Screening of potential PFOS alternatives to decrease liver

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Ecotoxicology and Human Environmental Health

Screening of potential PFOS alternatives to decrease liver bioaccumulation: Experimental and computational approaches Huiming Cao, Zhen Zhou, Ling Wang, Guangliang Liu, Yuzhen Sun, Yawei Wang, Thanh Wang, and Yong Liang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05564 • Publication Date (Web): 08 Feb 2019 Downloaded from http://pubs.acs.org on February 8, 2019

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Environmental Science & Technology

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Screening of potential PFOS alternatives to decrease liver bioaccumulation:

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Experimental and computational approaches

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Huiming Caoa,b‡, Zhen Zhoua,c‡, Ling Wanga,b, Guangliang Liua,b, Yuzhen Suna,b, Yawei Wangb,d,

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Thanh Wange and Yong Lianga,b*

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aHubei

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Jianghan University, Wuhan 430056, China

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bInstitute

Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances ， of Environment and Health, Jianghan University, Wuhan 430056, China

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cKey

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School of Chemical and Environmental Engineering, Jianghan University, Wuhan 430056, China

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dState

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Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085,

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China

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eMTM

16

Sweden

Laboratory of Optoelectronic Chemical Materials and Devices, Ministry of Education, Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for

Research Centre, School of Science and Technology, Örebro University, Örebro 70182,

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‡ Equal contribution

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*Correspondence to:

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Yong Liang

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Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances

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Jianghan University

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Wuhan 430056, P. R. China

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Tel: +86 27 84238886

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E-mail: [email protected]

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ACS Paragon Plus Environment


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Abstract

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Perfluorooctane sulfonate (PFOS) is a persistent organic pollutant with significant

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bioaccumulation potential in liver tissues. Exposure to PFOS could cause increase of liver

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weight, induce adenomas of the liver and hepatomegaly. Alternatives of PFOS might be

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designed and synthesized that have significantly lower liver bioaccumulation. In this study, we

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conducted animal exposure experiments to investigate tissue accumulations of 14 per- and

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polyfluoroalkyl substances. Correlation analysis demonstrated that accumulation of the

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compounds in rat liver had strong correlations with their binding affinities of liver fatty acid

41

binding protein (LFABP). Thus, we combined a quantitative structure-activity relationship

42

model with molecular dynamics (MD) simulations to develop computational models to predict

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the LFABP binding affinities of two newly synthesized alternatives, perfluorodecalin-2-sulfonic

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acid and N-di-perfluorobutanoic acid. The binding characteristics of the PFOS alternatives for

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LFABP were elaborated to explore how the different structural modifications of molecules

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influenced the underlying binding mechanisms. Subsequent animal experiments demonstrated

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that the binding free energy calculations based on the MD simulations provided a good indicator

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to reflect the relative degree of liver accumulation of the PFOS alternatives in the same exposure

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doses and durations. Our findings from the combination of experimental exposure and

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computational model can provide helpful information to design potential alternatives of PFOS

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with weak LFABP binding capability and low liver accumulation.

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Keywords: PFOS alternatives; Liver accumulation; Liver fatty acid binding protein; Quantitative

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structure-activity relationship models; Molecular dynamics simulations.

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Table of Contents (TOC)

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1. INTRODUCTION

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Per- and polyfluoroalkyl substances (PFASs) mainly contain fully or partially fluorinated

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carbon chains with a variety of neutral or ionic functional groups. Especially, perfluorooctane

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sulfonate (PFOS) has been extensively used in industrial and commercial products around the

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world.1 Due to the chemical stability, high temperature resistance and high surface activity, the

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production, usage and release of PFOS have led to a widespread contamination and subsequent

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exposure to wildlife and humans, which raised global awareness concerning its bioaccumulation

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and toxicity.2-4 Thus, in 2009, PFOS and its precursors were added to the list of persistent organic

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pollutants (POPs) in the Stockholm Convention on POPs.

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Previous reports indicated that PFOS could significantly accumulate in human serum with a

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long elimination half-life.5 Moreover, toxicological assessments have demonstrated that PFOS

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exposure can increase liver weight, and induce adenomas of the liver and hepatomegaly.6,7 In

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particular, some studies indicated that PFOS can activate the hepatic peroxisome proliferator

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receptors and thus disturb cholesterol homeostasis and fatty acid ω-oxidation.8,9 When entering the

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human body, PFOS distributes mainly between the blood and liver tissues, and it is speculated that

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the liver accumulation of PFOS may result in and even exacerbate its hepatotoxicity. It is therefore

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desirable to design and synthesize safe alternatives of PFOS for reduced hepatotoxicity, and some

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alternatives have already been available. However, the structural modifications of many current

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PFOS alternatives have not decreased their bioaccumulation in liver and potential hepatotoxicity.

