Nuo Wang1, Shenggao Zhou1, Peter M Kekenes-Huskey1, Bo Li1, J Andrew McCammon1. 1. Department of Chemistry and Biochemistry, ‡Department of Mathematics, §Department of Pharmacology, ⊥Howard Hughes Medical Institute, University of California-San Diego , La Jolla, California 92093, United States.
Abstract
Mean-field methods, such as the Poisson-Boltzmann equation (PBE), are often used to calculate the electrostatic properties of molecular systems. In the past two decades, an enhancement of the PBE, the size-modified Poisson-Boltzmann equation (SMPBE), has been reported. Here, the PBE and the SMPBE are reevaluated for realistic molecular systems, namely, lipid bilayers, under eight different sets of input parameters. The SMPBE appears to reproduce the molecular dynamics simulation results better than the PBE only under specific parameter sets, but in general, it performs no better than the Stern layer correction of the PBE. These results emphasize the need for careful discussions of the accuracy of mean-field calculations on realistic systems with respect to the choice of parameters and call for reconsideration of the cost-efficiency and the significance of the current SMPBE formulation.
Mean-field methods, such as the Poisson-Boltzmann equation (PBE), are often used to calculate the electrostatic properties of molecular systems. In the past two decades, an enhancement of the PBE, the size-modified Poisson-Boltzmann equation (SMPBE), has been reported. Here, the PBE and the SMPBE are reevaluated for realistic molecular systems, namely, lipid bilayers, under eight different sets of input parameters. The SMPBE appears to reproduce the molecular dynamics simulation results better than the PBE only under specific parameter sets, but in general, it performs no better than the Stern layer correction of the PBE. These results emphasize the need for careful discussions of the accuracy of mean-field calculations on realistic systems with respect to the choice of parameters and call for reconsideration of the cost-efficiency and the significance of the current SMPBE formulation.
Mean-field
methods provide a way to coarse grain the electrostatic
interactions between the solvent and the biomolecules of interest.[1] They are commonly used to generate the electrostatic
potentials needed for biomolecular diffusion simulations[2−5] and are used in calculations of the solvation free energies of biomolecules.[6,7]The hundred-year-old Poisson–Boltzmann equation (PBE)
is
currently the flagship of the mean-field methods.[8−10] Despite the
success of the PBE,[11,12] it is built upon several approximations;
for example, the ions in the PBE are considered infinitesimally small.
This approximation runs into trouble near highly charged biomolecular
surfaces, such as those of typical enzyme active sites.[13] The reason is that, under the strong electrostatic
attractions near highly charged surfaces, the concentration of the
counterion can exceed its maximally allowed value without the restriction
from steric hindrance; this will then lead to an overestimation of
electrostatic screening and an underestimation of the electrostatic
potential exerted by the biomolecule.[14,15] To remedy
the lack of ion steric hindrance, or ion-size effects, in the PBE,
a Stern layer, or ion-exclusion layer, outside of the molecular surface
has been used, within which no ion is allowed.[16]To improve the PBE, the size-modified Poisson–Boltzmann
equation (SMPBE), which incorporates the finite ion sizes through
a more physical lattice gas formulation, was developed and has been
applied to biomolecular systems.[14,17−20] The computational cost of the SMPBE is comparable to that of the
PBE when there are only two ion sizes.[18] However, in its generalized form that can handle an arbitrary number
of different ion sizes, the SMPBE takes much longer to solve.[19,21]It is known that mean-field methods are parameter-dependent
and
that there is no standard way of choosing input parameters that guarantee
the optimum accuracy of prediction.[22−25] The SMPBE is reported to reproduce
experimental[18] and molecular dynamics simulation[20] results better than the PBE, but there still
lacks a comprehensive comparison between these two mean-field methods
using a variety of input parameters to determine whether the SMPBE
generally outperforms the PBE.This paper discusses the accuracy
