Thermostat Artifacts in Replica Exchange Molecular
Dynamics Simulations
Edina Rosta,†
Nicolae-Viorel Buchete,‡
and Gerhard Hummer*,†
Laboratory of Chemical Physics, National Institute of Diabetes and DigestiVe and
Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-0520, and
School of Physics, UniVersity College Dublin, Belfield, Dublin 4, Ireland
Received December 15, 2008
Abstract: We explore the effects of thermostats in replica exchange molecular dynamics
(REMD) simulations. For thermostats that do not produce a canonical ensemble, REMD
simulations are found to distort the configuration-space distributions. For bulk water, we find
small deviations of the average potential energies, the buildup of tails in the potential energy
distributions, and artificial correlations between the energies at different temperatures. If a solute
is present, as in protein folding simulations, its conformational equilibrium can be altered. In
REMD simulations of a helix-forming peptide with a weak-coupling (Berendsen) thermostat, we
find that the folded state is overpopulated by about 10% at low temperatures, and underpopulated
at high temperatures. As a consequence, the enthalpy of folding deviates by almost 3 kcal/mol
from the correct value. The reason for this population shift is that noncanonical ensembles with
narrowed potential energy fluctuations artificially bias toward replica exchanges between lowenergy
folded structures at the high temperature and high-energy unfolded structures at the
low temperature. We conclude that REMD simulations should only be performed in conjunction
with thermostats that produce a canonical ensemble.
1. Introduction
Replica exchange molecular dynamics (REMD)1,2
is a widely
used method to enhance the conformational sampling of
molecular dynamics (MD) simulations.3
In typical REMD
simulations, several “replicas” i (i.e., copies of a physical
system) are simulated in parallel at different temperatures
Ti. At regular intervals, attempts are made to exchange the
structures of different replicas to increase the conformational
sampling efficiency at the lower temperatures.1
The exchange
of a structure X of a replica at temperature T1 with a structure
Y of a replica at temperature T2 is accepted with probability
pacc
(XY f YX) ) min{1, exp(∆β∆U)}, where ∆U ) U(Y)
- U(X) is the potential energy difference, and ∆β ) β2 β1
with βi
-1
) kBTi and kB being Boltzmann’s constant. This
acceptance criterion is designed to maintain canonical
probability distributions in configuration space, pi(X) ∝
e-βiU(X)
, at each temperature Ti, as follows from the detailed
balance relation
pacc
(XY f YX)
pacc
(YX f XY)
)
p1(Y)p2(X)
p1(X)p2(Y)
) e∆β∆U
(1)
After an accepted exchange, particle velocities can be
reassigned from a Maxwell-Boltzmann distribution at the
new temperatures T1 and T2, respectively. Alternatively, the
old velocities can be scaled by factors (T1/T2)1/2
and (T2/
T1)1/2
, respectively.1,2
Here, we explore the effects of combining REMD with
thermostats that do not produce canonical ensembles. The
weak-coupling (W-C) thermostat4
(often referred to as
“Berendsen” thermostat) is widely used in biomolecular
simulations because of its stability and efficiency, but
produces a noncanonical phase-space distribution.5-8
As a
consequence, the detailed balance relation, eq 1, is not
satisfied. Problems with ergodicity in W-C simulations
combined with REMD were identified previously in an
* To whom correspondence should be addressed. E-mail:
Gerhard.Hummer@nih.gov.
†
National Institutes of Health.
‡
University College Dublin.
J. Chem. Theory Comput. 2009, 5, 1393–1399 1393
10.1021/ct800557h CCC: $40.75  2009 American Chemical Society
Published on Web 04/09/2009
insightful study by Cooke and Schmidler.9
We show that
for bulk water at near-ambient conditions the effects of using
a W-C thermostat in REMD simulations are relatively small.
In contrast, the folding/unfolding equilibrium of a small
peptide in water is shifted significantly by the thermostat,
resulting in an overpopulation of the folded state at low
temperatures and an underpopulation at high temperatures.
The reason for this population shift is that the narrowed
potential energy distributions in the noncanonical ensembles
artificially favor replica exchanges between low-energy
folded structures of the high-temperature replica and highenergy
unfolded structures of the low-temperature replica.
