement with previously published data. 9 ACS Paragon Plus Environment


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As shown in Figure 2, the tested five groups of phenolic-DBPs exhibited distinct hTTR

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binding potency. The rank order of the hTTR binding affinity was 2,4,6-trihalo-phenols, 2,6-

222

dihalo-4-nitrophenols > 3,5-dihalo-4-hydroxybenzaldehydes > 3,5-dihalo- 4-hydroxybenzoic

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acids, halo-salicylic acids. In addition, the logRP values of 2,4,6-trichlorophenol, 2,4,6-

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tribromophenol, 2,4,6-triiodophenol are -0.314 ± 0.0314, 0.267 ± 0.0358 and 0.421 ± 0.0179,

225

respectively, implying the hTTR binding potency of iodo-phenolic-DBPs > bromo-phenolic-

226

DBPs > chloro-phenolic-DBPs. Previous evidence also shown that iodinated DBPs generally has

227

significantly the most cytotoxic and genotoxic.1,13,14

228

According to the criteria for classification proposed by Hamers et al.,69 the compound with

229

logRP > -1.26 is considered as a high potency hTTR binder, with -2.26< logRP < -1.26 is a

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moderate potency hTTR binder, with logRP < -2.26 is a low potency hTTR binder (Table S4). In

231

this

232

hydroxybenzaldehydes are high potency hTTR disruptors. Among the 2,4,6-trihalo-phenols and

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2,6-dihalo-4-nitrophenols, there are four phenolic-DBPs named 2,4,6-tribromophenol, 2,4,6-

234

triiodophenol, 2,6-dibromo-4-chlorophenol and 2,6-dibromo-4-nitrophenol with their logRP > 0,

235

indicating the binding affinity of those compounds is higher than that of T4. 3,5-dichlorosalicylic

236

acid, 3-bromo-5-chlorosalicylic acid and 3,5-dibromosalicylic acid are moderate potency hTTR

237

binders. 5-chlorosalicylic acid, 5-bromosalicylic acid, 3,5-dichloro-4-hydroxybenzoic acid and

238

3,5-dibromo-4-hydroxybenzoic acid are low potency hTTR binders. Gales et al.,70 determined the

239

binding affinity of salicylic acid, 5-iodosalicylic acid and 3,5-diiodosalicylic acid with hTTR

240

using radiolabeled ligand displacement method. They observed that salicylic acid do not compete

241

with T4, while 5-iodosalicylic acid presents a very low competition with T4; the logRP value of

242

3,5-diiodosalicylic acid is -0.890. Taken as a whole, our results indicated that 2,4,6-trihalo-

243

phenols, 2,6-dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes and polyhalogenated

244

salicylic acid have high priority for further in vivo testing.

case,

the

2,4,6-trihalo-phenols,

2,6-dihalo-4-nitrophenols,

3,5-dihalo-4-

245

It is well known that ionization of a compound makes it more water soluble and then less

246

lipophilic.71 The logD is usually employed to describe the distribution ability of ionizable 10 ACS Paragon Plus Environment

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compounds from water to organic phase (e.g. protein).72 For analogues, a higher logD values

248

usually means a higher distribution ability from water to proteins. As shown in Table 1, the logD

249

values of 3,5-dihalo- 4-hydroxybenzoic acids and halo-salicylic acids range from -1.28 to 0.

250

While the logD values of 2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols and 3,5-dihalo-4-

251

hydroxybenzaldehydes range from 0.59 to 3.8. In addition, a statistically significant positive

252

linear correlation between our observed logRP values and logD of model compounds (n = 23, r =

253

0.867, p < 0.0001) was found, indicating that a compound with higher logD value may lead to a

254

higher logRP value. This result implied that lower distribution ability of 3,5-dihalo-4-

255

hydroxybenzoic acids, halo-salicylic acids from water to the hTTR may be responsible for the

256

low hTTR binding potency of the two groups phenolic-DBPs. Recently, our QSAR modeling

257

results indicated that the logD had a positive correlation with the bovine serum albumin-water

258

partition coefficients,73 chicken and fish muscle protein – water partition coefficients59 of

259

ionizable compounds, implying that logD can be used to describe the distribution ability of

260

ionizable compounds from water to bovine serum albumin, chicken and fish muscle protein.