of prediction and the cost-efficiency
of the SMPBE with respect to the PBE using several different parameter
sets. Given that the mean-field methods are parameter-dependent, this
work also discusses the existence of a single parameter set with which
the mean-field methods give consistent high accuracy of prediction
across different ionic strengths and surface charge densities. Specifically,
the equilibrium ion distributions outside of neutral and charged lipid
bilayers are calculated by the PBE and the SMPBE using eight different
parameter sets and are compared to the molecular dynamics (MD) simulation
results. The long-term goal of this work is to identify appropriate
tools for modeling diffusion dynamics of molecules that are of biological
or medical interest.[26,27] In these diffusion processes,
the system evolves from a nonequilibrium state to the equilibrium
state, and this work is an initial step to test the ability of the
SMPBE in predicting ion distributions in the equilibrium state. Lipid
bilayers are considered here because of their profound importance
in the activity of membrane proteins and cross-membrane signaling
pathways.[28,29]
Methods
The SMPBE
Implementation
The SMPBE
formulation used here is capable of calculating the electrostatic
potential and the ion distributions for molecular systems containing
an arbitrary number of ion species with different ion sizes.[19,21] A brief description of the formulation is included in the Supporting Information (SI) section I. This formulation
is an extension of the earlier foundational SMPBE theory by Borukhov
et al.,[14] which, in its original form,
can only handle one same-size cation–anion pair.This
SMPBE formulation is implemented in the Adaptive Poisson–Boltzmann
Solver (APBS)[30] and is freely available
upon request. A brief explanation of the SMPBE routine in APBS is
included in section II of the SI.
Numerical Calculations
Molecular
dynamics (MD) simulations can generate the equilibrium ion distributions
in a molecular system at higher accuracy and resolution than the mean-field
methods but also at significantly greater computational expense.[1] In this work, the MD simulation results are used
as a standard to assess the accuracy of the PBE and the SMPBE predictions.
Molecular Dynamics Simulations
The dimensions of the
lipid bilayer systems used in the MD simulations
and the mean-field calculations are illustrated in Figure S1 of the SI. In this work, eight lipid bilayer MD systems
are simulated (Table 1).
Table 1
Composition of the Molecular Dynamics
Systemsa
system
lipid
cation
anion
bulk salt concentration
1
48 POPC
10 Na+
10 Cl–
0.060 M
2
48 POPC
20 Na+
20 Cl–
0.126 M
3
48 POPC
40 Na+
40 Cl–
0.268 M
4
48 POPC
10 K+
10 Cl–
0.060 M
5
48 POPC
20 K+
20 Cl–
0.134 M
6
48 POPC
40 K+
40 Cl–
0.275 M
7
48 POPS
68 Na+
20 Cl–
0.170 M
8
48 POPS
68 K+
20 Cl–
0.169 M
POPC, 1-palmitoyl-2-oleoylphosphatidylcholine;
POPS, 1-palmitoyl-2-oleoylphosphatidylserine. The bulk salt concentration
is the averaged salt (NaCl, KCl) concentration at |z| = 60 Å (z, the perpendicular distance to
the bilayer center, is defined in Figure S1 of the SI) over the converged portion of the MD trajectories.
POPC, 1-palmitoyl-2-oleoylphosphatidylcholine;
POPS, 1-palmitoyl-2-oleoylphosphatidylserine. The bulk salt concentration
is the averaged salt (NaCl, KCl) concentration at |z| = 60 Å (z, the perpendicular distance to
the bilayer center, is defined in Figure S1 of the SI) over the converged portion of the MD trajectories.1-Palmitoyl-2-oleoylphosphatidylcholine
(POPC) is a partially charged,
but overall neutral, lipid molecule, and each 1-palmitoyl-2-oleoylphosphatidylserine
(POPS) lipid molecule carries one negative charge. All of the systems
in Table 1 are built by the online application
CHARMM-GUI Membrane Builder.[31] The MD simulations
are performed by NAMD2.9 software package[32] with CHARMM36[33] lipid force field[34] using mostly the default system settings offered
by the CHARMM-GUI Membrane Builder (see section III of the SI). Each system in Table 1 is simulated for 100–150 ns. All of the POPC simulations
are 150 ns long and reach convergence after 20 ns. All of the POPS
simulations are 100 ns long and converge after 30 ns. The converged
MD trajectories are utilized for all of the data analysis (see section
III of the SI for the convergence criteria).