We discuss possible ways to address the artificial population
shift in REMD simulations with W-C thermostats. In
principle, the acceptance criterion could be adjusted to
maintain the energy distributions created by the thermostat,
as implemented previously for microcanonical dynamics.10
In practice, prior knowledge about the energy distributions,
including their tails, is normally not available. We thus
conclude that REMD simulations with a standard acceptance
criterion, eq 1, should only be performed in conjunction with
thermostats that preserve a canonical ensemble, such as
Langevin thermostats,11
Andersen thermostats,12
Nose´-Hoover
chains13,14
based on the Nose´-Hoover thermostat,15,16
or
hybrid Monte Carlo.17
2. Methods
2.1. Water Simulations. MD simulations are widely used
to study water and aqueous solutions. Even for relatively
large solutes such as proteins, the main contribution to the
heat capacity of the system, and thus to the potential energy
fluctuations, typically comes from the water solvent. To
explore the effects of combining REMD with thermostats,
we thus first study bulk TIP3P water18
at near-ambient
conditions with a particle density of F ) 33.0 nm-3
and at
temperatures of T ) 300 and 310 K. We compare the results
of regular equilibrium MD and REMD simulations using a
Langevin thermostat11
with a collision frequency of 2 ps-1
,
W-C thermostats4
with coupling constants of 0.5 and 5 ps,
and simulations run at constant energy (NVE ensemble; only
regular MD). In all simulations, the system contains 1024
water molecules in a cubic, periodically replicated box of
constant volume. Both regular MD and REMD simulations
are performed using the sander module of Amber 919
with
a time step of 0.002 ps, particle-mesh Ewald (PME)
summation20
with a grid width <1 Å, a real-space cutoff of
10 Å, and production times of 5 ns for each combination of
thermostat and temperature, including the REMD runs. Two
replicas are used in the REMD runs at temperatures of 300
and 310 K, respectively, with replica exchange attempts
every 1 ps. In the NVE runs, the average kinetic energies
correspond to temperatures of 299.9 and 310.1 K, respectively.
2.2. Protein Folding. To quantify the thermostat effects
on the folding/unfolding equilibrium of a protein, we
performed simulations of a helix-forming peptide Ala5 in
water.3,21
In these simulations, the GROMACS molecular
dynamics package22
was used to run both standard MD
(GROMACS version 3.3.0) and REMD simulations (GROMACS
version 3.3.1) of the folding of blocked Ala5(CH3COAla5-NHCH3)
in TIP3P water.18
For the peptide, we used
the AMBER-GSS force field23
ported to GROMACS.24
The
simulations were performed with periodic boundary conditions
and PME electrostatics20
using a real-space cutoff
distance of 10 Å and a grid width <1 Å. REMD simulations
were performed both with a W-C thermostat (coupling
constant of 1 ps)4
and a Langevin thermostat (collision
frequency of 1 ps-1
).22
Standard MD simulations were
performed with a W-C thermostat. The pressure was held
constant at p ) 1 bar using a W-C barostat with a coupling
constant of 5 ps.4
The replica-exchange acceptance criterion
was adjusted for simulations in an NPT ensemble by
replacing the energy difference ∆U in eq 1 with the enthalpy
difference ∆H, where H ) U + pV with V the fluctuating
system volume. Since the compressibility of water is low
near ambient conditions and pressure has little effect on the
helix-coil equilibrium,25
we expect that any deviations
between MD and REMD simulations are caused primarily
by the use of a W-C thermostat in the REMD runs and to
a lesser degree by using a barostat that produces nonBoltzmann
enthalpy distributions. A time step of 2 fs was
used in conjunction with constrained bonds of hydrogen
atoms.26
The simulation box contained 1050 TIP3P water
molecules.18
Four independent trajectories starting from
different initial conformations were created for each of the
three setups (MD/W-C, REMD/W-C, and REMD/Langevin,
respectively). The standard MD runs21
were performed
for 4 × 250 ns at 300 and 350 K, and 4 × 200 ns at 310,
325, and 340 K. REMD simulations of 150 ns duration (per
replica) were run for each thermostat and each of the four
initial conditions. We used 12 replicas spanning the 295-350
K temperature range, for a combined simulation time of 4
× 150 ns at each temperature.3
Coordinates were saved every
1 ps and REMD exchanges were attempted every 5 ps.
