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Modeling of the Passive Permeation of Mercury and Methylmercury Complexes Through a Bacterial Cytoplasmic Membrane Jing Zhou, Micholas Dean Smith, Sarah J Cooper, Xiaolin Cheng, Jeremy C. Smith, and Jerry M. Parks Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02204 • Publication Date (Web): 14 Aug 2017 Downloaded from http://pubs.acs.org on August 17, 2017

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Environmental Science & Technology

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Modeling of the Passive Permeation of Mercury and Methylmercury

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Complexes Through a Bacterial Cytoplasmic Membrane

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Jing Zhou,1,2 Micholas Dean Smith,2,3 Sarah J. Cooper,1,2 Xiaolin Cheng,2,3 Jeremy C.

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Smith,2,3 and Jerry M. Parks1,2 *

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Knoxville, Tennessee, 37996, USA

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2

9

Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee, 37831-6309, USA

Graduate School of Genome Science and Technology, University of Tennessee,

UT/ORNL Center for Molecular Biophysics, Biosciences Division, Oak Ridge National

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3

11

Tennessee, Knoxville, Tennessee, 37996, USA

Department of Biochemistry and Cellular and Molecular Biology, University of

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ACS Paragon Plus Environment


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Abstract

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Cellular uptake and export are important steps in the biotransformation of mercury (Hg)

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by microorganisms. However, the mechanisms of transport across biological membranes

15

remain unclear. Membrane-bound transporters are known to be relevant, but passive

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permeation may also be involved. Inorganic HgII and methylmercury ([CH3HgII]+) are

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commonly complexed with thiolate ligands. Here, we have performed extensive

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molecular dynamics simulations of the passive permeation of HgII and [CH3Hg]+

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complexes with thiolate ligands through a model bacterial cytoplasmic membrane. We

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find that the differences in free energy between the individual complexes in bulk water

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and at their most favorable position within the membrane are ~2 kcal mol-1. We provide a

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detailed description of the molecular interactions that drive the membrane crossing

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process. Favorable interactions with carbonyl and tail groups of phospholipids stabilize

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Hg-containing solutes in the tail-head interface region of the membrane. The calculated

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permeability coefficients for the neutral compounds CH3S–HgII–SCH3 and CH3Hg–SCH3

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are on the order of 10-5 cm s-1. We conclude that small, non-ionized Hg-containing

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species can permeate readily through cytoplasmic membranes.

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1. Introduction

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Mercury is a global pollutant that exists naturally in the environment in various forms.

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All forms of Hg are toxic, but the degree of toxicity varies depending on the particular

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form.1-4 Elemental mercury (Hg0) is the least toxic because it is poorly absorbed through

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ingestion (less than 0.01%) and is less reactive compared to other forms of Hg.5 Inorganic,

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divalent mercury (HgII) is more toxic than Hg0 and it has an exceptionally strong affinity

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for thiols ¾ the stability constant log b for HgII(SR)2 compounds is 35 – 40.6, 7 This

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strong affinity for thiols leads to the binding of Hg to Cys residues, disrupting protein

36

functions. The bioavailability of HgII can also be affected by complexation with

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thiolates.8

38 39

Organic mercury is particularly toxic, and of particular relevance in this context is

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methylmercury (MeHg). MeHg ([CH3Hg]+) has one free valence, and commonly binds a

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single thiolate to form CH3HgSR complexes. The stability constant for CH3HgSR

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complexes (log b) is 16.6, which suggests a strong affinity for thiols.9 MeHg can cross

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the blood-brain barrier and is a potent neurotoxin that primarily affects the central and

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peripheral nervous systems.10 MeHg bioaccumulates and biomagnifies in the food web;

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the concentration in fish can reach 107 times higher than in stream water.11, 12 Thus,

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MeHg affects human health primarily through the ingestion of fish and shellfish.11

47 48

Both the uptake and export of biotransformed Hg species involve crossing cell

49

membranes. Various mechanisms have been proposed for HgII uptake in

50

microorganisms.13, 14 For those microorganisms encoding the mer operon, which

3



51

detoxifies the cells from Hg, the uptake of HgII into the cytoplasm may be mediated by

52

mer transporters, such as MerC, MerT, and others.13, 14 For organisms lacking the mer

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system, Hg may be taken up through passive permeation,15-17 facilitated permeation or

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active transport.18-20

55 56

Experimental studies of passive permeation mechanisms are very limited and have

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mainly focused on [HgCln]2-n (n = 1–4) complexes, which are not among the most

58

abundant biologically available forms for microorganisms.15-17 Recently, it has been

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suggested that heavy metal transporters may be involved in Hg uptake in Hg-methylating

60

bacteria.19, 21, 22 Uptake of CH3Hg–Cys by microorganisms and humans has been

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suggested to be the result of this complex being “mistaken” for the amino acid

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methionine, due to perceived structural23 and chemical component similarity.24 Using

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XANES spectroscopy, it was shown that intracellular HgII in D. desulfuricans ND132

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cells is predominantly present in linear, bis thiolate complexes (i.e., RS–HgII–SR).25

65 66

Few measurements of membrane permeabilities of Hg species have been reported, and

67

these have been limited to inorganic HgII compounds such as HgCl2. Experimental

68

approaches that have been used to measure the permeability of Hg2+ or Hg-containing

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compounds through model membranes have been summarized in a recent review.26

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Widely varying values for Hg compounds have been found. For example, the permeation

71

of HgCl2 through an egg-PC and cholesterol (1:1) membrane was studied by measuring

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radioactive 203Hg isotope flux, leading to a value of the permeability coefficient of 1.3 ×