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For example, some PFOS analogues such as 6:2 chlorinated polyfluorinated ether sulfonate (6:2

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Cl-PFESA) and 6:2 fluorotelomer sulfonic acid (6:2 FTSA) have been detected in fire-fighting

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foams and metal plating sites.10,11 These two compounds have been detected in river water, sludge,

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wild fish and occupational workers.12,13 Recent studies reported that 6:2 Cl-PFESA could induce a

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stronger activation of peroxisome proliferator-activated receptors (PPARs, including PPARα,

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PPARβ, and PPARγ subtypes) than PFOS, while 6:2 FTSA exhibited a comparable hepatotoxicity

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in mice relative to PFOS.14,15 Environmental investigations and toxicity assessments suggested

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that the production and application of emerging PFOS alternatives might lead to new risks for

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environmental and human health.

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Accordingly, reduction of bioaccumulation in liver and hepatotoxicity potential should be

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considered as a key point in the development of potential PFOS alternatives. Because of the 3



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structural similarity between PFASs and fatty acids, bioaccumulation of PFASs in liver tissues are

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presumably ascribed to their strong binding affinities with liver fatty acid binding proteins

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(LFABP), which is a member of the intracellular lipid-binding protein superfamily governing

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metabolism of fatty acids.16,17 This binding also facilitates the transport of PFASs into

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hepatocytes, thereby initializing relevant hepatotoxic effects. Thus, the binding of LFABP plays

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an important role in the bioaccumulation and toxicological evaluation of potential PFOS

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alternatives. Elucidating the structure-based interactions between LFABP and potential PFOS

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alternatives is essential to design safe alternatives of PFOS by minimizing liver bioaccumulation

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and indirectly decreasing the hepatotoxicity. Computational methods such as quantitative

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structure-activity relationship (QSAR) models and molecular dynamics (MD) simulations can

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provide a theoretical evaluation for binding affinities and mechanisms of target compounds with

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biomacromolecules. These theoretical studies are particularly beneficial to the molecular design,

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structural modification and virtual screening of drugs and other chemicals for increasing activities

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or decreasing toxicities.18,19

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In this work, we aimed to provide a theoretical guidance for predicting the binding affinity of

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new PFOS alternatives to LFABP by developing computational models. We first conducted

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exposure experiments to evaluate the concentrations of 14 PFASs in rat, in order to identify

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possible predictors closely related to PFASs accumulation. From the analysis of tissues, we found

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that the protein-binding affinities of PFASs with LFABP have a strong correlation with their

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accumulations in liver. We then combined QSAR modeling and MD simulations to develop the

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computational models for the predictions of PFASs-LFABP binding affinities. The developed

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models showed good performance in predicting the binding affinities of PFASs with LFABP,

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which can be used as an indicator of degree of liver accumulation, thereby reflecting indirectly

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their potential hazards to human health. The current experimental and computational studies

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provided an effective and feasible strategy of molecular design to decrease the liver accumulation

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of potential PFOS alternatives.

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2. MATERIALS AND METHODS

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2.1. Chemicals.

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Native calibration standards and isotopically labeled internal standards of PFASs were 4


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obtained from Wellington Laboratories (Ontario, Canada). Perfluorobutanoic acid (PFBA, 98%),

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perfluoropentanoic acid (PFPA, 97%), perfluoroheptanoic acid (PFHpA, 96%), perfluorononanoic

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acid (PFNA, 97%), perfluorodecanoic acid (PFDA, 97%), perfluorododecanoic acid (PFDoDA,

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96%), and perfluorotetradecanoic acid (PFTeDA, 96%) were obtained from Alfa Aesar (Ward

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Hill, MA, USA). Perfluorohexanoic acid (PFHxA, ≥97%), perfluorooctanoic acid (PFOA, 96%),

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perfluoroundecanoic acid (PFUnDA, 95%), perfluorotridecanoic acid (PFTrDA, 97%),

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perfluorobutanesulfonate (PFBS, 97%), perfluorohexanesulfonate (PFHxS, ≥98%), and PFOS

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(98%) were obtained from Sigma Aldrich (St. Louis, MO, USA). Perfluorodecalin-2-sulfonic acid

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(PFDecS) and N-di-perfluorobutanoic acid (N-diPFBS) were obtained from a manufacturing

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facility in Xiaogan, Hubei province. The molecular structures of PFDecS and N-diPFBS are

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shown in Figure 1.

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2.2. Animal Exposure Experiments and Chemical Analysis of PFASs.

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The male Sprague-Dawley rats at 6-8 weeks of age used for these experiments were obtained

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from the Laboratory Animal Center of Hunan. All the animal treatments were approved by the

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ethics committee of Jianghan University. The acclimatized rats were exposed to 14 PFASs in a

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mixture dose with each concentration of 0.5 mg/kg/day (0.5% Tween 20) by daily oral gavage for

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one week. Blood and liver tissue samples (n = 4) were collected after 24 h and 7 d, respectively.

