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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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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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1
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Knoxville, Tennessee, 37996, USA
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2
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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
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Tennessee, Knoxville, Tennessee, 37996, USA
Department of Biochemistry and Cellular and Molecular Biology, University of
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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
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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
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membranes. Various mechanisms have been proposed for HgII uptake in
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microorganisms.13, 14 For those microorganisms encoding the mer operon, which
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detoxifies the cells from Hg, the uptake of HgII into the cytoplasm may be mediated by
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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
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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
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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
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these have been limited to inorganic HgII compounds such as HgCl2. Experimental
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approaches that have been used to measure the permeability of Hg2+ or Hg-containing
69
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
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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 ×
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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
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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
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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
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in their zwitterionic forms. However, inside the hydrophobic core of a lipid bilayer,
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protons may be transferred from the N-terminus to the C-terminus. Thus, in the above
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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
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in protonation state of the termini upon partitioning into hydrophobic membranes. Side
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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
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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
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cancel to a large extent for analogous systems.
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Several MD simulation studies have been carried out for Hg0, Hg2+, and other charged
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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
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into heterogeneous lipid membranes, to our knowledge the changes in membrane
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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
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transferring low molecular weight thiols with and without Hg or MeHg from aqueous
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solution into a membrane bilayer. Specifically, we investigate how the complexation of
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HgII or [CH3Hg]+ alters the permeability characteristics of CH3SH and CH3S–SCH3,
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which are side chain analogues of the amino acids Cys and Met, respectively. First, we
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develop CHARMM molecular mechanics force field parameters for the selected solutes.
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We then compute free energy profiles for the passive permeation of the solutes across a
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lipid bilayer that mimics the composition of gram-negative bacterial inner membranes
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(i.e., 75% POPE (1-hexadecanoyl-2-(9Z-octadecenoyl)-sn-glycero-3-
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phosphoethanolamine) and 25% POPG (1-hexadecanoyl-2-(9Z-octadecenoyl)-sn-
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glycero-3-phospho-(1'-rac-glycerol)). Next, we identify the energetic factors that
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contribute to the observed differences between the thiols and their Hg-containing
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counterparts and compute the permeability coefficients for those solutes. We also
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examine whether a simplified implicit membrane model reproduces the energetic trends
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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
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Most Hg-methylating bacteria are gram-negative.13, 47 Gram-negative bacteria are
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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
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not been thoroughly characterized. However, the inner membrane composition of
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Desulfovibrio gigas, which does not methylate Hg, was found to be 30%
135
phosphatidylethanolamine (PE) and 70% phosphatidylglycerol (PG).49 For comparison,
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the inner membrane of E. coli consists of ~70-80% PE, 15-20% PG and 5% or less of
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cardiolipin (CL).50, 51 Gram-positive bacteria lack an outer membrane, but have thicker
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peptidoglycan cell walls. For Gram-positive bacteria such as Bacillus subtilis the inner
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membrane composition is approximately 70% PG, 12% PE and 4% CL.52 Although the
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compositions of bacterial inner membranes vary among different species and strains,
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studying a model bacterial membrane can be expected to provide general insight into
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bacterial inner membrane permeability.
143 144
We constructed models of five environmentally relevant neutral thiol compounds, i.e.
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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-
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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
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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
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followed by 1 ns of NVT equilibration was performed to bring each system to 303.15 K.
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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
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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
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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.
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Using the Marrink-Berendsen inhomogeneous solubility-diffusivity model (ISDM),66 the
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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 DG(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
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Supporting Information.
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3. Results and Discussion
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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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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.
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3.3 Free energy profiles
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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
Distance"from"membrane"center"(Z)"
0 -1 -2 -3
252
0
0.5
1
1.5
Distance"from"membrane"center"(Z)"
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.
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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
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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
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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
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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
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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
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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 DEsol-
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 DEsol-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
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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).
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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Environmental Science & Technology
References 1. 2. 3. 4. 5. 6.
7. 8.
9. 10. 11. 12. 13. 14. 15. 16. 17.