73

10-2 cm s-1.27, 28 Using a similar method, the effect of lipid composition, pH and chloride

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concentration on the permeability of Hg(II) compounds across egg-PC and egg-

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PC/cholesterol membranes was studied, and, for example, at pH 5.0 the measured

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permeability coefficient of HgCl2 was much lower, at 1.5 × 10-4 cm s-1.29 The

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permeability coefficient of free Hg2+ ions (i.e., without ligands) through a

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diphytanoylphosphatidylcholine (DPhyPC) membrane was also measured with a 203Hg

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isotope method and was found to be very low, i.e. ~3.8 × 10-11 cm s-1.30

80 81

Experimental work on Hg permeability suffers from differences in measurements

82

resulting from differing membrane compositions, the Hg compounds studied, and the

83

experimental conditions.26 Hence, a consistent, systematic study is merited. In this regard,

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molecular dynamics (MD) simulation is a powerful technique to study the permeation of

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solutes through lipid membranes, as it can provide detailed insight into the effects and

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origins of solute properties and lipid composition on permeability.31-33 Several MD

87

studies have been carried out on the passive permeation and non-facilitated membrane

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permeation of amino acids.31, 34-37 In the aqueous phase, amino acids exist predominantly

89

in their zwitterionic forms. However, inside the hydrophobic core of a lipid bilayer,

90

protons may be transferred from the N-terminus to the C-terminus. Thus, in the above

91

computational studies the N- and C- termini of the amino acids were omitted and only

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side chain analogues were used to avoid complications associated with possible changes

93

in protonation state of the termini upon partitioning into hydrophobic membranes. Side

94

chains may gain or lose protons during permeation processes as well. Although the

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effects of protonation state change of the termini on permeability are generally not

96

considered, relative permeability differences between amino acids and related systems

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through membranes can still be ascertained as the effects of the termini are expected to

98

cancel to a large extent for analogous systems.

99 100

Several MD simulation studies have been carried out for Hg0, Hg2+, and other charged

101

and neutral Hg-containing molecules. 38-46 Although substantial effort has been made to

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understand the solution-phase dynamics of Hg species and the partitioning of amino acids

103

into heterogeneous lipid membranes, to our knowledge the changes in membrane

104

permeability of thiols upon complexation by HgII or MeHgII have not been investigated.

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Thus, in this work we perform MD simulations to calculate the changes in free energy for

106

transferring low molecular weight thiols with and without Hg or MeHg from aqueous

107

solution into a membrane bilayer. Specifically, we investigate how the complexation of

108

HgII or [CH3Hg]+ alters the permeability characteristics of CH3SH and CH3S–SCH3,

109

which are side chain analogues of the amino acids Cys and Met, respectively. First, we

110

develop CHARMM molecular mechanics force field parameters for the selected solutes.

111

We then compute free energy profiles for the passive permeation of the solutes across a

112

lipid bilayer that mimics the composition of gram-negative bacterial inner membranes

113

(i.e., 75% POPE (1-hexadecanoyl-2-(9Z-octadecenoyl)-sn-glycero-3-

114

phosphoethanolamine) and 25% POPG (1-hexadecanoyl-2-(9Z-octadecenoyl)-sn-

115

glycero-3-phospho-(1'-rac-glycerol)). Next, we identify the energetic factors that

116

contribute to the observed differences between the thiols and their Hg-containing

117

counterparts and compute the permeability coefficients for those solutes. We also

118

examine whether a simplified implicit membrane model reproduces the energetic trends

119

determined in the all-atom free energy simulations.

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Scheme 1. POPE, POPG and five solutes.

121 POPE$

O

O

P

Chain$A$

O

O O

Chain$B$

H

O

Chain$A$

OH

O

O

POPG$

OH O

O

P O

O

Chain$B$

O

H

O

NH 3

O

O

CH3SH$

CH3CH2SCH3$

CH3S–SCH3$

CH3Hg–SCH3$

CH3S–HgII–SCH3$

122 123 124

2. Methods

125

Most Hg-methylating bacteria are gram-negative.13, 47 Gram-negative bacteria are

126

composed of two cell envelopes, the outer and inner membranes, and a thin

127

peptidoglycan cell wall between the two membranes. The outer membrane is asymmetric

128

and the main components of the outer leaflet are lipopolysaccharides (LPS), whereas the

129

inner leaflet comprises phospholipids. In contrast, the inner membrane is symmetric and

130

is mainly composed of phospholipids in both leaflets. 48

131 132

To our knowledge, the inner membrane compositions of Hg-methylating bacteria have

133

not been thoroughly characterized. However, the inner membrane composition of

134

Desulfovibrio gigas, which does not methylate Hg, was found to be 30%

135

phosphatidylethanolamine (PE) and 70% phosphatidylglycerol (PG).49 For comparison,

136

the inner membrane of E. coli consists of ~70-80% PE, 15-20% PG and 5% or less of

137

cardiolipin (CL).50, 51 Gram-positive bacteria lack an outer membrane, but have thicker

138

peptidoglycan cell walls. For Gram-positive bacteria such as Bacillus subtilis the inner

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139

membrane composition is approximately 70% PG, 12% PE and 4% CL.52 Although the

140

compositions of bacterial inner membranes vary among different species and strains,

141

studying a model bacterial membrane can be expected to provide general insight into

142

bacterial inner membrane permeability.

143 144

We constructed models of five environmentally relevant neutral thiol compounds, i.e.