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Subsequently, exposure experiments of rats were conducted by daily oral gavage of 10 mg/kg/day

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PFOS, PFDecS or N-diPFBS for one week, respectively. Blood and liver tissue samples (n = 4)

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were collected after the exposure experiment ended. All the samples were prepared for the

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analysis of PFASs according to a procedure adapted from previous reports using ion-pair

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liquid-liquid extraction followed by SPE cleanup.20,21 Detailed sample preparation and

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pretreatment procedures are described in the Supporting Information (SI). A high performance

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liquid chromatography (Ultimate 3000 HPLC, Thermo Fisher, CA, USA) coupled to an

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electrospray ionization tandem mass spectrometer (Triple Quad 4500, Applied Biosystems/MDS

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SCIEX, USA) was used for the quantification of the above tested compounds in blood and liver

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tissue samples. Quality assurance and quality control of the tested compounds were conducted

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using the internal standard method (isotopic dilution) adapted from our previous study.20,22 Further

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information about instrumental parameters, quality assurance and quality control can be found in

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the SI (Table S1 and S2). 5



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2.3. QSAR Model Development and Evaluation.

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Experimental data (Dataset I) for the QSAR model construction were collected from a

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previous study23 which investigated the LFABP binding affinities of 19 compounds determined by

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ligand displacement assay (Table S3). Among these compounds, 14 compounds were considered

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as a binder of LFABP with measurable dissociation constant (Kd), while the other 5 compounds

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(PFBA, PFPA, PFHxA, 6:2 FTOH and 8:2 FTOH) have no measurable Kd as non-binder. The

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negative logarithm value of dissociation constant of each compound (–logKd, M) was employed

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to scale their binding potencies. In order to model in the full range of activity, both the active and

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inactive compounds were included in the development of the QSAR models. For the inactive

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compounds, the Kd value was given a value of 10340 μM, 1 log unit greater than the highest

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tested concentration of 1034 μM for PFBS24-26. Thus, the –logKd (M) of inactive compounds was

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set as 1.985. The Data set I was divided into training set (11 active compounds and 3 inactive

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compounds) and validation set (3 active compounds and 2 inactive compounds) with the ratio of

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3:1 for the development of the QSAR model. The distribution between training and validation sets

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was conducted in a way that the compounds in each of the sets contained at least one example of

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each basic function group (i.e. -COO-, -SO3-, -OH). The reason for this distribution for the Data

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set I was that both training and validation sets can contain compounds with diverse structures to

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explore the full range of different activities [from very active (palmitic acid and oleic acid)

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through moderately active (the medium- and long-chain PFASs) to inactive (short-chain PFASs

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and 6:2 FTOH and 8:2 FTOH) compounds]. Thus, oleic acid (very active), PFOA and PFHxS

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(moderately active), PFPA and 8:2 FTOH (inactive) were extracted from the Data set I as the

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validation set.

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The training set was employed to develop QSAR models, and the validation set was used to

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evaluate the predictive ability of the model. Furthermore, a set of six other PFASs (Data set II)

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from another study.24 was also included to evaluate the constructed QSAR model (Table S3).

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Finally, calculations of the two newly-synthesized PFOS alternatives (Data set III) were made

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using the developed QSAR model to predict their binding affinities to LFABP (Table S3).

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1D, 2D and 3D descriptors were calculated by ChemDes software, which provides 3679

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molecular descriptors from six open source packages (Chemopy, CDK, RDKit, Pybel, BileDesc

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and PaDEL).25 Moreover, three quantum chemical descriptors including the highest occupied 6


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molecular orbital energy (EHOMO), the lowest unoccupied molecular orbital energy (ELUMO), and

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the most positive net atomic charge on an oxygen atom (qO-) were selected to characterize the

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electrostatic interactions between ligand and protein, which has been proven useful in some QSAR

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studies for the toxicity prediction of PFASs.26,27 These descriptors were obtained from the

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optimized structures of molecules at the B3LYP/6-311++G(d,p) level followed by the frequency

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analysis by Gaussian 16 program package. During the geometry optimizations, polarized

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continuum model (PCM) was also considered for solvent effect of water. The 3D molecular

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structures of tested compounds are shown in Figure S1.

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Stepwise multiple linear regression (MLR) analysis was used to construct the QSAR models

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by the SPSS 13.0 software. The applicability domain (AD) of the developed QSAR models was

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assessed by the Euclidean distance-based method that was incorporated in the AmbitDiscovery

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software package. The Williams plot is the plot of standardized residuals (δ) versus leverage (hat

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diagonal) values (h).28

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2.4. MD Simulations and Binding Free Energy Calculations.

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The initial binding poses of PFOS and its potential alternatives (6:2 Cl-PFESA, 6:2 FTSA,

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N-diPFBS and PFDecS) were predicted by molecular docking calculations using the Ledock

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program which has been demonstrated to be highly accurate in the pose-prediction of ligands

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compared with the native binding poses of complexes in a comprehensive evaluation report.29 In

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the current work, the crystal structure of LFABP in complex with one molecule, palmitic acid

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(PLA), was used as the receptor template (PDB ID: 3STM).30 The binding pocket was generated

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on the native ligand PLA by a box extending in x, y, and z directions, with a radius of 5 Å defined

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by an in-house plug-in of PyMOL software. For each chemical, 100 docking runs were carried out

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for the conformational searching of ligand, and their binding poses with the best docking scores

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were kept for further evaluation of stability in the explicit solvent MD simulations.