Driscoll, C. T.; Mason, R. P.; Chan, H. M.; Jacob, D. J.; Pirrone, N. Mercury as a global pollutant: Sources, pathways, and effects. Environ. Sci. Technol. 2013, 47, (10), 4967-4983. Park, J.-D.; Zheng, W. Human exposure and health effects of inorganic and elemental mercury. J. Prev. Med. Public Health 2012, 45, (6), 344-352. Hong, Y.-S.; Kim, Y.-M.; Lee, K.-E. Methylmercury exposure and health effects. J. Prev. Med. Public Health 2012, 45, (6), 353-363. Bernhoft, R. A. Mercury toxicity and treatment: A review of the literature. J. Environ. Public Health 2011, 2012, 1-10. Von Burg, R. Inorganic mercury. J. Appl. Toxicol. 1995, 15, (6), 483-493. Stricks, W.; Kolthoff, I. Reactions between mercuric mercury and cysteine and glutathione. Apparent dissociation constants, heats and entropies of formation of various forms of mercuric mercaptocysteine and glutathione. J. Am. Chem. Soc. 1953, 75, (22), 5673-5681. Ravichandran, M. Interactions between mercury and dissolved organic matter––A review. Chemosphere 2004, 55, (3), 319-331. Liu, Y.-R.; Lu, X.; Zhao, L.; An, J.; He, J.-Z.; Pierce, E. M.; Johs, A.; Gu, B. Effects of cellular sorption on mercury bioavailability and methylmercury production by Desulfovibrio desulfuricans ND132. Environ. Sci. Technol. 2016, 50, (24), 13335-13341. Martell, A.; Smith, R.; Motekaitis, R. NIST Standard Reference Database 46. Critical Stability Constants of Metal Complexes Database Version 2004, 8. Aschner, M.; Aschner, J. L. Mercury neurotoxicity: Mechanisms of blood-brain barrier transport. Neurosci. Biobehav. Rev. 1990, 14, (2), 169-176. Booth, S.; Zeller, D. Mercury, food webs, and marine mammals: Implications of diet and climate change for human health. Environ. Health Perspect. 2005, 113, (5), 521-526. Peterson, S. A.; Van Sickle, J.; Herlihy, A. T.; Hughes, R. M. Mercury concentration in fish from streams and rivers throughout the western United States. Environ. Sci. Technol. 2007, 41, (1), 58-65. Hsu-Kim, H.; Kucharzyk, K. H.; Zhang, T.; Deshusses, M. A. Mechanisms regulating mercury bioavailability for methylating microorganisms in the aquatic environment: A critical review. Environ. Sci. Technol. 2013, 47, (6), 2441-2456. Barkay, T.; Miller, S. M.; Summers, A. O. Bacterial mercury resistance from atoms to ecosystems. FEMS Microbiol. Rev. 2003, 27, (2-3), 355-384. Mason, R. P.; Reinfelder, J. R.; Morel, F. M. Uptake, toxicity, and trophic transfer of mercury in a coastal diatom. Environ. Sci. Technol. 1996, 30, (6), 1835-1845. Gorski, P.; Armstrong, D.; Hurley, J.; Krabbenhoft, D. Influence of natural dissolved organic carbon on the bioavailability of mercury to a freshwater alga. Environ. Pollut. 2008, 154, (1), 116-123. Najera, I.; Lin, C.-C.; Kohbodi, G. A.; Jay, J. A. Effect of chemical speciation on toxicity of mercury to Escherichia coli biofilms and planktonic cells. Environ. Sci. Technol. 2005, 39, (9), 3116-3120.
29
ACS Paragon Plus Environment
Environmental Science & Technology
616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660
18. 19. 20. 21. 22. 23.
24. 25. 26. 27. 28. 29.
30. 31. 32. 33.
Achá, D.; Hintelmann, H.; Yee, J. Importance of sulfate reducing bacteria in mercury methylation and demethylation in periphyton from Bolivian Amazon region. Chemosphere 2011, 82, (6), 911-916. Schaefer, J. K.; Rocks, S. S.; Zheng, W.; Liang, L.; Gu, B.; Morel, F. M. Active transport, substrate specificity, and methylation of Hg(II) in anaerobic bacteria. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, (21), 8714-8719. Eckley, C. S.; Hintelmann, H. Determination of mercury methylation potentials in the water column of lakes across Canada. Sci. Total Environ. 2006, 368, (1), 111125. Schaefer, J. K.; Szczuka, A.; Morel, F. o. M. Effect of divalent metals on Hg(II) uptake and methylation by bacteria. Environ. Sci. Technol. 2014, 48, (5), 30073013. Szczuka, A.; Morel, F. M.; Schaefer, J. K. Effect of thiols, zinc, and redox conditions on Hg uptake in Shewanella oneidensis. Environ. Sci. Technol. 