145

CH3SH (a low molecular weight thiol and Cys side-chain analogue), CH3CH2SCH3

146

(thioether and Met side chain analogue), CH3S–SCH3 (disulfide analogue), and the Hg-

147

containing complexes CH3Hg–SCH3 and CH3S–HgII–SCH3 (Scheme 1). The CHARMM

148

36 all-atom additive force field53 was used to describe the POPE and POPG

149

phospholipids together with TIP3P water54 and Na+ and Cl- counterions.55 Force field

150

parameters for the Hg-containing compounds CH3Hg–SCH3 and CH3S–HgII–SCH3 were

151

optimized with the Force Field Toolkit (ffTK)56, which is a plugin implemented in

152

VMD.57 For consistency, the parameters for CH3SH, CH3CH2SCH3 and CH3S–SCH3

153

were also optimized with the same approach.

154 155

The initial coordinates for the membrane used in our simulations were taken from a

156

previously published, thoroughly equilibrated (2.1 µs NPT) system with a POPE:POPG

157

ratio of 3:1 and 0.15 M NaCl.58 This membrane system consists of 128 POPE lipids, 42

158

POPG lipids, and 6040 water molecules. We performed an additional 1 ns equilibration

159

in the NVT ensemble followed by 20 ns NPT equilibration. The system pressure is shown

160

in Figure S1. To prepare the membrane systems containing the desired solutes, the final

161

snapshot of the 20 ns of NPT equilibration was selected. Each solute was placed in the

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water layer and overlapping water molecules were removed. Energy minimization

163

followed by 1 ns of NVT equilibration was performed to bring each system to 303.15 K.

164

Another 20 ns NPT equilibration was performed after the NVT equilibration.

165 166

All MD simulations were carried out with GROMACS program (version 5.1.1)59 at a

167

pressure of 1 bar using the Berendsen barostat60 with a relaxation time of 2 ps, and

168

temperature of 303.15 K using the v-rescale thermostat61 with a relaxation time of 1 ps

169

with periodic boundary conditions. It has been shown that the stochastic velocity

170

rescaling thermostat does not affect particle velocities directly, and natural time

171

correlations are preserved 61, 62 Semi-isotropic coupling was applied such that changes of

172

the box size in the Z-direction (normal to the membrane bilayer) were uncoupled from

173

the X- and Y-directions. Electrostatic interactions were treated with the particle-mesh

174

Ewald (PME) full-range electrostatics method.63 A switching function was used to treat

175

van der Waals interactions by gradually reducing the force to zero between 0.8 and 1.2

176

nm. All bonds involving hydrogen atoms were constrained with the LINCS algorithm.64

177

Initial configurations for umbrella sampling were generated by pulling solutes through

178

the membrane system along the Z-axis using steered molecular dynamics (SMD)

179

simulations. The statistical errors in the free energy profiles were estimated from

180

bootstrap analysis. 65 The free energy for each solute in bulk water was normalized to

181

zero to facilitate comparison. Additional details are provided in the Supporting

182

Information.

183

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Using the Marrink-Berendsen inhomogeneous solubility-diffusivity model (ISDM),66 the

185

permeability coefficient (Pm) can be estimated as follows:

186

# $

∆+ ,

=

12 &'( ( -. ) 𝑑𝑧 1# 0(1)

(1)

187 188

where D(z) is the position-dependent diffusion coefficient, which is estimated using the

189

Hummer positional autocorrelation function,67, 68 DG(z) is the potential of mean force,

190

which can be obtained from umbrella sampling, R is the gas constant (8.314 J mol-1 K-1),

191

T is the temperature and z is the position of the solute along the membrane normal. The

192

trajectories obtained from umbrella sampling simulations were used to calculate D(z).

193

Additional details are provided in the Supporting Information.

194 195

In additional calculations, an implicit membrane model was considered. For these

196

calculations, a dielectric slab model of the cytoplasmic membrane was constructed using

197

APBSmem version 2.0269, 70 APBS calculates the electrostatic solvation energy of solutes

198

by solving the Poisson-Boltzmann equation. For these calculations, atomic partial charges

199

were taken from the ffTK parameters developed in this study, with radii from the PARSE

200

force field for H, C and S atoms.71, 72 The PARSE force field does not include a radius for

201

Hg, so the Bondi radius of 1.7 Å was used.73 Solvation free energies were computed as

202

the sum of electrostatic (ΔGelec) and nonpolar solvation free energies (ΔGnp):

203

ΔGsolv = ΔGelec + ΔGnp

204

Electrostatic contributions were calculated with APBS, and nonpolar contributions were

205

calculated as described previously74 with MSMS version 2.6.1,75 which is called by

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APBSmem. Additional details and an example APBS input file are provided in the

207

Supporting Information.

208

3. Results and Discussion

209

3.1 Force field parameters

210

The optimized geometries and partial atomic charges for the five solutes are shown in

211

Scheme S1. The partial charges of HgII are 0.53 and 0.55 for CH3Hg–SCH3 and CH3S–

212

HgII–SCH3, respectively, values of about one quarter of the formal charge of Hg2+.

213

Optimized force constants and equilibrium values for bonds, angles and dihedrals are

214

provided in Tables S1-S5. The fits to the quantum mechanical potential energy surfaces

215

from torsional scans are shown in Figure S2.

216 217

3.2 Model membrane

218

To assist in the discussion of free energy profiles of the five solutes, we adopted the four-

219

region membrane notation proposed by Marrink and Berendsen66 and computed partial

220

density profiles of the simulation system (Figure 1). The center of the membrane system

221

is defined as the reference point (i.e., z = 0). Region I (z = 0–1.0 nm) contains primarily

222

the lipid tail groups, as is apparent from the partial density maximum in this region.