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The AMBER14SB force field (ff14SB)31 and general AMBER force field (GAFF)32 were

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used to establish the topology parameters of LFABP and the tested chemicals, respectively. The

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RESP partial charges of chemicals were generated at the HF/6-31G* level using the Antechamber

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module of AmberTools17.33 The protonation forms of ionizable residues abided by the definitions

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of H++3.0 program at pH=7 condition.34 The constructed ligand-LFABP complexes were solvated

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in a cubic periodic box of the TIP3P water model with a 10 Å distance around the solute. The 7



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complex systems were neutralized by adding Na+ ions. Prior to the production simulation,

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equilibrium course of MD simulation including the minimization (5000 steps), heating (500 ps)

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and density equilibrium (500 ps) was implemented for all the complex systems. Subsequently, 100

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ns MD production simulation was performed under the constant pressure and temperature (1 atm

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and 300 K) with three replicates of each complex. The Nvidia Tesla K40 card was used to perform

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the above production simulations using the GPU-accelerated PMEMD module (pmemd.cuda) of

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AMBER16 software package.35

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The binding free energies of the ligand-LFABP complexes were calculated based on the last

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50 ns trajectories of each replicate by using the Molecular Mechanics/Generalized Born Surface

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Area (MM/GBSA) algorithm.36 The enthalpy (ΔH) and entropic (TΔS) contributions were

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assessed by the MMPBSA.py and Nmode module of AmberTools17, respectively.37 The total

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binding free energy (ΔGcal) can be calculated using the following equations: ΔGcal = ΔH – TΔS

(1)

ΔH = ΔEvdW + ΔEele +ΔGSA + ΔGGB

(2)

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ΔH includes ΔEvdW (van der Waals), ΔEele (electrostatic), ΔGSA (polar contribution of solvation

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free energy) and ΔGGB (nonpolar contribution of solvation free energy). Due to the high

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computational cost, only 150 snapshots of complex were taken for the calculations of entropic

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contributions. Details about molecular docking, MD simulation and binding free calculation have

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been summarized in our previous studies.38-40

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3. RESULTS AND DISCUSSION

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3.1. Correlations between the PFASs levels in liver and their binding affinities with LFABP.

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The 14 tested PFASs in rat blood and liver were quantified to investigate their distribution

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trends in the two major tissues in regards to the bioaccumulations. After 24 h or 7 d exposure to a

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mix of the 14 PFASs, it was found that the levels of short-chain PFASs such as PFBA, PFPA,

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PFHxA, PFHpA and PFBS, were relatively low in both blood and liver tissues (Figure 2A and B),

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suggesting that these compounds could be excreted rapidly in urine due to the shorter half-lives

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and higher water solubility compared to PFASs with longer carbon chains.41-43 Conversely, higher

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levels of PFNA, PFDA, PFUnDA, PFDoDA, PFTrDA, PFTeDA and PFOS in liver than in blood 8


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were detected, suggesting that most medium- and long-chain PFCA and PFSA are more inclined

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to be accumulated in the liver tissue (Figure 2A and B). This suggested that distribution of PFASs

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in the two major tissues is dependent on the molecular size of the compounds. In this study,

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PFASs exhibited selective accumulation in rat liver when the backbone chain contained more than

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eight carbons. Hence, PFOA and PFHxS would be more prevalently distributed in blood due to

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relative shorter backbone chain length (≤ 8). Further, correlation analysis showed strong linear

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correlations between the –log dissociation constants (–logKd) of PFASs for LFABP and the

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measured liver concentrations after 24 h and 7 d (Figure 2C and D). This suggests that binding to

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LFABP is a major contributing factor to the bioaccumulation potential of PFASs in liver tissues,

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although they are also known to bind to phospholipid membranes and other intracellular proteins

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in liver tissues.44 In addition, it is speculated that the binding of PFASs with LFABP could affect

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the uptake and transport behaviors of fatty acids, and thus could contribute to hepatomegaly.45

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3.2. Development of the QSAR models.

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Based on the above concentration trends of PFASs and correlation analysis, the binding

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affinities of PFASs with LFABP were selected as an indicator to reflect their bioaccumulation and

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toxicity potential in liver tissues. The QSAR model was used to predict the –logKd values of the

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selected compounds and thereby evaluate their binding abilities of LFABP. The experimental

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data23 involving the binding abilities of PLA, oleic acid (OA) and seventeen PFASs for human

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LFABP were extracted and divided into a training set and a validation set. The optimum QSAR

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model parameters were found to be:

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Model A:

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―LogKd = ―44.757 + 21.489 × GATS1i ― 1.756 × SpMin8_Bhe + 12.035 × GATS1c