2015, 49, (12), 7432-7438. Simmons-Willis, T. A.; Clarkson, T. W.; Ballatori, N. Transport of a neurotoxicant by molecular mimicry: The methylmercury–L-cysteine complex is a substrate for human L-type large neutral amino acid transporter (LAT) 1 and LAT2. Biochem. J. 2002, 367, (1), 239-246. Hoffmeyer, R. E.; Singh, S. P.; Doonan, C. J.; Ross, A. R.; Hughes, R. J.; Pickering, I. J.; George, G. N. Molecular mimicry in mercury toxicology. Chem. Res. Toxicol. 2006, 19, (6), 753-759. Colombo, M. J.; Ha, J.; Reinfelder, J. R.; Barkay, T.; Yee, N. Anaerobic oxidation of Hg(0) and methylmercury formation by Desulfovibrio desulfuricans ND132. Geochim. Cosmochim. Acta 2013, 112, 166-177. Payliss, B. J.; Hassanin, M.; Prenner, E. J. The structural and functional effects of Hg(II) and Cd(II) on lipid model systems and human erythrocytes: A review. Chem. Phys. Lipids 2015, 193, 36-51. Walter, A.; Gutknecht, J. Permeability of small nonelectrolytes through lipid bilayer membranes. J. Membr. Biol. 1986, 90, (3), 207-217. Gutknecht, J. Inorganic mercury (Hg2+) transport through lipid bilayer membranes. J. Membr. Biol. 1981, 61, (1), 61-66. Bienvenue, E.; Boudou, A.; Desmazes, J.; Gavach, C.; Georgescauld, D.; Sandeaux, J.; Sandeaux, R.; Seta, P. Transport of mercury compounds across bimolecular lipid membranes: Effect of lipid composition, pH and chloride concentration. Chem.-Biol. Interact. 1984, 48, (1), 91-101. Gutknecht, J. Cadmium and thallous ion permeabilities through lipid bilayer membranes. BBA-Biomembranes 1983, 735, (1), 185-188. Awoonor-Williams, E.; Rowley, C. N. Molecular simulation of nonfacilitated membrane permeation. BBA-Biomembranes 2016, 1858, (7), 1672-1687. Ulmschneider, J. P.; Smith, J. C.; White, S. H.; Ulmschneider, M. B. In silico partitioning and transmembrane insertion of hydrophobic peptides under equilibrium conditions. J. Am. Chem. Soc. 2011, 133, (39), 15487. Ulmschneider, M. B.; Doux, J. P.; Killian, J. A.; Smith, J. C.; Ulmschneider, J. P. Mechanism and kinetics of peptide partitioning into membranes from all-atom
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34. 35. 36. 37. 38. 39. 40. 41. 42. 43.
44.
45.
46. 47.
simulations of thermostable peptides. J. Am. Chem. Soc. 2010, 132, (10), 34523460. Johansson, A. C.; Lindahl, E. The role of lipid composition for insertion and stabilization of amino acids in membranes. J. Chem. Phys. 2009, 130, (18), 05B602. MacCallum, J. L.; Bennett, W. D.; Tieleman, D. P. Distribution of amino acids in a lipid bilayer from computer simulations. Biophys. J. 2008, 94, (9), 3393-3404. Li, L.; Vorobyov, I.; Allen, T. W. The different interactions of lysine and arginine side chains with lipid membranes. J. Chem. Phys. B 2013, 117, (40), 11906-11920. Johansson, A. C.; Lindahl, E. Position-resolved free energy of solvation for amino acids in lipid membranes from molecular dynamics simulations. Prot. Struct. Func. Bioinf. 2008, 70, (4), 1332-1344. Kuss, J.; Holzmann, J. R; Ludwig, R. An elemental mercury diffusion coefficient for natural waters determined by molecular dynamics simulation. Environ. Sci. Technol. 2009, 43, (9), 3183-3186. Migliorati, V.; Mancini, G.; Chillemi, G.; Zitolo, A.; D’Angelo, P. Effect of the Zn2+ and Hg2+ ions on the structure of liquid water. J. Chem. Phys. A 2011, 115, (18), 4798-4803. Mancini, G.; Sanna, N.; Barone, V.; Migliorati, V.; D'Angelo, P.; Chillemi, G. Structural and dynamical properties of the Hg2+ aqua ion: A molecular dynamics study. J. Chem. Phys. B 2008, 112, (15), 4694-4702. Li, X.; Tu, Y.; Tian, H.; Ågren, H. Computer simulations of aqua metal ions for accurate reproduction of hydration free energies and structures. J. Chem. Phys. 2010, 132, (10), 104505. Šebesta, F.; Sláma, V.; Melcr, J.; Futera, Z. k.; Burda, J. V. Estimation of transition-metal empirical parameters for molecular mechanical force fields. J. Chem. Theory Comput. 