Mean-Field Calculations
To test
the accuracy of a mean-field method in predicting the ion distributions
in an MD system listed in Table 1, three MD
frames are evenly sampled from the converged MD trajectory, and a
mean-field calculation is performed for each frame; the final mean-field
results are averaged over all three calculations. Note that APBS has
no periodic boundary conditions in its finite difference routine.
To avoid boundary effects, the lipid bilayer structures obtained from
the MD simulations are manually extended 0.25 times along the positive
and negative x,y-axis, while the
mean-field results are only collected above and below the original
size of the lipid bilayer as in the MD simulations, see Figure S1
of the SI. A 161(x) by
161(y) by 193(z) finite difference
grid is used for all of the mean-field calculations following the
APBS electrostatic focusing scheme,[30] and
the grid spacing is 0.43 Å(x) by 0.43 Å(y) by 0.47 Å(z), a reasonable resolution
for calculating ion distributions. The lipid atomic charges and radii
are taken from CHARMM36 charges and CHARMM36 van der Waals radii.
Both the PBE and the SMPBE are solved in their nonlinear form. The
majority of the input parameters for the mean-field methods follow
the default settings of the APBS input preparation program PDB2PQR[35] except for the ones mentioned in the next section.
See section IV of the SI for an example
of the APBS input file. Data analysis in this work utilizes GROMACS4.5.5,[36] VMD1.9.1,[37] and customized
scripts.
Explanations on the Eight
Mean-Field Parameter
Sets
The use of different mean-field method input parameters
is key
to the discussion of the accuracy of predictions of the SMPBE with
respect to those of the PBE. Among all of the mean-field calculation
input parameters or settings, two are selected for the discussion.
First, the ion size is chosen mainly because it is a particularly
important input parameter for the SMPBE, and it is also used to define
the size of the Stern layer. Second, the definition of molecular surface
is chosen for its direct relationship to ion accessibility, thus the
ion distributions. Specifically, four definitions of molecular surface,
Figure 1, and two sets of ion sizes, Table 2, are tested, yielding a permutation of eight different
sets of input parameters.
Figure 1
Definitions of molecular surface. VDWS: the
van der Waals surface,
drawn around the lipid atom centers with the CHARMM36 VDW radii, i.e.,
the Lennard-Jones rmin/2 values. FPS:
the first-peak surface, drawn according to the MD cation–lipid
carbonyl oxygen radial distribution function (RDF) first-peak positions.
SAS: the solvent-accessible surface, drawn by the center of the water
probe of radius 1.4 Å[38] rolling across
VDWS. IAS: the ion-accessible surface, drawn in the same way as the
SAS, only that the probe radius is that of the counterion. The dielectric
boundaries in all of the mean-field calculations are the same and
are defined by the solid light-blue line (or equivalently the solvent-exclusion
surface).
Table 2
Two Sets of Ion Radiia
VDW radius
RDF radius
Na+
1.4 Å
2.3 Å
K+
1.8 Å
2.6 Å
Cl–
2.3 Å
4.0 Å
The van der Waals (VDW) radii
are taken from the CHARMM36 ion VDW radii. The radial distribution
function (RDF) radii are the first-peak positions of the ion–lipid
carbonyl oxygen RDFs from the MD simulations.
Definitions of molecular surface. VDWS: the
van der Waals surface,
drawn around the lipid atom centers with the CHARMM36 VDW radii, i.e.,
the Lennard-Jones rmin/2 values. FPS:
the first-peak surface, drawn according to the MD cation–lipid
carbonyl oxygen radial distribution function (RDF) first-peak positions.