261

Influence of substituent groups on the binding of compounds with hTTR

262

As shown in Figure S7, the prominent difference for some phenolic-DBPs is that there is a

263

different substituent group in the para-position compared with 2,4,6-tribromophenol. In order to

264

clarify the influence of substituent groups on the hTTR binding interaction, other six substituent

265

groups were further introduced (Figure S7), i.e. -CH3 (2,6-dibromo-4-methylphenol), -COCH3

266

(3,5-dibromo-4-hydroxyacetophenone), -CN (3,5-dibromo-4-hydroxybenzonitrile), -NH2 (4-

267

amino-2,6-dibromophenol), -CF3 (3,5-dibromo-4-hydroxybenzotrifluoride), -COOCH3 (3,5-

268

dibromo-4-hydroxybenzoic acid methyl ester). Among the studied substituent groups, there are

269

three electron-donor groups (i.e. -COO-, -NH2, -CH3) and eight electron-withdrawing groups (i.e.

270

-COCH3, -COOCH3, -CHO, -CN, -Cl, -CF3, -Br, -NO2).74

271

The logRP values of those analogues are -2.48 ± 0.0899 (-COO-), -0.665 ± 0.0470 (-NH2), -

272

0.190 ± 0.0699 (-CH3), -0.530 ± 0.0233 (-COCH3), -0.515 ± 0.0167 (-COOCH3), -0.459 ± 0.0109

273

(-CHO), 0.00962 ± 0.0144 (-CN), 0.128 ± 0.0182 (-Cl), 0.212 ± 0.0316 (-CF3), 0.267 ± 0.0358 (11 ACS Paragon Plus Environment


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Br), 0.304 ± 0.0515 (-NO2). The results indicated that (a) the logRP values for all of the six new

275

introduced compounds is > -1.26 (Figure 2), implying that the six compounds are high potency

276

hTTR disruptors; (b) the substituent group in para-position influence the binding interaction

277

between hTTR and compounds; (c) the logRP values of chemicals with electron-withdrawing

278

groups on their structures are higher than that of chemicals with electron donor groups except for

279

-CH3.

280

A quantitative contribution of the substituent groups on the binding interaction was also

281

attempted to establish by employing Hammett substituent constants (σ) (Table S5). However, no

282

significant linear correlation was observed between σ and logRP (Figure S8). The reason why

283

2,6-dibromo-4-methylphenol (-CH3) exhibited high hTTR disrupting affinity is that the

284

interaction between model compounds and hTTR is positively correlated with logD. The logD

285

value of 2,6-dibromo-4-methylphenol (-CH3) is second largest among the model compounds.

286

Binding mechanism analysis

287

The binding pattern of the studied model compounds in the hTTR ligand binding domain was

288

analyzed. Among the 23 studied compounds, 16 compounds were found to have ionized groups

289

that point towards the mouth of the T4 binding site (Figure S9). Thus, in the dominant

290

conformations of the anionic forms binding with hTTR, the ionized groups point towards the

291

entry port of hTTR. Formation of the orientational noncovalent interactions may explain this

292

phenomenon.

293

According to the noncovalent interaction analysis results, we found that all of the compounds

294

contained the conformation with oxygen− nitrogen distances (dO−N) between the ionized groups

295

in the ligands and the −NH3+ group of Lys 15 ≤ 5 Å, documenting the anionic groups of the model

296

compounds form ionic pair interactions with the -NH3+ group of Lys15 in hTTR. To evaluate the

297

stability of the ionic pair interactions, we further measured the cumulative percentage of the

298

distance for the model compounds by calculating the ratio of conformations with ionic pair to the

299

total conformations (500 frames). For the 23 compounds, the occupancy ratio of ionic pair for 16


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300

compounds were > 90 % (Figure 3), indicating that the formed ionic pair interactions is stable for

301

those compounds.

302

The hydrogen bond analysis results indicated that all of the compounds can form hydrogen

303

bond with the amino acid residues in hTTR. The observed total hydrogen bonds occupancy ratio

304

for 16 compounds is > 80%, implying the hydrogen bonds between the ionizable groups of the

305

compounds and hTTR is stable for the 16 anionic compounds. In addition, the oxygen/nitrogen

306

hydrogen bonds occupancy ratio is higher than that of halogen hydrogen bonds except for 5-

307

chlorosalicylic acid, elucidating the main hydrogen bonds type is oxygen/nitrogen hydrogen

308

bonds (Figure 4). Furthermore, the amino acid residues involved in forming hydrogen bonds were

309

also analyzed. As shown in Table S6, eleven amino acid residues were observed. The most

310

important amino acid residues are Lys 15, Lys 15' and Thr 119' (Figure S10).