2016, 12, (8), 3681-3688. Guo, H.-B.; Johs, A.; Parks, J. M.; Olliff, L.; Miller, S. M.; Summers, A. O.; Liang, L.; Smith, J. C. Structure and conformational dynamics of the metalloregulator MerR upon binding of Hg(II). J. Mol. Biol. 2010, 398, (4), 555568. Hirano, Y.; Okimoto, N.; Kadohira, I.; Suematsu, M.; Yasuoka, K.; Yasui, M. Molecular mechanisms of how mercury inhibits water permeation through aquaporin-1: Understanding by molecular dynamics simulation. Biophys. J. 2010, 98, (8), 1512-1519. Fuchs, J. F.; Nedev, H.; Poger, D.; Ferrand, M.; Brenner, V.; Dognon, J. P.; Crouzy, S. New model potentials for sulfur–copper (I) and sulfur–mercury (II) interactions in proteins: From ab initio to molecular dynamics. J. Comput. Chem. 2006, 27, (7), 837-856. Lagache, M.; Ridard, J.; Ungerer, P.; Boutin, A. Force field optimization for organic mercury compounds. J. Chem. Phys. B 2004, 108, (24), 8419-8426. Ranchou-Peyruse, M.; Monperrus, M.; Bridou, R.; Duran, R.; Amouroux, D.; Salvado, J.; Guyoneaud, R. Overview of mercury methylation capacities among anaerobic bacteria including representatives of the sulphate-reducers: Implications for environmental studies. Geomicrobiol. J. 2009, 26, (1), 1-8.
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54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65.
Silhavy, T. J.; Kahne, D.; Walker, S. The bacterial cell envelope. Cold Spring Harb. Perspect. Biol. 2010, 2, (5), a000414. Makula, R.; Finnerty, W. Phospholipid composition of Desulfovibrio species. J. Bacteriol. 1974, 120, (3), 1279-1283. Huijbregts, R. P.; de Kroon, A. I.; de Kruijff, B. Topology and transport of membrane lipids in bacteria. BBA-Rev. Biomembranes 2000, 1469, (1), 43-61. Yamagami, A.; Yoshioka, T.; Kanemasa, Y. Differences in phospholipid composition between wild strains and streptomycin resistant mutants of certain enteric bacteria. Microbiol. Immunol. 1970, 14, (2), 174-176. Den Kamp, J. O.; Redai, I.; Van Deenen, L. Phospholipid composition of Bacillus subtilis. J. Bacteriol. 1969, 99, (1), 298-303. Klauda, J. B.; Venable, R. M.; Freites, J. A.; O’Connor, J. W.; Tobias, D. J.; Mondragon-Ramirez, C.; Vorobyov, I.; MacKerell Jr, A. D.; Pastor, R. W. Update of the CHARMM all-atom additive force field for lipids: Validation on six lipid types. J. Chem. Phys. B 2010, 114, (23), 7830-7843. Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, (2), 926-935. Beglov, D.; Roux, B. Finite representation of an infinite bulk system: Solvent boundary potential for computer simulations. J. Chem. Phys. 1994, 100, (12), 9050-9063. Mayne, C. G.; Saam, J.; Schulten, K.; Tajkhorshid, E.; Gumbart, J. C. Rapid parameterization of small molecules using the force field toolkit. J. Comput. Chem. 2013, 34, (32), 2757-2770. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graphics 1996, 14, (1), 33-38. Hong, C.; Tieleman, D. P.; Wang, Y. Microsecond molecular dynamics simulations of lipid mixing. Langmuir 2014, 30, (40), 11993-12001. Abraham, M. J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J. C.; Hess, B.; Lindahl, E. GROMACS: High-performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1, 19-25. Berendsen, H. J.; Postma, J. V.; van Gunsteren, W. F.; DiNola, A.; Haak, J. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984, 81, (8), 3684-3690. Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007, 126, (1), 014101. Basconi, J. E.; Shirts, M. R. Effects of temperature control algorithms on transport properties and kinetics in molecular dynamics simulations. J. Chem. Theory Comput. 2013, 9, (7), 2887-2899. Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, (19), 8577-8593. Miyamoto, S.; Kollman, P. A. SETTLE: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 1992, 13, (8), 952962. Efron, B., Bootstrap Methods: Another Look at the Jackknife. In Breakthroughs in Statistics, Springer: 1992; pp 569-593.