SAS: the solvent-accessible surface, drawn by the center of the water
probe of radius 1.4 Å[38] rolling across
VDWS. IAS: the ion-accessible surface, drawn in the same way as the
SAS, only that the probe radius is that of the counterion. The dielectric
boundaries in all of the mean-field calculations are the same and
are defined by the solid light-blue line (or equivalently the solvent-exclusion
surface).The van der Waals (VDW) radii
are taken from the CHARMM36 ion VDW radii. The radial distribution
function (RDF) radii are the first-peak positions of the ion–lipid
carbonyl oxygen RDFs from the MD simulations.Three key points need to be
clarified. First, despite the multiple
definitions of the molecular surface in Figure 1, only the van der Waals surface (VDWS) is commonly recognized as
the molecular surface. Second, the reason for the flexible use of
the term molecular surface in this work is as follows. Supposedly,
the SMPBE, without the help of the Stern layer, is able to predict
solvation properties comparably accurately or more accurately than
the PBE with a Stern layer. However, as it is shown later in the Results section, unexpectedly, using the SMPBE outside
of the VDWS without a Stern layer leads to large ion concentration
overestimation. To carry out further investigations, several alternative
molecular surfaces that are further away from the lipid molecules
than the VDWS are tested. Finally, the seemingly peculiar use of the
first-peak surface (FPS) is inspired by the fact that the MD radial
distribution function (RDF) first-peak positions are often smaller
than the sum of the VDW radii of the cation and its association partner,
the lipid carbonyl oxygen.[39] FPS is generated
by uniformly expanding the VDWS until the minimum FPS–lipid
carbonyl oxygen center distance is equal to the counterion–lipid
carbonyl oxygen MD RDF first-peak position. The VDWS is expanded by
0.6 Å for the Na+ FPS and 0.9 Å for the K+ FPS. Using the FPS in the mean-field methods could theoretically
reproduce the MD results better. However, this turns out not to be
the case as shown in the Results section.
The RDF radius is chosen because it was used in a previous work,[20] and an alternative set of ion radii is needed
to show how the PBE and the SMPBE results change with respect to the
change of ion sizes.
Results and Discussion
The PBE vs the SMPBE
Figure 2 shows an array of calculations on the number of
Na+ ions bound per POPC molecule. The corresponding results
for the K+ ion are shown in the SI (Figure S2). The number of ions bound is calculated by integrating
the concentration of ions within 25 Å to the lipid bilayer center.
An example of the detailed position-dependent ion concentration is
shown in the SI (Figure S3).
Figure 2
Number of Na+ bound per POPC molecule at three NaCl
bulk concentrations (systems 1–3 in Table 1). MD: molecular dynamics; PBE: nonlinear Poisson–Boltzmann
equation (without Stern layer); PBES: PBE with Stern layer; SMPBE:
size-modified Poisson–Boltzmann equation (without Stern layer).
Subfigures (a)–(h) use eight different parameter sets; each
is a combination of a molecular surface (VDWS, FPS, SAS, IAS, see
Figure 1) and an ion radius set (VDW radius,
RDF radius, see Table 2). (g) turns out to
be identical to (e) because the Na+ VDW radius happens
to be the same as the radius of water, 1.4 Å, and so the SAS
in (e) is exactly the same as the IAS in (g).
Number of Na+ bound per POPC molecule at three NaCl
bulk concentrations (systems 1–3 in Table 1). MD: molecular dynamics; PBE: nonlinear Poisson–Boltzmann
equation (without Stern layer); PBES: PBE with Stern layer; SMPBE:
size-modified Poisson–Boltzmann equation (without Stern layer).