3. Results and Discussion
3.1. Bulk Water. MD. Figure 1 shows the potential
energy distributions for bulk TIP3P water obtained from
regular MD with W-C and Langevin thermostats, and in
Figure 1. Potential energy distributions for simulations of
TIP3P water at ambient conditions using Langevin (green
circles) and W-C thermostats4
with coupling constants of 0.5
ps (black triangles) and 5 ps (blue crosses), and in an NVE
simulation (red dots). The continuous lines show Gaussians
of corresponding means and variances. The inset shows the
same distributions on a semilogarithmic scale.
1394 J. Chem. Theory Comput., Vol. 5, No. 5, 2009 Rosta et al.
the NVE ensemble. We find that at a given temperature all
distributions are well approximated by Gaussians of nearly
identical means. However, the widths of the distributions
vary considerably, with the W-C and NVE simulations
producing narrower distributions than the Langevin simulations,
consistent with results from earlier studies.5-7
As
shown in Table 1, the variances in the potential energy
distributions differ by up to a factor of 3.
To test whether the potential energy distributions are
consistent with a canonical distribution, we compare the
excess heat capacity at constant volume calculated (1) from
the temperature derivative of the average potential energy,
CV ) ∂〈U〉/∂T, and (2) from the fluctuations in the potential
energy, CV
fluc
) σ2
/(kBT2
) where σ2
)〈U2
〉 - 〈U〉2
is the
variance in the potential energy. For a canonical ensemble,
the two expressions are identical, CV ≡ CV
fluc
. However, we
will show in the following that in the noncanonical ensembles
created by W-C thermostats, CV
fluc
differs from CV because
of the narrowed potential energy distributions. To estimate
the temperature derivative in the expression for CV, we use
the difference between the average potential energies at 310
and 300 K, CV ≈(〈U〉2 -〈U〉1)/(T2 - T1). The variance in
CV
fluc
is averaged over the two temperatures, σ2
≈(σ1
2
+ σ2
2
)/
2. As listed in Table 1, we find that the ratio of the two
expressions for the excess heat capacity, g ) CV/CV
fluc
, is 1.0
for the Langevin simulations, consistent with canonical
distributions. In contrast, for the W-C and NVE simulations
g varies between 2.4 and 2.9, indicative of strong deviations
from the canonical distribution.
REMD. Figure 2 compares the potential energy distributions
of bulk water at 300 and 310 K obtained from MD
and REMD simulations using W-C thermostats with a
coupling constant of 5 ps. The results for different thermostats
are summarized in Table 1. We find that in REMD with
a Langevin thermostat, the distributions do not change
compared to those from MD simulations. In contrast, REMD
significantly changes the potential energy distributions when
a W-C thermostat is used: the mean energies at the two
temperatures move together, the variances increase, and the
distributions become skewed with pronounced non-Gaussian
tails. Table 1 also lists the normalized cross-correlation
coefficient C12 )〈(U1 -〈U1〉)(U2 -〈U2〉)〉/(σ1σ2) between the
instantaneous potential energies of the two replicas. We find
that in the REMD simulations with a Langevin thermostat,
the energies are uncorrelated (C12 ) 0). In REMD with a
W-C thermostat, we find small but significant correlations,
C12 ≈ -0.0050 ( 0.0004. We conclude from these results
that REMD with a W-C thermostat can significantly alter
the potential energy distributions.
Model of Thermostat Effects in REMD. The effects of
thermostats in conjunction with REMD can be understood
from a simple model, similar to the ones used previously27,28
to study the efficiency of REMD. With pi
MD
(U) the potential
energy distribution at temperature Ti, the joint probability
distribution of the potential energies U and W at the two
temperatures T1 and T2 is pMD
(U,W) ) p1
MD
(U)p2
MD
(W). Let
us consider how replica exchange alters this distribution.