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Marrink, S.-J.; Berendsen, H. J. Simulation of water transport through a lipid membrane. J. Phys. Chem. 1994, 98, (15), 4155-4168. Woolf, T. B.; Roux, B. Conformational flexibility of O-phosphorylcholine and Ophosphorylethanolamine. J. Am. Chem. Soc. 1994, 116, (13), 5916-5926. Hummer, G. Position-dependent diffusion coefficients and free energies from Bayesian analysis of equilibrium and replica molecular dynamics simulations. New Journal of Physics 2005, 7, (1), 34. Callenberg, K. M.; Choudhary, O. P.; Gabriel, L.; Gohara, D. W.; Baker, N. A.; Grabe, M. APBSmem: A graphical interface for electrostatic calculations at the membrane. PLoS One 2010, 5, (9), e12722. Sengupta, D.; Meinhold, L.; Langosch, D.; Ullmann, G. M.; Smith, J. C. Understanding the energetics of helical peptide orientation in membranes. Prot. Struct., Func., Bioinf. 2005, 58, (4), 913-922. Sitkoff, D.; Sharp, K. A.; Honig, B. Accurate calculation of hydration freeenergies using macroscopic solvent models. J. Phys. Chem. 1994, 98, (7), 19781988. Tang, C. L.; Alexov, E.; Pyle, A. M.; Honig, B. Calculation of pK a s in RNA: On the structural origins and functional roles of protonated nucleotides. J. Mol. Biol. 2007, 366, (5), 1475-1496. Bondi, A. van der Waals volumes and radii. J. Phys. Chem. 1964, 68, (3), 441451. Nagle, J. F.; Tristram-Nagle, S. Structure of lipid bilayers. BBA-Rev. Biomembranes 2000, 1469, (3), 159-195. Sanner, M. F.; Olson, A. J.; Spehner, J. C. Reduced surface: an efficient way to compute molecular surfaces. Biopolymers 1996, 38, (3), 305-320. Sengupta, D.; Smith, J. C.; Ullmann, G. M. Partitioning of amino-acid analogues in a five-slab membrane model. BBA-Biomembranes 2008, 1778, (10), 2234-2243. Mirjalili, V.; Feig, M. Interactions of Amino Acid Side-Chain Analogs within Membrane Environments. J. Chem. Phys. B 2015, 119, (7), 2877-2885. Bemporad, D.; Essex, J. W.; Luttmann, C. Permeation of small molecules through a lipid bilayer: A computer simulation study. J. Chem. Phys. B 2004, 108, (15), 4875-4884. Orsi, M.; Sanderson, W. E.; Essex, J. W. Permeability of small molecules through a lipid bilayer: A multiscale simulation study. J. Chem. Phys. B 2009, 113, (35), 12019-12029. Marrink, S. J.; Berendsen, H. J. Permeation process of small molecules across lipid membranes studied by molecular dynamics simulations. J. Phys. Chem. 1996, 100, (41), 16729-16738. Dickson, C. J.; Hornak, V.; Pearlstein, R. A.; Duca, J. S. Structure-kinetic relationships of passive membrane permeation from multiscale modeling. J. Am. Chem. Soc. 2016. Chakrabarti, A. C.; Deamer, D. W. Permeability of lipid bilayers to amino acids and phosphate. BBA-Biomembranes 1992, 1111, (2), 171-177. Chakrabarti, A. C.; Clark-Lewis, I.; Harrigan, P. R.; Cullis, P. R. Uptake of basic amino acids and peptides into liposomes in response to transmembrane pH gradients. Biophys. J. 1992, 61, (1), 228-234.
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84.
Radzicka, A.; Wolfenden, R. Comparing the polarities of the amino acids: Sidechain distribution coefficients between the vapor phase, cyclohexane, 1-octanol, and neutral aqueous solution. Biochemistry 1988, 27, (5), 1664-1670. Terreno, E.; Sanino, A.; Carrera, C.; Castelli, D. D.; Giovenzana, G. B.; Lombardi, A.; Mazzon, R.; Milone, L.; Visigalli, M.; Aime, S. Determination of water permeability of paramagnetic liposomes of interest in MRI field. J. Inorg. Biochem. 2008, 102, (5), 1112-1119. Chakrabarti, A. C.; Deamer, D. W. Permeation of membranes by the neutral form of amino acids and peptides: relevance to the origin of peptide translocation. J. Mol. Evol. 1994, 39, (1), 1-5. Jalilehvand, F.; Leung, B. O.; Izadifard, M.; Damian, E. Mercury (II) cysteine complexes in alkaline aqueous solution. Inorg. Chem. 2006, 45, (1), 66-73. Foti, C.; Giuffre, O.; Lando, G.; Sammartano, S. Interaction of inorganic mercury (II) with polyamines, polycarboxylates, and amino acids. J. Chem. Eng. Data 2009, 54, (3), 893-903.
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