223

Region II (z = 1.0–2.0 nm) is the interfacial area between the hydrophobic core and

224

hydrophilic groups. The density of the tail groups drops dramatically in this region,

225

whereas the densities of water, phosphate and the polar head groups increase. Carbonyl

226

groups are a major component in this region; the density increases to a maximum at z =

227

1.6 nm and then drops off. Correspondingly, the density of the whole system also reaches

228

a maximum of ~1,300 kg cm-3 at the interface of regions II and III. Region III (z = 2.0–

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229

2.5 nm) is the interface between bulk water and phospholipid head groups. Water

230

molecules, charged phosphate groups and head groups are the main components of this

231

region, although some carbonyl and lipid tail densities are present. Region IV (z > 2.5 nm)

232

consists mainly of bulk water. However, small fractions of the phosphate and head group

233

densities are also present in this region. I"

II" III"

IV"

Distance"from"membrane"center"(Z)"

234 235

Figure 1. Model cytoplasmic membrane system (top) and partial density profiles

236

(bottom). The vertical lines divide half of the membrane system into four regions. Region

237

I: lipid tail groups (z = 0–1 nm); Region II: hydrophobic-hydrophilic interface (z = 1–2

238

nm); Region III: head group-water interface (z = 2–2.5 nm); Region IV: bulk water (z >

239

2.5 nm). Water molecules are shown in the CPK representation in red and white. The

240

lipid tails are shown as cyan lines. O, N, and P are shown as red, blue and gold spheres,

241

respectively.

242 243 12


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3.3 Free energy profiles

245

To investigate how complexation with HgII and [CH3Hg]+ affects the partitioning of low

246

molecular weight thiols into the model bacterial inner membrane and how CH3Hg–SCH3

247

compares with CH3CH2SCH3, we divided the five solutes into two groups on the basis of

248

their structural characteristics. We first discuss and compare the free energy profile for

249

CH3Hg–SCH3 with those of CH3SH and CH3CH2SCH3 (Figure 2A-C) and then compare

250

CH3S–SCH3 to CH3S–HgII–SCH3 (Figure 2D-E).

251 III"

IV"

CH3SH"

0 -1 -2

-3 B 1 0

ΔG"(kcal"mol,1)"

II"

ΔG"(kcal"mol,1)"

I"

1

0.5

1

1.5

2

2.5

3

3.5

CH3CH2SCH3"

0 -1 -2

ΔG"(kcal"mol,1)"

ΔG"(kcal"mol,1)"

A

ΔG"(kcal"mol,1)"

I" 1 0

II"

IV"

III"

CH3S,SCH3"

-1 -2

E

-3 1 0

0

0.5

1

1.5

2

2.5

3

3.5

0.5

1

1.5

2

2.5

3

3.5

CH3S,HgII,SCH3"

-1 -2 -3

-3

C 1 0 0.5 1 1.5 CH3Hg,SCH3"

D

2

2.5

3

3.5

2

2.5

3

3.5

0


0 -1 -2 -3

252

0

0.5

1

1.5


253

Figure 2. Computed free energy profiles for the passive transport of solutes across a

254

model cytoplasmic membrane. Only half the membrane is shown here and in Figure 3.

255 256

3.3.1 Distribution of CH3SH, CH3CH2SCH3 and CH3Hg–SCH3

257

Because the membrane bilayer in this study is symmetric, we confine our discussion to

258

only half the system. For CH3SH, the calculated free energy difference between the

259

solute in bulk water and at the center of membrane (z = 0) is ~1.5 kcal mol-1.

13



260

Two isoenergetic free energy minima for CH3SH are present: one at the center of the

261

membrane (region I), and the other at the interface of the tail and head groups (region II,

262

Figure 2A). Two small local energy maxima are present in region I (z = ~0.7 nm) and

263

region III (z = ~2.2 nm), respectively. Overall, CH3SH prefers the hydrophobic

264

environment and partitions favorably in a relatively broad region (z = ~0–1.5 nm).

265 266

CH3CH2SCH3 shows a similar distribution pattern to the Cys analogue (CH3SH) (Figure

267

2B). The free energy difference between CH3CH2SCH3 in bulk water and the global

268

minimum in the center of the membrane is ~2.8 kcal mol-1, somewhat larger than that of

269

CH3SH. CH3CH2SCH3 is slightly more hydrophobic and therefore partitions more readily

270

into the tail region. A similar solvation free energy difference was also observed in a

271

previous study of Cys and Met side chains in a DOPC membrane bilayer.35 The global

272

energy minimum for CH3CH2SCH3 is also located at the center of the membrane (i.e., z =

273

0). However, the local minimum in region II is less favorable than the global minimum,

274

which is at z = 0, by ~0.5 kcal mol-1.

275 276

CH3Hg–SCH3 shows a different distribution pattern ¾ it is relatively energetically

277

unfavorable for this molecule to reside in either bulk water or the hydrophobic tail region

278

and these two regions are roughly isoenergetic (Figure 2C). Instead, the global free

279

energy minimum is located at the interface of the head and tail groups (region II). The

280

free energy difference between the global minimum (region II) and bulk water (region IV)

281

is ~2 kcal mol-1 for CH3Hg–SCH3. A local minimum is present in the hydrophobic tail

282

area (region I) where the global minima for CH3SH and CH3Hg–SCH3 are located.

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3.3.2 Distribution of CH3S–SCH3 and CH3S–HgII–SCH3

284

The free energy profile for CH3S–HgII–SCH3 is very similar to that of CH3S–SCH3,

285

which does not contain Hg (Figure 2D and 2E). The free energy difference between the

286

solute in bulk water and at the global minimum is ~2 kcal mol-1 for both compounds.