Subfigures (a)–(h) use eight different parameter sets; each
is a combination of a molecular surface (VDWS, FPS, SAS, IAS, see
Figure 1) and an ion radius set (VDW radius,
RDF radius, see Table 2). (g) turns out to
be identical to (e) because the Na+ VDW radius happens
to be the same as the radius of water, 1.4 Å, and so the SAS
in (e) is exactly the same as the IAS in (g).By a crude observation, the PBE and the SMPBE reproduce MD
results
the best in Figure 2g, while the PBES (the
PBE with Stern layer; the size of the Stern layer is always set to
be the radius of the cation) performs the best in Figure 2a. However, the PBES calculation in Figure 2a is actually the same as the PBE calculation in
Figure 2g. This is because the ion-accessible
surface (IAS) used in Figure 2g exactly overlaps
with the outer surface of the Stern layer in Figure 2a. Also, it should be reemphasized that the VDWS is the physical
molecular surface, and it is only fair to compare the SMPBE results
without the use of a Stern layer to the PBE results with the use of
a Stern layer. With this said, it can be concluded from Figure 2a and b that when the SMPBE is used outside of the
physical molecular surface without a Stern layer, it significantly
overestimates the MD ion concentrations compared to the PBE calculation
with the aid of a Stern layer; the size-modified correction of the
PBE does not seem to be as effective as the Stern-layer-modified correction.
This is a surprising result considering the fact that the POPC bilayer
has zero net surface charge and yet the capability of the SMPBE is
not enough to limit the ion concentrations to the correct range. Furthermore,
caution should be taken to distinguish the real source of the corrections
or errors. Given the poor performance of the SMPBE in Figure 2a, it is clear that the reproduction of the MD results
by the SMPBE in Figure 2g is mainly due to
the introduction of an effective Stern layer through the use of the
IAS instead of the SMPBE formulation itself. The reason for the underestimation
of the MD results by the PBES in Figure 2g
is the redundant use of a second Stern layer on top of the IAS.Fortunately, as expected, at least the use of the SMPBE consistently
alleviates the overestimation of ion concentrations by the PBE. The
strength of SMPBE correction increases as the molecular surface shrinks
(IAS to VDWS), which causes ions to experience a stronger electrostatic
potential, and as the ion radii increase (VDW radius to RDF radius),
which results in more steric exclusion between ions in the SMPBE formulation.
However, in general, the SMPBE gives similar results to the PBE (Figure 2a, c, e, f, g, and h). A previous study has shown
that the electrostatic free energies predicted by the PBE and the
SMPBE can be quite different,[40] but in
this work, the PBE and the SMPBE electrostatic potentials are shown
to also be very similar under most parameter sets, see Figure S4 of
the SI. As mentioned in the Introduction, the SMPBE has a comparable computational cost
to the PBE when two ion sizes are used,[18] but the cost significantly increases when the SMPBE is generalized
to handle an arbitrary number of different ion sizes because the SMPBE
can no longer be expressed analytically.[19,21] If the simple Newton’s method is used to solve the generalized
SMPBE, then the SMPBE is up to hundreds of times slower than the PBE.
Without developing a more efficient SMPBE solver, only in the two-ion-size
case is SMPBE a cost-efficient choice for its degree of correction
on the PBE. However, even with a faster SMPBE solver, the Stern layer
remains a more effective correction than the current SMPBE formulation
at no extra computational cost.
Existence
of the Best Parameter Set
From Figure 2, it can be said that the VDWS
combined with a Stern layer of the size of the VDW radius of the cation
is the parameter set that gives the best PBE prediction of ion distributions.
However, Figure 3 shows that this does not
hold true for a different lipid bilayer surface charge density (the
corresponding figure for the K+ ion is Figure S5 of the SI).
Figure 3
Number of Na+ bound per lipid as
a function of the effective
Stern layer thickness, the minimum distance between the VDWS and the
molecular surface used for each calculation. (a) NaCl of bulk concentration
0.13 M outside of the POPC bilayer, system 2 in Table 1. (b) NaCl of bulk concentration 0.17 M outside of the POPS
bilayer, system 7 in Table 1; VDW R and RDF
R are shorthand for VDW radius and RDF radius. Gray shades mark where
the mean-field calculations using the VDW radius agree with MD.