After an attempted replica exchange, a given pair of energies
U and W is obtained either from an accepted move starting
with W and U or from a rejected move starting with U and
W. Replica exchange thus transforms pMD
(U,W) to:
Table 1. Potential Energy Distributions Using Constant Energy (NVE) Dynamics, Langevin Dynamics, and W-C
Thermostats for Bulk Watera
300 K 310 K
thermostat τ[ps] 〈U〉 σ2
skew 〈U〉 σ2
skew g C12[10-4
]
NVE -9779.3(2) 709(7) -0.01(2) -9665.7(2) 757(8) -0.01(2) 2.9 -2(3)
MD Langevin 2.0 -9779.3(7) 2120(30) 0.00(2) -9665.8(7) 2110(30) -0.01(2) 1.0 -3(3)
weak coupling 5.0 -9779.4(4) 722(8) -0.03(2) -9665.9(3) 767(7) -0.01(2) 2.8 0(3)
weak coupling 0.5 -9778.5(4) 858(9) -0.01(2) -9665.5(4) 900(10) -0.01(2) 2.4 -2(3)
REMD Langevin 2.0 -9777.8(7) 2110(30) -0.01(2) -9666.7(8) 2150(30) -0.01(2) 1.0 -1(2)
weak coupling 5.0 -9773.4(7) 910(10) 0.26(3) -9671.2(7) 940(10) -0.31(2) 2.0 -55(3)
weak coupling 0.5 -9775.2(5) 990(10) 0.17(2) -9669.0(5) 1000(10) -0.18(3) 2.0 -42(4)
a
Energies are in units of kcal/mol. The numbers in parentheses are the estimated statistical errors in the last digits (one standard
deviation). Italic numbers indicate deviations between REMD and MD results that exceed twice the combined statistical errors. τ is the
thermostat coupling time. The skew coefficient is defined as skew ) 〈(U - 〈U〉)3
〉/〈(U - 〈U〉)2
〉3/2
. g ) CV/CV
fluc
is the ratio of the excess heat
capacities calculated from the temperature derivative and the canonical fluctuation formula. C12 is the normalized cross-correlation
coefficient.
Figure 2. Comparison of bulk TIP3P water potential energy
distributions from MD and REMD simulations (semilogarithmic
scale). The blue circles and red squares show the REMD
results for the replicas at 300 and 310 K, respectively. The
blue and red dashed lines are calculated from iterated
numerical solutions of eq 2 at 300 and 310 K, respectively.
For reference, Gaussian approximations to the MD results
using a W-C thermostat4
with a coupling time of 5 ps are
shown as black (300 K) and green lines (310 K). The arrows
indicate the change in the energy distributions of REMD
simulations compared to MD, in particular the buildup of
artificial tails and the small shifts in the means.
Thermostat Artifacts J. Chem. Theory Comput., Vol. 5, No. 5, 2009 1395
pREMD
(U, W) ) pMD
(W, U)pacc
(W, U) +
pMD
(U, W)[1 - pacc
(U, W)] (2)
where pacc
(U,W) is the probability that a replica exchange is
accepted if U and W are the potential energies of the
structures in replicas 1 and 2, respectively. If the acceptance
probabilities satisfy detailed balance, pacc
(U,W)pMD
(U,W) )
pacc
(W,U)pMD
(W,U), then replica exchange preserves the
distribution, pREMD
(U,W) ) pMD
(U,W); otherwise, the distribution
will be distorted. In our model, we iterate eq 2 to
self-consistency and then calculate the marginal distributions
by integration, p1
REMD
(U) ) ∫pREMD
(U,W)dW and p2
REMD
(W)
) ∫pREMD
(U,W)dU. In the numerical calculations we approximate
the potential energy distributions by Gaussians
with means and variances taken from the MD results in Table
1. As shown in Figure 1, Gaussians provide excellent
approximations to the actual distributions over the whole
range of potential energies sampled in the MD simulations.
Figure 2 compares the potential energy distributions
obtained by iteration of eq 2 to those of the REMD
simulations. We find that the model can account almost
quantitatively for the effects of the W-C thermostat on the
energy distributions. The distribution at the low temperature
is shifted upward and has a pronounced tail toward the higher
energies. The energy distribution at the high temperature is
modified in the opposite direction: it is shifted toward lower
energies and has a tail skewed to the left. The crosscorrelation
coefficient C12 predicted by the model is about
-0.09, close to the C12 ≈ -0.005 obtained from the W-C
REMD simulations (Table 1). The smaller correlations in
the REMD simulations can be explained by the randomizing
effect of the thermostatted MD runs between replica
exchanges.