262 263

N

training

N

= 14, R2

EXT

training

= 5, R2

EXT

= 0.963, Q2

LOO

= 0.809, Q2

EXT

= 0.937, RMSEtraining = 0.237, p < 10-4,

= 0.724, RMSEEXT = 0.696, p < 10-4

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where Ntraining and NEXT represents the number of compounds in the training and validation sets

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respectively, p is the significance level. The parameters including the coefficient of determination

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(R2training) and leave-one out cross validation (Q2LOO) were employed to evaluate the

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goodness-of-fit and robustness of the QSAR model, respectively. The R2training and Q2LOO values

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for Model A were 0.963 and 0.937, which implied that this model had goodness-of-fit and

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robustness. The descriptive statistics for validation set were R2EXT = 0.809 and Q2EXT = 0.724,

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indicating that this model had good predictive ability. GATS1i was the most important parameter

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of positive coefficient for the model, which means that –logKd increases with the increase of 9



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GATS1i. The correlation coefficient between the descriptor and –logKd was 0.86, explaining

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approximately 74.5% variations of –logKd values. This parameter was calculated by the

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summations of Geary autocorrelation of two atoms located at the topological distance of one

275

weighted by the first ionization potential. For the ionizable PFASs, it was considered to describe

276

the whole electrostatic features of the molecules. SpMax8_Bhe was the second most important

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predictor variable of negative coefficient. It is an indication of the largest absolute eigenvalue of

278

Burden modified matrix - n 8 weighted by relative Sanderson electronegativities. GATS1c

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encodes a Geary autocorrelation for the topological distance of one between pairs of atom

280

weighted by charges, and more positive values of this parameter will lead to a higher –logKd

281

value. The detailed definitions of descriptors have been summarized in a previous report.46

282

Furthermore, we assessed the applicability domain of Model A. All the tested compounds

283

were located within the domain by the analysis of Euclidean distance-based approach (Figure

284

S2A), and standardized residuals of these compounds in Model A were less than 2.5 (Figure S2B),

285

suggesting that the training set has a great representativeness for the linear PFASs. Thus, we used

286

Model A to predict the –logKd values of other PFASs (the external Data set II). The predicted –

287

logKd values showed a strong correlation with the experimental values in this data set (Figure

288

S2C). Notably, the results indicated that the –logKd value of 6:2 Cl-PFESA was higher than that

289

of PFOS while 6:2 FTSA was lower (Table S3). This was in line with the report of Sheng et al.24

290

which also indicated that the PFOS alternative 6:2 Cl-PFESA had higher binding affinities to

291

LFABP than PFOS. Moreover, for the two newly synthesized PFOS alternatives, the –logKd value

292

for N-diPFBS was slightly greater than that of PFOS, whereas a negative value was observed for

293

PFDecS (Table S3). Since the ring-shaped structure of PFDecS is significantly different from the

294

linear PFASs, we considered that the domain of the current QSAR models does not cover

295

ring-containing PFASs (Figure S2D). Only limited data for the fluorotelomer compound (6:2

296

FTOH only) was included in the training set, which could not fully describe their structural

297

characteristic. Thus, the binding free energy calculations based on the MD simulations were

298

carried out to further assess the binding affinities of LFABP for the two known PFOS alternatives

299

(6:2 Cl-PFESA and 6:2 FTSA) and the two newly synthesized PFOS alternatives (PFDecS and

300

N-diPFBS).

301

3.3. Binding free energy calculations by MM/GBSA method.

302

In the crystal structure of PLA-LFABP complex (pH 8.0), the C=O bond lengths of

303

carboxylic acid group of PLA were 1.262 and 1.258 Å, respectively, suggesting that most PLA 10


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molecules bind to LFABP in the anionic forms. We also optimized the PLA structures of different

305

form at the B3LYP/6-311++G(d,p) level with the solvent model of PCM, and obtained the similar

306

results for molecular structure of PLA (Figure S3). Likewise, the PFASs used in our study can

307

dissociate completely in physiological pH condition since their pKa values have been shown to be

308

mostly below 0.3.47 Thus, we adopted the anionic forms of the tested compounds to explore their

309

binding modes and affinities with LFABP. In the docking study, molecular superimposition of top

310

scored ligand binding pose with the native ligand binding pose displayed a small

311

root-mean-square deviation (RMSD) of 0.67 Å, indicating that the current docking method was

312

able to reproduce the native binding pose for the PLA-LFABP complex (Figure S4).

313

Subsequently, the initial binding poses of PFOS, 6:2 Cl-PFESA, 6:2 FTSA, PFDecS and

314

N-diPFBS were obtained by the docking calculations. Their binding stabilities were further

315

assessed during the 100 ns MD simulations. The RMSD changes of Cα atoms of protein and tested

316

compounds showed that all simulation systems reached equilibrium after 40 ns simulation time

317

(Figure S5). Among these complex systems, the binding of 6:2 FTSA induced larger fluctuations

318

of protein RMSD values, while PFDecS showed the most stable binding behavior with the lowest

319

RMSD values of ligand in the complex system (Figure S5). Furthermore, we evaluated the binding

320

free energies of the complexes by the MM/GBSA method. The results showed that PFDecS had

321

the most negative value for binding free energy, followed by 6:2 Cl-PFESA, PFOS, 6:2 FTSA and

322

N-diPFBS (Figure 3A), resulting in the rank order of binding affinities: PFDecS > 6:2

323

Cl-PFESA > PFOS > 6:2 FTSA > N-diPFBS.