287

Unlike CH3Hg–SCH3, partitioning of CH3S–SCH3 and CH3S–HgII–SCH3 in the

288

hydrophobic core is more favorable than in the bulk water. The global free energy

289

minima for CH3S–SCH3 and CH3S–HgII–SCH3 are both located at the interface between

290

the hydrophobic acyl chains and the polar carbonyl and phosphate groups (region II).

291 292

Overall, our free energy calculations show that the energy difference in bulk water and at

293

the most favorable position inside the membrane for each solute in this study is ~2 kcal

294

mol-1. Thus, these small, neutral, Hg-containing complexes are likely to permeate rapidly

295

through the cytoplasmic membrane.

296 297

3.3.3 Free energy profiles computed with an implicit membrane model

298

Implicit membrane models based on Poisson-Boltzmann theory can offer insight into

299

membrane permeation at a fraction of the computational cost of all-atom MD simulations.

300

We constructed a simple five-slab continuum model69, 76 (see Methods) and compared the

301

free energy profiles to the all-atom results. This five-slab partitioning should not be

302

confused with the four-region description in section 3.2. The dielectric constant, or

303

relative permittivity, e, describes how a molecule interacts with an electric field. The use

304

of three dielectric constants to represent the solvent, hydrophilic head groups, and

305

hydrophobic tails of a symmetric lipid bilayer provides an intuitive physical description

15



306

of the membrane environment both within each region and at the interfaces of adjacent

307

regions.

308

CH3SH%

CH3S'SCH3%

CH3CH2SCH3%

CH3S'HgII'SCH3% 1.8 nm ε=2

0.7 nm ε=10 ε!=80

CH3Hg'SCH3% 3.5

2.5 2.2 1.8

0.9

0.0

309

(nm)

310

Figure 3. Computed free energy profiles from all-atom umbrella sampling MD (A-E,

311

purple) and dielectric continuum membrane models (A-E, blue). Schematic

312

representation of the continuum model (F).

313 314

The implicit membrane model qualitatively reproduces the trends observed in the all-

315

atom MD simulations (Figure 3), including the effects of incorporating Hg into the

316

solutes. However, the dielectric continuum model generally overestimates the

317

favorability of the solute in the membrane relative to bulk water by ~1–3 kcal mol-1.

318

Solvation free energies computed with an implicit membrane model are the sum of an

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electrostatic (ΔGelec) and a nonpolar term (ΔGnp). Inspection of these two quantities

320

shows that at all membrane depths the magnitude of ΔGnp for all five neutral solutes is

321

larger than ΔGelec and the two terms have opposite signs (Figure S3). Therefore, the

322

difference with the all-atom MD arises from the difference between the positive

323

(unfavorable) electrostatic interactions and the negative (favorable) nonpolar interactions

324

being larger.

325 326

Our simple dielectric slab membrane model requires only geometry optimization and

327

orientation of the solute with respect to the membrane, and uses only three dielectric

328

constants to represent the membrane. To improve the accuracy of the implicit membrane

329

model, a more sophisticated approach, such as the heterogeneous dielectric generalized

330

Born (HDGB) method,77 could be used. Mirjalili and Feig assessed the ability of three

331

different implicit membrane models to reproduce free energy profiles obtained from all-

332

atom, umbrella sampling simulations in explicit solvent. In their study, the energy

333

profiles in implicit solvent were generated using 1.5 ns of MD for each umbrella window,

334

and the dielectric constant was varied as a function of membrane depth. They found that

335

the HDGB implicit model yielded the closest agreement with all-atom umbrella sampling

336

simulations. Although the HDGB model provides better agreement with MD simulations

337

in explicit solvent, our aim was to present a rapid and accessible approach for

338

qualitatively estimating the permeability profiles of Hg-containing solutes without

339

requiring umbrella sampling MD simulations.

340 341

3.4 Energy Decomposition Analysis

17



342

3.4.1. Energy decomposition of CH3SH and CH3Hg–SCH3

343

We observed distinct differences in the all-atom free energy profiles for CH3SH and

344

CH3Hg–SCH3, indicating that complexation of CH3SH by [CH3Hg]+ indeed alters its

345

partitioning (Figure 2). To examine the thermodynamic driving forces that govern the

346

partitioning behavior of the solutes, we computed the total non-bonded interaction

347

energies between the solutes and the rest of the system (Esol-sys) over the 20 ns umbrella

348

sampling trajectories for CH3SH and CH3Hg–SCH3 (Figure S4).

349 350

In both cases, we found that Esol-sys favors partitioning from bulk water into the interface

351

between region II and region III. The two energy minima of Esol-sys for CH3SH and

352

CH3Hg–SCH3 are both located at around z = 2 nm, at which point the membrane has the

353

most diverse chemical composition (Figure 1), providing a suitable environment for

354

amphiphilic compounds. As the solutes move toward the tail region, Esol-sys becomes

355

unfavorable for both CH3SH and CH3Hg–SCH3. However, for CH3SH, the increase in

356

Esol-sys (from z = 2 to z = 0 nm) is ~4 kcal mol-1, whereas Esol-sys for CH3Hg–SCH3

357

increases much more dramatically, by ~10 kcal mol-1.

358 359

To characterize the individual contributions to Esol-sys (Figure S4), we decomposed DEsol-

360

sys

361

Unsurprisingly, Esol-water and Esol-lip showed opposite trends — when moving solutes

362

toward the hydrophobic core, the interactions with lipids becoming more favorable and

363

the interactions with water less favorable. As a result, the energy minima in DEsol-sys for

364

the solutes are all located at z = ~2 nm (Figure S5).

into solute-water (Esol-water) and solute-lipid (Esol-lip) interaction energies (Figure S5).