311

The halogen bonds analyzing results indicated that only eight compounds formed halogen

312

bonds with hTTR and the halogen bonds occupancy ratio was low (Table S7). As shown in Figure

313

5, for most of the high potency hTTR disruptors, they could form stable ionic pair interactions

314

and/or hydrogen bonds.

315

In addition to forming orientational noncovalent interactions, all the compounds have

316

hydrophobic interactions with hTTR (Figure S11). The molecular modeling results indicated that

317

ionic pair, hydrogen bonds and hydrophobic interactions are the dominant interactions between

318

the model compounds and hTTR. Thus, appropriate descriptors should be selected to characterize

319

the critical interactions in the QSAR modeling.

320

Development of the QSAR model

321 322

The optimum QSAR model is: logRP = -0.744 ± 0.276 + 0.463 ± 0.0854 logD – 0.0773 ± 0.0236 dipoleadj

(4)

323

ntraining = 18, R2training = 0.828, Q2LOO = 0.729, Q2BOOT = 0.776, R2YS = 0.109, Q2YS = -0.284,

324

RMSETrain = 0.418, sTrain =0.458, MAETrain = 0.339, p < 0.0001

325

nEXT = 5, Q2EXT = 0.902, CCC = 0.936, RMSEEXT = 0.316, sEXT = 0.499, MAEEXT = 0.257

326

where ntraining and nEXT are the number of compounds in the training set and validation set, 13 ACS Paragon Plus Environment


327

respectively; R2training is the squared correlation coefficient between observed and fitted values for

328

the training set; Q2LOO and Q2BOOT are leave-one out cross validation Q2 and bootstrapping

329

coefficient, respectively; R2YS and Q2YS are Y-scrambling technique parameters; Q2EXT is the

330

externally explained variance; CCC is the concordance correlation coefficient; RMSEtraining and

331

RMSEEXT are the root mean square error for the training set and validation set, respectively; sTrain

332

and sEXT are standard errors for the training set and validation set, respectively; MAETrain and

333

MAEEXT are mean absolute error for the training set and validation set, respectively; p is the

334

significant level.

335

As shown in Table S8, the value of VIF is < 10, indicating there is no serious multi-collinearity

336

among the variables. The R2training, Q2LOO, Q2BOOT, Q2EXT, and CCC are > 0.700, implying the model

337

had a good goodness-of-fit, robustness and external prediction performances according to the

338

acceptable criteria (Q2 > 0.600, R2 > 0.700, CCC > 0.850).64 According to the Y-scrambling test

339

criteria (R2YS < 0.3, Q2YS < 0.05), this model also has no accidental correlation.75 The plot of

340

observed versus predicted logRP was shown in Figure S12.

341

The applicability domain of the developed model was characterized using the Williams plot

342

and Euclidean distance-based approach (Figure S13). As shown, all the chemicals in both the

343

training set and the validation set were in the domain, indicating the training set had great

344

representativeness.

345

logD is a descriptor for proteinophilic property.48,54 It can be used to characterize the

346

hydrophobic interactions between the chemicals and hTTR as well as describe the distribution

347

ability of ionizable compounds from water to protein. Its positive coefficient in the developed

348

model indicated that a compound with high hydrophilicity and/or distribution ability may lead to

349

a higher logRP value. dipoleadj was chemical form adjusted dipole moment of the molecule. It

350

was used to describe the polarity of a given compounds.76 Its negative coefficient in the model

351

implied that a compound with high polarity may results in a lower logRP value. The dipoleadj

352

values of 3,5-dihalo- 4-hydroxybenzoic acids, halo-salicylic acids are higher than that of 2,4,6-

353

trihalo-phenols, 2,6-dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes except for 3,514 ACS Paragon Plus Environment

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354

dichloro-4-hydroxybenzoic acid (Table S9), indicating the molecular polarity of the first two

355

groups are higher than that of the last three groups. It is therefore conceivable why the 3,5-dihalo-

356

4-hydroxybenzoic acids, halo-salicylic acids exhibited low hTTR binding potency.