324

According to previous binding free energy calculations48 using the Molecular Mechanics/

325

Poisson Born Surface Area (MM/PBSA) algorithm based on the 24 ns MD simulation, 6:2

326

Cl-PFESA also showed a more favorable binding affinity of LFABP than PFOS, which is in

327

accord with the current calculated evaluations. It is noteworthy that a human LFABP structure

328

(3STM) was used in this study. We also performed additional MD simulation for the above five

329

compounds using the rat LFABP structure (1LFO) to assess the robustness of the MM/GBSA

330

method. The initial complex structure for each compound was taken from the best scored poses of

331

docking calculations. The rank order of binding affinities was 6:2 Cl-PFESA > PFDecS > PFOS >

332

6:2 FTSA > N-diPFBS, suggesting that the species is important for LFABP binding of PFASs

333

(Table S4). To further evaluate the effectiveness of the MM/GBSA calculations, the binding free 11



334

energies of the remaining compounds in the Data set I and II (22 compounds) were using the same

335

workflow of combining molecular docking and MD simulation (Table S5). By analyzing the

336

correlation coefficients between the different energy components and the experimental –logKd

337

values (Table S6), we found that the enthalpy contributions (ΔH) exhibited good correlations with

338

the experimental –logKd values (r = -0.94 and -0.82 for Data set I and II, respectively). Moreover,

339

a subset of Data set I, Data set I′, was constructed for comparing the results of previous

340

MM/PBSA calculations48, which only contained the six compounds (PFHpA, PFOA, PFNA,

341

PFBS, PFHxS and PFOS) for correlation analysis. The results showed a coefficient of -0.95 for

342

the enthalpy contributions (ΔH), which was comparable to a coefficient of -0.96 for the polar

343

contributions (ΔEpolar = ΔEele + ΔGGB) in the previous MM/PBSA calculations. In contrast, the

344

entropic contributions (TΔS) showed strong correlations with the different data sets (r = -0.83 ~

345

-0.97) in our study when compared to the prediction of previous MM/PBSA method. The obtained

346

entropic contributions from long MD simulations (such as 100 ns in the current work) could more

347

accurately reflect the unfavorable intramolecular torsional energy and intermolecular collision

348

between tested PFASs and surrounding residues in the binding pocket. Moreover, we compared

349

the performances of MM/GBSA and MM/PBSA calculations based on the current MD simulations

350

(Table S7). The results indicated that relative to the MM/PBSA method, the MM/GBSA method

351

showed slight improvement of correlation coefficients for ΔH in the Data set I and Data set I′

352

(Table S8), implying that the MM/GBSA method may be more suitable to evaluate the relative

353

binding affinity for the current complex system.

354

To further understand the ligand recognitions from an energy perspective, we analyzed the

355

contribution of energy terms for each compound (Table S9). Among all the contributors, the

356

electrostatic interactions (ΔEele) of the gas phase were identified as the dominant forces to stabilize

357

the binding of PFDecS, 6:2 Cl-PFESA, PFOS and 6:2 FTSA in the active pocket of LFABP

358

(-83.40 to -103.06 kcal/mol). The results correspond with the related electrostatic descriptors in

359

the developed QSAR models. Conversely, regarding to N-diPFBS, the van der Waals interactions

360

(ΔEvdW) of the gas phase was the main contributor to the total binding free energy (-44.62

361

kcal/mol), while the contribution of ΔEele significantly decreased to -7.72 kcal/mol. This may be

362

because the N-sulfonic amides group of the compound is located at the center of the chemical

363

structure, thereby reducing the favorable contacts with the polar amino acids located at the 12


Page 12 of 22

Page 13 of 22


364

entrance of binding pocket. As for the polar contributions of solvation, the values from all the

365

simulation systems showed greater variation amplitude from 29.94 to 102.62 kcal/mol, while the

366

changes of nonpolar contributions of solvation were low from -5.71 to -7.56 kcal/mol. Thus, the

367

changes of binding abilities of ligands were affected mainly by the polar contributions of solvation

368

free energies.