365

18


Page 18 of 34

Page 19 of 34


366

To identify which specific lipid components stabilize the solutes inside the membrane

367

and understand sources of the difference in the free energy profiles between CH3SH and

368

CH3Hg–SCH3, we further decomposed Esol-lip into solute-lipid head (Esol-head), solute-

369

phosphate (Esol-phosphate), solute-carbonyl (Esol-carbonyl) and solute-tail (Esol-tail) interaction

370

energies (Scheme S2). For CH3SH and CH3Hg–SCH3, the total non-bonded interaction

371

energies between the individual solutes and lipid head groups (Esol-head) are favorable, and

372

no statistically significant difference in Esol-head was found (Figure S6). As expected, both

373

solutes interact favorably with the charged phosphate groups, and again, no significant

374

difference in Esol-phosphate was observed. However, we found that CH3Hg–SCH3 interacts

375

more strongly with the carbonyl groups than does CH3SH (Figure S6). This effect arises

376

because Hg, which has a positive partial charge (+0.53) in CH3Hg–SCH3, has a strong

377

affinity for the negatively charged carbonyl oxygens. It is also interesting that CH3SH

378

and CH3Hg–SCH3 show significant differences in their interactions with the lipid tail

379

groups, with CH3Hg–SCH3 interacting ~7.5 kcal mol-1 more favorably than CH3SH

380

(Figure S6). However, we then normalized Esol-tail by the number of atoms in each

381

molecule and found that Esol-tail for CH3Hg–SCH3 and Cys nearly overlap (Figure S7).

382

Therefore, the more favorable Esol-tail interactions for CH3Hg–SCH3 come mainly from an

383

additional number of contacts with the tail groups (Figure S8).

384 385

Both POPE and POPG contain two acyl chains comprising one saturated (chain A) and

386

one unsaturated (chain B) tail (Scheme 1). CH3SH interacts with each of the two chains

387

of both lipids in a very similar manner (Figure S9). However, CH3Hg–SCH3 interacts

388

more strongly with chain B than with chain A (Figure S9).

19



389 390

3.4.2. Energy decomposition of CH3S–SCH3 and CH3S–HgII–SCH3

391

Unlike the CH3SH and CH3Hg–SCH3 pair, complexation of HgII by two CH3S– ligands to

392

form CH3S–HgII–SCH3 does not result in a different free-energy minimum in the

393

membrane relative to CH3S–SCH3 (Figure 2D and E). For both solutes the global

394

minima are located in region II. Again, to understand the thermodynamic contributions to

395

the observed free energy profiles, we calculated the non-bonded interaction energies

396

between each solute and the rest of the system. Similar to the findings for the CH3SH and

397

CH3Hg–SCH3 pair, the minima in Esol-sys for CH3S–SCH3 and CH3S–HgII–SCH3 are

398

located around z = 2 nm (Figure S10). As the two solutes are moved from their

399

respective minima (z = ~2 nm) to the center of the membrane (z = 0 nm), Esol-sys for both

400

solutes becomes unfavorable, but the increase in Esol-sys is different. For CH3S–HgII–SCH3,

401

Esol-sys increases by ~13 kcal mol-1, whereas the non-Hg containing compound CH3S–

402

SCH3 increases by only ~5 kcal mol-1. Thus, more energy is required to partition CH3S–

403

HgII–SCH3 from region II to the center of the membrane.

404 405

Again, we divided the POPE and POPG phospholipids into four groups: lipid head,

406

phosphate, carbonyl and tail groups (Scheme S2), and compared the interaction energies

407

between the solutes (CH3S–SCH3 and CH3S–HgII–SCH3) and individual groups (Figure

408

S11). Similar to the CH3SH and CH3Hg–SCH3 pair, CH3S–SCH3 and CH3S–HgII–SCH3

409

show similar interaction energies with the lipid head groups and phosphate groups. The

410

Hg-containing compound CH3S–HgII–SCH3 again exhibits more favorable interactions

411

with the carbonyl group than CH3S–SCH3, and CH3S–HgII–SCH3 also shows ~5 kcal

20


Page 20 of 34

Page 21 of 34


412

mol-1 stronger interactions with the tail groups than does CH3S–SCH3. Therefore,

413

compared with CH3S–SCH3, the major source of the more favorable Esol-lip interaction for

414

CH3S–HgII–SCH3 comes from the stronger interaction with the lipid tail and carbonyl

415

groups.

416 417

3.5 Permeability coefficients

418

Using the Marrink-Berendsen solubility-diffusion model,66 we computed the permeability

419

coefficients for all five solutes using the data from the all-atom umbrella sampling

420

simulations (Table 1). The calculated position-dependent diffusion coefficients D(z) are

421

provided in Figure S12. We found that, although CH3SH and CH3Hg–SCH3 showed

422

distinct distribution patterns inside the membrane, their permeability coefficients are

423

quite similar, with Pm = 6.08 ×10-5 cm s-1 for CH3SH and 8.06 × 10-5 cm s-1 for CH3Hg–

424

SCH3. In contrast, comparing CH3S–SCH3 with the CH3S–HgII–SCH3, we found that

425

CH3S–HgII–SCH3 (Pm = 1.63 × 10-5 cm s-1) permeates significantly faster than CH3S–

426

SCH3 (6.32 × 10-6 cm s-1) (Table 1). The above results indicate that complexation with

427

either HgII or [CH3Hg]+ increases the permeability relative to the corresponding non-Hg-

428

containing compounds. We also found that CH3Hg–SCH3 permeates the most rapidly

429

among the five solutes in this study.