357

Environmental Implications

358

The potential adverse effects caused by DBPs are international issues of common concern.77,78

359

However, little is known about whether the DBPs, especially the emerging DBPs could induce

360

potential endocrine related detrimental effects and what is the underlying mechanism of endocrine

361

disruptor action. To our knowledge, the data stated here are the first from a study of the disrupting

362

effects of new identified polar phenolic-DBPs on serum hormone transport protein. We have

363

documented that the binding affinity of some phenolic-DBPs to hTTR was similar with that of

364

T4. Thus, attention should be given to determine the possible adverse effects elicited by the action

365

of serum hormone transport protein disrupting. In addition, this result also heightened interest in

366

testing other mechanism of endocrine disruption for phenolic-DBPs, such as activating/inhibiting

367

hormone receptors, inhibiting hormone synthesis and metabolism -related enzymes, and so on.

368

Lastly, a QSAR model was also developed here. The data gap for other phenolic-DBPs on their

369

hTTR binding potency can be filled by the model.

370 371

ASSOCIATED CONTENT

372

Supporting Information

373

The Supporting Information is available free of charge on the ACS Publications website.

374

Selection of ANSA competitive fluorescence displacement assay; reagents purchase information;

375

total number of atoms for simulated systems; selected molecular descriptors; criteria for

376

classification of chemicals based on in vitro toxicity results; information of substituents and their

377

Hammett constant; Occupancy ratio of amino acid residues involved in forming hydrogen bonds;

378

halogen bonds occupancy ratio; descriptions of the modeled descriptors and corresponding t, p,

379

VIF values; values of the modeled descriptors, Observed and Predicted logRP; chemical structures



380

of model compounds; plots of ANSA binding to hTTR; fluorescence spectra of DMSO;

381

fluorescence spectra and fluorescence displacement curve of model compounds; different

382

substituent group in para-position compared with 2,4,6-tribromophenol; correlation of Hammett

383

substituent constants and observed logRP; views of model compounds in the binding site;

384

hydrogen bonds occupancy ratio of amino acid residues; hydrophobic interaction of model

385

compounds with hTTR; plot of predicted versus observed logRP values; applicability domains

386

for the developed QSAR model.

387 388

AUTHOR INFORMATION

389

Corresponding Author

390

* Corresponding Author. Tel./fax: +86 025-84315521; +86 025-84315827. E-mail address:

391

[email protected]; [email protected]

392

Notes

393

The authors declare no competing financial interest.

394 395

ACKNOWLEDGMENTS

396

The study was supported by National Natural Science Foundation of China (No. 21507038, No.

397

41671489, No. 21507061) and Environmental Monitor Scientific Foundation of Jiangsu Province

398

(No.1804).

399 400

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predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding

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650


Table 1. Information of model compounds and their IC50, Kd and logRP values NO.

Compound name

logD

IC50 (nM)

Kd (nM)

logRP

-

L-thyroxine

-

685±10

31.2±3.55

-

1

2,4,6-trichlorophenol (2,4,6-TCP)

2.14

1410±100

64.3±8.56

-0.314±0.0314

2

2,4,6-tribromophenol (2,4,6-TBP)

2.92

370±30

16.9±2.34

0.267±0.0358

3

2,4,6-triiodophenol (2,4,6-TIP)

3.81

260±10

11.9±1.41

0.421±0.0179

4

4-bromo-2,6-dichlorophenol (4-Br-2,6-DCP)

2.36

890±10

40.6±4.60

-0.114±0.00800

5

4-chloro-2,6-dibromophenol (4-Cl-2,6-DBP)

2.68

510±20

23.3±2.78

0.128±0.0182

2,4,6-trihalo-phenols

2,6-dihalo-4-nitrophenols 6

2,6-dichloro-4-nitrophenol (2,6-diCl-4-NP)

0.840

780±80

35.6±5.42

-0.0564±0.0450

7

2,6-dibromo-4-nitrophenol (2,6-diBr-4-NP)

1.17

340±40

15.5±2.53

0.304±0.0515

3,5-dihalo-4-hydroxybenzaldehydes 8

3,5-dichloro-4-hydroxybenzaldehyde (3,5-diCl-4-HBA)