The distortions seen in Figure 2 arise because the potential
energy distributions of the thermostat are inconsistent with
eq 1. For the narrowed energy distributions produced by the
W-C thermostat, as compared to the wider canonical
distributions, replica exchange is relatively more likely to
be accepted for conformations in the high-energy tail at the
low temperature, and in the low-energy tail at the high
temperature. We will show in the following that this bias
can result in distortions of the conformational equilibrium
of a solute.
3.2. Protein Folding. REMD is widely used to study the
folding of peptides and proteins. Protein folding can often
be described by using only two dominant states, with a folded
state being enthalpically stabilized against an entropically
favored unfolded state. In the following, we explore how
simulations of protein folding are affected by replica
exchange when W-C thermostats are used. We analyze the
simulation data from refs 3 and 21 for blocked Ala5 in water,
which folds into a short helical peptide for the force field
used.
Figure 3 compares the relative populations of the folded
(helical) state obtained from long MD runs at 300 and 350
K, and from REMD simulations using Langevin and W-C
thermostats, respectively, with 12 replicas equally spaced in
temperature between 295 and 350 K. The folded state is
defined as in refs 3 and 21. We find that the equilibrium
populations of the folded state agree for standard MD
simulations using a W-C thermostat and REMD simulations
using a Langevin thermostat. In contrast, the folded populations
obtained from REMD simulations using a W-C
thermostat differ significantly from both the standard MD
results and the REMD/Langevin results. In the REMD runs
with the W-C thermostat, the population of the folded
(helical) peptide is increased at the low temperatures and
decreased at the high temperatures, with ∼10% shifts in the
folded populations at 300 and at 350 K.
For a quantitative comparison, we fit the relative populations
pf(T) of the folded state to a melting profile pf(T) )
1/[1 + exp (∆H/kBT - ∆S/kB)] where ∆H and ∆S are the
enthalpy and entropy of folding, respectively, which are
assumed to be independent of temperature in the range of
295 to 350 K. The results are shown in Table 2. We find
that both the enthalpy and entropy of folding from the
Langevin-thermostatted REMD and the MD simulations are
in excellent agreement. In contrast, both the enthalpy and
entropy obtained from REMD/W-C and REMD/Langevin
simulations differ by about 7 times their respective combined
standard deviations. For the small peptide, the systematic
error in the enthalpy of folding is almost 3 kcal/mol. We
conclude from these inconsistencies that REMD with a W-C
Figure 3. Relative populations of the folded (helical) state of
Ala5 as a function of temperature (red crosses: REMD with
W-C thermostat; blue squares: REMD with Langevin thermostat;
black circles: MD). The dashed line (REMD with
W-C) and solid line (REMD with Langevin) are fitted melting
profiles. The error bars correspond to one standard deviation
estimated from four independent runs. In case of the MD
simulations, the standard deviations agree well with the
theoretical value obtained from the kinetic rate coefficients of
folding and unfolding21
in a two-state kinetic model.29
Vertical
arrows indicate the population changes in the REMD W-C
simulations.
Table 2. Enthalpy, ∆H, and Entropy, ∆S, of Folding
Obtained from Fits to the Melting Profiles (Figure 3)a
∆H
[kcal/mol]
∆S
[cal/(mol K)]
〈U〉f -〈U〉u
[kcal/mol]
MD -3.20 ( 1.00 -8.0 ( 2.9 -3.00 ( 0.10
REMD (Langevin) -2.93 ( 0.26 -7.0 ( 0.8 -3.13 ( 0.10
REMD (W-C) -5.74 ( 0.14 -15.5 ( 0.4 -3.68 ( 0.07
a
The last column lists the difference in average total potential
energy of the folded and unfolded states. The estimated statistical
errors (one standard deviation) are also given.
1396 J. Chem. Theory Comput., Vol. 5, No. 5, 2009 Rosta et al.
thermostat significantly alters the temperature dependence
of the folded population.
Qualitatively, the change in the folded populations induced
by combining REMD with a W-C thermostat can be
explained by using a simple model, as illustrated in Figure
4. In this model, we assume that the folded state has lower
enthalpy than the unfolded state. The blue (red) lines show
the potential energy distribution of the folded (unfolded)
states. In the canonical case, replica exchange leaves the
distributions unchanged, thus preserving equilibrium. For a
W-C thermostat (bottom), the distributions are narrower,
reducing the overlap of the distributions at different temperatures.