369

By further energy decompositions, a residue with energy contribution < -1.0 kcal/mol is

370

considered as a key residue for ligand binding. The representative binding modes based on

371

clustering analysis were used to dissect the protein-ligand interactions (Figure 3B-F). It should be

372

noted that for 6:2 Cl-PFESA, the energy contributions of Arg122 and Ser124 were more favorable

373

than that for PFOS, which implied that the two residues can form a stable hydrogen bonding

374

interaction with the acid groups of the ligands (Figure 3B and C). This means that the introducing

375

of an oxygen atom to the backbone structure not only increased the chain length, but also

376

enhanced the flexibility of sulfonate group that can accommodate the polar residues buried in the

377

ligand-binding pocket for the formations of stable hydrogen bonding. Moreover, chlorine

378

substitution in the fluorine-containing chains of 6:2 Cl-PFESA also provided more hydrophobic

379

interactions with the residues Ile41 and Ile52 compared to PFOS (Figure 3C). Similarly, the

380

bicyclic ring of fluorine-containing in PFDecS provided more hydrophobic interactions with the

381

residue Ile41 and Ile109 by increasing the number of –CF2 group and structural rigid, while the

382

sulfonate group of PFDecS has stronger hydrogen bonding interactions with Arg122 and Ser124

383

relative to PFOS (Figure 3E). Conversely, these contributions of hydrogen bonding and

384

hydrophobic interactions were decreased in the 6:2 FTSA and N-diPFBS complex systems (Figure

385

3D). In particular, N-diPFBS forms new interactions with Asn111 and Thr102 instead of Ser39,

386

Ile41, Ile109, Ser124 and Arg122 (Figure 3F). The above energy comparisons implied that the

387

stable recognition of different functional head groups (i.e., carboxyl and sulfonate groups) was an

388

essential factor to distinguish the strong binders from weaker ones under similar molecular size.

389

3.4. Implications for design of potential PFOS alternatives

390

On the basis of binding free energy calculations, we speculated that the synthesized PFOS

391

alternative PFDecS has a stronger binding affinity with LFABP, and thus leads to higher

392

bioaccumulation potential in liver tissues relative to PFOS. In the case of N-diPFBS, the binding

393

free energy predictions suggest that the compound can bind to LFABP but with a relatively lower 13



394

affinity than PFOS. To validate the theoretical hypothesis, we measured the concentrations of

395

blood and liver in rats that were separately exposed to PFOS, PFDecS and N-diPFBS. The results

396

from individual exposures indicated that the levels of these compounds were higher in liver than

397

blood tissue samples, implying that they are prone to accumulate in the liver tissue (Figure 4). The

398

rank order of accumulation potential was PFDecS > PFOS > N-diPFBS, signifying that the

399

performance of the MM/GBSA method was consistent with the experimental evaluations for liver

400

bioaccumulation. It should be noted that the sulfonic amide group of N-diPFBS has neutral or

401

anionic form at the different dissociation states. Thus, we also calculated the binding free energies

402

of neutral and anionic N-diPFBS. The results indicated that both forms of N-diPFBS have relative

403

lower binding affinities of LFABP than PFOS based on the human or rat crystal structure (Table

404

S4 and S9).

405

Compared with the MM/PBSA algorithm, the MM/GBSA algorithm was more accurate and

406

effective for refining the binding poses and assessing the binding free energies, although

407

MM/PBSA is theoretically more rigorous.36 We expect that the current MM/GBSA calculations

408

based on the trajectories of GPU-accelerated MD simulations can be used to screen the relative

409

binding affinities of theoretically designed PFOS alternatives. Therefore, in order to improve the

410

performance of the constructed QSAR (Model A), various energy components of MM/GBSA

411

calculations were proposed as the descriptors to integrate into the development of QSAR models.

412

The new QSAR model was further developed as Model B:

413 414 415

―LogKd = ―0.653 ― 0.147 × Δ𝐻 ― 1.078 × 𝑆𝑝𝑀 𝑖𝑛8_𝐵ℎ𝑣 N

training

N

= 14, R2

EXT

training

= 5, R2

EXT

= 0.908, Q2

LOO

= 0.979, Q2

EXT

= 0.807, RMSEtraining = 0.381, p < 10-4,

= 0.97, RMSEEXT = 0.228, p < 10-4

416

which had the comparable performance with Model A by the statistical criteria (Figure 5A). ΔH

417

was the most important parameter of negative coefficient for this model, which means that the

418

enthalpy contribution was most suitable for the QSAR model construction in the current system.

419

SpMin8_Bhv was the second most important predictor variable of negative coefficient. It is an

420

indication of the smallest absolute eigenvalue of Burden modified matrix - n 8 weighted by

421

relative van der Waals volumes. Encouragingly, no negative values were found in the predicted –

422

logKd values and all the tested compounds were located within the applicability domain of Model

423

B (Figure 5B), suggesting that the introduction of the energy parameter into the QSAR model 14


Page 14 of 22

Page 15 of 22


424

could provide a more reliable description of the PFASs-LFABP interactions. This could thereby

425

improve the performance of QSAR model for predicting the absolute binding affinities of PFASs

426

at a wider range of application domain. The predicted –logKd values of neutral N-diPFBS, anionic

427

N-diPFBS, PFDecS and PFOS were 3.193, 3.588, 5.053 and 4.346 respectively, which was in line

428

with the relative order of liver accumulation for the compounds (Table S10).

429

Taken together, these experimental and computational results indicated that the potential

430

PFOS alternatives may not be suitable alternatives in terms of bioaccumulations in liver tissues.