0.590

8010±290

365±43.2

-1.07±0.0169

9

3,5-dibromo-4-hydroxybenzaldehyde (3,5-diBr-4-HBA)

0.930

1970±40

89.9±10.3

-0.459±0.0109

10

3-bromo-5-chloro-4-hydroxybenzaldehyde (3-Br-5-Cl-4-HBA)

0.760

2360±100

108±13.0

-0.537±0.0195

halo-salicylic acids 11

5-chlorosalicylic acid (5-Cl-SA)

-0.930

211000±25000

9622±1573

-2.49±0.0518

12

5-bromosalicylic acid (5-Br-SA)

-0.760

639000±156000

29138±7835

-2.97±0.106

13

3,5-dichlorosalicylic acid (3,5-diClSA)

-0.330

40900±4900

1865±307

-1.78±0.0524

14

3-bromo-5-chlorosalicylic acid (3-Br-5-Cl-SA)

-0.160

26400±1300

1204±148

-1.59±0.0223

15

3,5-dibromosalicylic acid (3,5-diBr-SA)

0

14300±700

652±80.1

-1.32±0.0222



Page 28 of 41

3,5-dihalo- 4-hydroxybenzoic acids 16

3,5-dichloro-4-hydroxybenzoic acid (3,5-diCl-4-HB)

-1.28

257000±29000

11719±1869

-2.57±0.0494

17

3,5-dibromo-4-hydroxybenzoic acid (3,5-diBr-4-HB)

-0.720

208000±43000

9485±2233

-2.48±0.0899

Compounds resembling the structure of 2,4,6-tribromophenol 18

2,6-dibromo-4-methylphenol (2,6-diBr-4-MP)

3.29

1060±170

48.3±9.48

-0.190±0.0699

19

3,5-dibromo-4-hydroxyacetophenone (3,5-diBr-4-HAP)

0.900

2320±120

106±13.1

-0.530±0.0233

20

3,5-dibromo-4-hydroxybenzonitrile (3,5-diBr-4-HBN)

1.19

670±20

30.6±3.56

0.00962±0.0144

21

4-amino-2,6-dibromophenol (4-A-2,6-DBP)

2.19

3170±340

145±22.5

-0.665±0.0470

22

3,5-dibromo-4-hydroxybenzotrifluoride (3,5-diBr-4-HBTF)

3.27

420±30

19.2±2.56

0.212±0.0316

23

3,5-dibromo-4-hydroxybenzoic acid methyl ester (3,5-diBr-4HBME)

1.75

2240±80

102±12.1

-0.515±0.0167


Page 29 of 41

652


FIGURE LEGENDS

653 654

Figure 1. Fluorescence displacement curves of thyroxine, 2,4,6-tribromophenol, 2,6-dibromo-4-

655

nitrophenol, 3,5-dibromo-4-hydroxybenzaldehyde, 3,5-dibromosalicylic acid and 3,5-dibromo-4-

656

hydroxybenzoic acid titrated into the solution of 50 μM ANSA and 0.5 μM hTTR.. The error bars

657

represent the standard deviation of three independent measurements.

658 659

Figure 2. Distribution of the logRP for model compounds. The compound with logRP > -1.26 is a

660

high potency hTTR binder, with -2.26 < logRP < -1.26 is a moderate potency hTTR binder, with

661

logRP < -2.26 is a low potency hTTR binder.

662 663

Figure 3. Cumulative percentage of the oxygen− nitrogen distances (dO−N) for the model compounds.

664

The cumulative percentage of dO−N for each compound was measured from the dO−N of total

665

conformations (500 frames).

666 667

Figure 4. Percentage of the formed total hydrogen bonds, oxygen/nitrogen - hydrogen bonds and

668

halogen hydrogen bonds. The percentages were the ratio of conformations formed total hydrogen bonds,

669

oxygen/nitrogen - hydrogen bonds and halogen hydrogen bonds to the total conformations (500 frames).

670 671

Figure 5. Relationship of the observed logRP and orientational noncovalent interactions. The

672

percentages were the ratio of conformations formed ionic pair interactions, total hydrogen bonds and

673

halogen bonds to the total conformations (500 frames).



674

Figure 1.

675


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676


Figure 2.

677



678

Figure 3.

679 680


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681


Figure 4.

682 683



684

Figure 5.

685 686


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687


Table of Contents

688



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