430 431

The Marrink-Berendsen inhomogeneous solubility-diffusion model is known to

432

accurately describe the permeation of small molecules, such as water, ethane, and

433

methylamine. 78-80 However, for some large, asymmetric, lipophilic molecules, ISDM

434

may not be suitable. The main reasons lie in the significant change in intramembrane

21



Page 22 of 34

435

orientation of solutes and the strong intramolecular hydrogen bonding with membranes.

436

In those cases, a dynamic mechanistic model constructed using individual rate constant

437

may be more suitable to describe the permeation behavior. 81 However, the solutes in our

438

study are relatively small (< 5 heavy atoms) and cannot form hydrogen bonds with the

439

membrane. Thus, we expect that the Marrink-Berendsen ISDM model is appropriate in

440

the present study.

441

Table 1. Calculated Passive Permeability Coefficients (Pm) for the Five Solutes and

442

Experimentally Measured Pm for Hg-containing Compounds, Water and Selected

443

Amino Acids. Compound CH3SH CH3CH2SCH3 CH3Hg–SCH3 CH3S–SCH3 CH3S–HgII–SCH3 HgCl2 CH3HgCl lysine lysine methyl ester

Permeability Coefficient (cm s-1) 6.08 × 10-5 3.41 × 10-6 8.06 × 10-5 6.32 × 10-6 1.63 × 10-5 1.3 × 10-2 1.5 × 10-4 7.4 × 10-4 2.6 × 10-3 7.2 × 10-4 5.1 × 10-12 1.9 × 10-11 2.1 × 10-2

Type of membrane

Ref.

model cytoplasmic membrane (75% POPE/25% POPC)

This study

egg lecithin-cholesterol egg-PC/cholesterol (pH = 5.0) marine diatom egg-PC/cholesterol (pH = 5.0) marine diatom Egg-PC DMPC Egg-PC

28 29 15 29 15 82 82 83

444 445

Transfer free energies from water into a hydrophobic solvent are often used to represent

446

the transfer of a given solute from bulk solvent to the hydrophobic tails of lipids. We

447

compared our calculated free energies with experimentally measured transfer free

448

energies of Cys and Met side chains from water into cyclohexane (Figure S13).84 Our

22


Page 23 of 34


449

calculated values of 1.75 and 2.75 kcal/mo1 for Cys and Met are in very good agreement

450

with the experimentally measured free energies of 1.28 and 2.35 kcal/mo1, respectively.

451 452

We also compared the calculated free energy profiles of Cys and Met side chains with a

453

previous all-atom simulation study of amino acids in a dioleoylphosphatidylcholine

454

(DOPC) bilayer (Figure S13). 35 The two free energy profiles for the Cys analogue

455

nearly overlap. For the Met analogue, the two computed profiles analogue show good

456

agreement from the bulk water region to the head-tail interface region (i.e., from z = 3.5

457

nm to z = 1 nm), but there is an obvious energy discrepancy in the tail region (i.e., from z

458

=1 nm to z = 0 nm) (Figure S13). We emphasize that the membrane system used by

459

MacCallum et al. was DOPC, whereas ours is a mixed POPE/POPG bilayer. DOPC

460

phospholipids have two unsaturated tails, but POPE/POPG has only one. Thus, our

461

simulation results are in overall agreement with those of MacCallum et al., and the

462

energetic differences in the tail regions of the two computed free energy profiles can be

463

attributed to differences in the unsaturated tails of the lipids.

464 465

Experimentally measured Pm values for the Hg-containing solutes presented in this study

466

have not been reported. However, we can compare the predicted Pm with values obtained

467

for other Hg species, HgCl2 and CH3HgCl, and also with water (Table 1 and Table S7).

468

An experimentally measured Pm value for HgCl2 through an egg lecithin-cholesterol

469

membrane is 1.3 × 10-2 cm s-1.28 In contrast, a Pm value estimated for HgCl2 crossing egg-

470

PC and egg-PC/cholesterol membranes was measured to be significantly lower: 1.5 × 10-

471

4

cm s-1 at pH 5.0.29 In another study, the permeability coefficient of HgCl2 through the 23



472

cytoplasmic membrane of a marine diatom was measured to be 7.4 × 10-4 cm s-1.15 Hence,

473

there is considerable variation in the measured HgCl2 rates. Nevertheless, all are orders of

474

magnitude faster than the rates estimated here for the solutes studied.

475 476

For CH3HgCl, a measured Pm value through an egg-PC/cholesterol membrane is 2.6 ×

477

10-3 cm s-1,29 whereas the value obtained through a marine diatom membrane is 7.2 × 10-

478

4

479

rapidly than HgCl2. This observation seems to be consistent with our findings — the

480

addition of a methyl group increases permeability through cell membranes. The

481

experimentally measured Pm value for H2O permeating through a

482

dipalmitoylphosphocholine (DPPC) membrane (95% DPPC) is 2.6 × 10-5 cm s-1, which

483

is similar to the present value estimated for CH3Hg–SCH3.85 However, in another

484

experiment, the Pm for H2O through an egg PC-decane membrane was much faster, at 3.4

485

× 10-3 cm s-1.27

cm s-1.15 CH3HgCl would thus appear to permeate through membranes even more

486 487

Thiolate-bound Hg species are among the most abundant forms of HgII in intracellular

488

environments and are likely to be involved in HgII uptake. In the present study, we have

489

shown that neutral side chain analogues of Hg(Cys)2 and CH3Hg–Cys can cross a model

490

membrane passively. However, free amino acids, such as cysteine, generally are present

491

in zwitterionic forms in the condensed phase. Thus, it is important to consider the

492

permeability of the zwitterionic forms of Hg(Cys)2 and CH3Hg–Cys and to examine the

493

feasibility of passive permeation of those complexes.