Number of Na+ bound per lipid as
a function of the effective
Stern layer thickness, the minimum distance between the VDWS and the
molecular surface used for each calculation. (a) NaCl of bulk concentration
0.13 M outside of the POPC bilayer, system 2 in Table 1. (b) NaCl of bulk concentration 0.17 M outside of the POPS
bilayer, system 7 in Table 1; VDW R and RDF
R are shorthand for VDW radius and RDF radius. Gray shades mark where
the mean-field calculations using the VDW radius agree with MD.The surface charge density of
the simulated POPS bilayer is about
0.28 C/m2 or 1.73 e/nm2 (for
reference, the surface charge density of DNA is about 1 e/nm2), a fairly high value. The gray shade positions in
Figure 3a and b show that the thickness of
the Stern layer for the POPS bilayer needs to be smaller than what
is used for the POPC bilayer (the VDW radius of Na+) for
the PBE to reproduce the MD ion concentrations. Figure 3 reveals that the same parameter set does not give consistent
accuracy of prediction when the surface charge density changes. This
is also true, although not as significantly, when the bulk ionic strength,
or the bulk ion concentration, varies. When looked at closely, the
slopes of the PBE, the PBES, and the SMPBE plots in Figure 2 are always equal to or smaller than that of the
MD, leading to inconsistent performances of a parameter set across
different ionic strengths. Additionally, the same parameter set does
not give the same performance for different ions. For example, for
K+, the IAS with VDW radius does not perform as well as
it does for Na+, that is, the gray shade in Figure S5 of
the SI is not exactly overlapping with
the IAS + VDW radius data point like it does in Figure 3. The inconsistency of the performance of a parameter set
is partly because mean-field methods are much more simplified than
the MD simulations and do not describe several ionic effects such
as ion–ion correlations and fluctuations;[7] they also ignore the ion–lipid specific interactions
modeled in the MD force fields.[41,42]The parameter
inconsistency can be circumvented if empirical guidelines
are available on how to choose parameters on the basis of the characteristics
of a system. For example, one can summarize a relationship between
the surface charge density and the effective Stern layer thickness.
Two data points in this relationship are generated here: at zero surface
charge density, the VDW radius can be used as the size of the Stern
layer, and at surface charge density 1.73 e/nm2, roughly 0.8 Å for Na+ and 0.6 Å for
K+ should be used as the size of the Stern layer. More
data points need to be generated for a more complete knowledge of
this relationship, and the lipid-type specific ion absorption effects
should be considered.Last but not least, even though theoretically
the PBE and the SMPBE
become more distinguishable under a stronger electrostatic potential,
the strong electrostatic potential exerted by the higher surface charge
density of the POPS bilayer is not yet enough to significantly separate
the PBE and the SMPBE results when the common VDW ion radii are used,
Figure 3b, putting the capability of the SMPBE
under further doubt.
Conclusion
First,
when it comes to studying ion equilibrium distributions,
the SMPBE only offers a slight advantage over the PBE for the systems
considered here. It is neither faster nor more accurate than the simple
Stern layer ion-size correction of the PBE. There may, however, be
other physical regimes where the SMPBE has a strong advantage. Further
improvements are needed for the SMPBE to be an impactful correction
to the PBE. Second, no single parameter set gives consistent mean-field
method accuracy of prediction across systems of varying surface charge
densities and ionic strengths. Empirical rules may be able to be summarized
to guide the choice of parameters of the mean-field calculations.
Authors: K Vanommeslaeghe; E Hatcher; C Acharya; S Kundu; S Zhong; J Shim; E Darian; O Guvench; P Lopes; I Vorobyov; A D Mackerell Journal: J Comput Chem Date: 2010-03 Impact factor: 3.376