431

Although the molecular structure of PFDecS is very different from the linear chain of PFOS and

432

fatty acid, its binding affinity to LFABP did not significantly decrease. To further understand the

433

interactions between ligands and LFABP, we analyzed the binding experiments from a previous

434

study

435

(1-anilino-8-naphthalenesulfonate) as a fluorometric probe was used to evaluate the dissociation

436

constants of PFASs.23,24 The molecular structures of PFDecS and 1,8-ANS are different from the

437

linear structure of other tested PFASs in this work, which implied that the nature of LFABP is

438

versatile to respond to the diverse chemical features of different molecules in the PLA-bound

439

pocket (Figure S6). Compared with the flexible and linear structure of PFOS, the rigid and

440

ring-shaped structures of PFDecS and 1,8-ANS increased their binding affinities of LFABP by

441

more stable hydrophobic interactions. On the other hand, the N-diPFBS results suggested that the

442

substitution of sulfonate group by other polar groups into the middle of molecular structure could

443

significantly reduce the favorable electrostatic interactions, and thus directly decrease its binding

444

affinity to LFABP and thereby also lower the liver accumulation. This structural modification is

445

different from that in 6:2 Cl-PFESA, which preserved the sulfonate group with addition of an

446

ether group, thereby indirectly increasing the hydrogen bonding interactions between the sulfonate

447

group and LFABP. With implications from these experimental and computational results, future

448

works could be directed to design potential PFOS alternatives with diverse polar groups away

449

from the sulfonate group for weak LFABP binding and lower the liver accumulation potential.

450

Supporting Information

which

used

fluorescence

displacement

assay,

and

found

that

1,8-ANS

451

Details on the analytical methods and descriptions, and further experimental and

452

computational data such retention times, molecular structures, docking poses, dynamic RMSD

453

changes, binding free energies, QSAR models for tested PFASs (Figure S1-S6 and Table S1-S8). 15



454 455 456

Conflict of Interest The authors declare that there are no conflicts of interest. Acknowledgements

457

This work was supported by grants from the Strategic Priority Research Program of the

458

Chinese Academy of Sciences (XDB14030501), the National Nature Science Foundation of China

459

(21477049, 21507044, 21777061, 21806058).

460

References

461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493

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608 609 610

Figure Legends

611 612

Figure 1 Molecular structures of two newly synthesized alternatives of PFOS: PFDecS (top) and N-diPFBS (bottom).

613 614 615

Figure 2 Blood and liver concentrations of 14 PFASs after 24 h (A) and 7 d (B) exposure. Linear correlation between the experimental –logKd values of PFASs reported by Zhang et al.23 and their liver concentrations after 24 h (C) and 7 d (D) exposure.

616 617 618 619 620 621 622

Figure 3 Binding free energies of PFOS and its alternatives by the MM/GBSA method (A). The significant differences between tested compounds were determined using a one-way analysis of variance (ANOVA) and Tukey’s multiple range tests. A p-value < 0.05 was considered statistically significant in the current binding free energy calculations. Representative binding modes of tested compounds in the binding pocket of LFABP (B-F). Key residues for the interactions between compounds and LFABP were shown in stick and energy contributions (kcal/mol) were labeled below the numbers of residues.

623

Figure 4 Blood and liver concentrations of PFOS, PFDecS and N-diPFBS after 7 d exposure. 19



624 625 626

Figure 5 Plots of the predicted versus experimental –logKd values for the training and validation sets in Mode B (A). Applicability domains for the developed QSAR model described by the Euclidean distance-based approach for Model B (B).

627 628

629 630 631 632

Figure 1 Molecular structures of two newly synthesized alternatives of PFOS: PFDecS (top) and N-diPFBS (bottom).

633 634

635 636 637 638

Figure 2 Blood and liver concentrations of 14 PFASs after 24 h (A) and 7 d (B) exposure. Linear correlation between the experimental –logKd values of PFASs reported by Zhang et al.23 and their liver concentrations after 24 h (C) and 7 d (D) exposure. 20


Page 20 of 22

Page 21 of 22


639

640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668

Figure 3 Binding free energies of PFOS and its alternatives by the MM/GBSA method (A). The significant differences between tested compounds were determined using a one-way analysis of variance (ANOVA) and Tukey’s multiple range tests. A p-value < 0.05 was considered statistically significant in the current binding free energy calculations. Representative binding modes of tested compounds in the binding pocket of LFABP (B-F). Key residues for the interactions between compounds and LFABP were shown in stick and energy contributions (kcal/mol) were labeled below the numbers of residues.

21



669

670 671 672

Figure 4 Blood and liver concentrations of PFOS, PFDecS, and N-diPFBS after 7 d exposure.

673 674 675 676

Figure 5 Plots of the predicted versus experimental –logKd values for the training and validation sets in Model B (A). Applicability domains for the developed QSAR model described by the Euclidean distance-based approach for Model B (B).

22


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Screening of potential PFOS alternatives to decrease liver

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