494 24


Page 24 of 34

Page 25 of 34


495

Passive permeability coefficients have been measured experimentally for various amino

496

acids, including polar, non-polar and charged species (Table 1 and Table S7).82 Their Pm

497

values are in the range of ~10-10 to 10-12 cm s-1, which is on the same order as divalent

498

Hg2+ (10-11 cm s-1).30 However, neutralizing the termini of the amino acid lysine through

499

methylation increases the permeability by a factor of ~1010 (Table 1).86 Therefore,

500

changing neutral side chain analogues to the corresponding zwitterion complexes reduces

501

the permeation rates considerably. Although complexation with HgII or [CH3Hg]+

502

increases the permeability of the Cys side chain analogue somewhat—in the present

503

calculations at most by approximately threefold—this enhancement would not be enough

504

to compensate for the increased unfavorability resulting from the presence of the N- and

505

C- termini. Thus, for zwitterionic Hg-coordinated amino acids, membrane-bound

506

transporters may be required for uptake, export, or both. Nevertheless, for small, neutral

507

Hg-containing compounds (e.g. CH3S–HgII–SCH3, CH3Hg–SCH3, and similar molecules)

508

the present calculations suggest that passive permeation cannot be excluded. Our results

509

are consistent with MD work on the preferred locations for partitioning Cys and Met

510

side-chain analogues into a dioleoylphosphatidylcholine (DOPC) bilayer, in which it was

511

found that both molecules favorably transfer from bulk water to the center of the

512

membrane. In another study, a similar distribution pattern was observed for Cys and Met

513

side-chain analogues in a dimyristoylphosphatidylcholine (DMPC) model membrane.37

514

Lastly, we note that Hg-amino acid complexes can have coordination numbers higher

515

than 2 for Hg.87 In addition to thiolate ligands, amine and carboxylate groups can also

516

coordinate Hg,88 and this may facilitate the permeation of Hg complexes as well.

517

25



518

4. Implications for Hg uptake in bacteria

519

Previous experimental studies have attributed the uptake of Hg in bacteria to the result of

520

accidental uptake by metal transporters and facilitation by complexation with cysteine.19,

521

21

522

weight thiolate-Hg complexes through a model gram-negative bacterial cytoplasmic

523

membrane. We find that, without facilitation by membrane-bounded transporters, it is

524

energetically feasible for thiolate-Hg complexes to permeate through the membrane. The

525

calculated permeability coefficients for these compounds are ~10-5 to 10-6 cm s-1, which

526

is on the same order of the passive permeation rate of H2O through a DPPC membrane.85

527

As reported previously, it takes less than 1 ns for a water molecule to permeate a

528

POPE/POPC membrane,58 and one might thus expect a comparable time scale for the

529

present Hg complexes in a POPE/POPG membrane. Thus, appreciable amounts of Hg in

530

this form would permeate passively across the membrane. The passive permeation

531

coefficients for full amino acids, as opposed to side chain analogues, have been measured

532

to be ~10-10 to 10-12 cm s-1, suggesting that amino acid complexes permeate too slowly to

533

permit substantial passive uptake. In these cases, as suggested by Schaefer et al.,21 metal

534

transporters are likely required for efficient Hg(Cys)2 uptake.

Here we have examined the feasibility of passive permeation of neutral, low molecular

535 536

Supporting information

537

Partial charges and geometries, structure of POPE/POPG, CHARMM force field

538

parameters for the five solutes (PDB, RTF, and PAR files), passive permeation

539

coefficients, system pressure, potential energy surface from torsional scans, non-bonded

540

interaction energies, energy decomposition of Esol-sys, normalized non-bonded solute-tail

26


Page 26 of 34

Page 27 of 34


541

interaction energies, number of contacts with the lipid tail groups, interaction energies

542

with individual chain, comparison with experimentally measured free energies and

543

previous all-atom simulations, calculated diffusion coefficients, complete Gaussian 09

544

Reference, example APBS input file, and additional details for force field

545

parameterization, umbrella sampling, and implicit membrane calculations.

546 547

Corresponding author:

548

Jerry M. Parks

549

UT/ORNL Center for Molecular Biophysics

550

Biosciences Division

551

Oak Ridge National Laboratory

552

1 Bethel Valley Road

553

Oak Ridge, TN 37831-6309

554

Email: [email protected]

555

Tel: (865) 574-9259

556

27



557

Acknowledgements

558

This work was supported by the U.S. Department of Energy (DOE) Office of Science,

559

Biological and Environmental Research, Subsurface Biogeochemical Research (SBR)

560

Program through the Mercury Scientific Focus Area Program (SFA) at Oak Ridge

561

National Laboratory (ORNL). ORNL is managed by UT-Battelle LLC for the U.S. DOE

562

under contract number DE-AC05-00OR22725. This work used resources of the Compute

563

and Data Environment for Science (CADES) at Oak Ridge National Laboratory. This

564

research also used resources at the National Energy Research Scientific Computing

565

Center (NERSC), which is supported by the Office of Science of the U.S. DOE under

566

Contract No. DE-AC02-05CH11231. SJC was supported by NIH/NIGMS-IMSD grant

567

R25GM086761. We thank Christopher T. Lee for providing the script to compute

568

diffusion coefficients, Jianhui Tian for assistance with MD simulations, and Baohua Gu

569

for insightful discussions.

570

28


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34. 35. 36. 37. 38. 39. 40. 41. 42. 43.

44.

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Modeling of the Passive Permeation of Mercury ... - ACS Publications

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