As a consequence, the probability of acceptance
of replica exchange moves is reduced. In addition, in the
W-C REMD there are relatively more accepted replica
exchanges between the unfolded state at the lower temperature
T1 and the folded state at T2. As a result, the population
of the folded states artificially increases at T1 and decreases
at T2. In Appendix A we implement such a model, and show
that it can account for the observed population shifts.
Interestingly, this model also explains the observation of
Cooke and Schmidler9
that in their simulations of alanine
dipeptide in vacuum using REMD with a W-C thermostat,
the C7ax configuration was not populated in the lowesttemperature
replica: C7ax has higher energy than C7eq and,
according to the model in Figure 4, should have an artificially
low population in the replicas at low temperatures.
For further validation of our model, we also calculate ∆H
directly from the difference in the average total potential
energies of the system with the peptide in the folded and
unfolded state, respectively (see Table 2; the small pV
contribution is ignored). The potential energy differences
between folded and unfolded states averaged over all
temperatures and all four initial conditions are listed for both
the MD and REMD simulations with Langevin and W-C
thermostats. Errors are estimated from the 5 × 4 ) 20 and
12 × 4 ) 48 independent MD and REMD simulations,
respectively. ∆H obtained from the potential energy differences
in REMD/Langevin and in standard MD simulations
are consistent with the values obtained from the fit of the
melting profile. In contrast, for the REMD simulations with
the W-C thermostat the energy difference and the value of
∆H are inconsistent, differing by ∼10 combined standard
deviations. ∆H obtained from the potential energy distributions
of the REMD W-C simulations is significantly closer
to the values obtained from the MD and REMD Langevin
data, as expected from our model illustrated in Figure 4.
4. Conclusions
We showed that thermostats can significantly affect the
outcome of REMD simulations, consistent with the results
of earlier studies.9
W-C thermostats produce potential
energy distributions that are narrower than those expected
for a canonical ensemble. If a standard replica-exchange
acceptance criterion is used, replica exchange will distort
these distributions and, as a result, shift the configurationspace
populations. For bulk TIP3P water18
at near-ambient
conditions, the effects are statistically significant but overall
small. We found that in W-C simulations the mean potential
energies are shifted, become artificially correlated, and
develop distributions with enhanced tails. No such effects
were seen in REMD simulations using a Langevin thermostat
that preserves the canonical distribution.
For protein folding, REMD with noncanonical thermostats
is expected to result in more pronounced effects. Simulations
of a small helix-forming peptide using a W-C thermostat
showed that the folding probabilities are artificially enhanced
by ∼10% at the low temperatures and reduced at the high
temperatures. For thermostats that produce a higher variance
than the canonical energy distribution, the opposite effects
are expected.
We have used simple models to estimate the effects of
thermostats on the energy distributions and folding equilibria.
As input, these models use the energy differences between
folded and unfolded states, and the means and variances of
the potential energy. We found that the thermostat-induced
changes in the energy distributions and folding equilibria
could be predicted nearly quantitatively, suggesting that the
models can be used to assess and possibly correct W-C
REMD simulation results.
For noncanonical simulations (e.g., at constant energy or
with W-C thermostats), the replica-exchange acceptance
criterion, eq 1, has to be modified.10
Without exact explicit
expressions for the configuration-space distribution, such
modifications are not easily possible in practice. We therefore
conclude that REMD simulations should be carried out with
thermostats that preserve canonical distributions, such as
Langevin thermostats,11
Andersen thermostats,12
Nose´-Hoover
Figure 4. Schematic of the effect of REMD with W-C
thermostats on folding probabilities. Potential energy probability
distributions of folded (blue) and unfolded (red) states
are shown at two temperatures before and after replica
exchange. For classical canonical distributions (upper panel),
REMD does not affect the distributions. However, for the
narrowed distributions in W-C REMD (lower panel), replica
exchange alters the relative populations of folded and unfolded
states. Folded states become overpopulated at low temperatures
and underpopulated at high temperatures, as indicated
by the vertical arrows.
Thermostat Artifacts J. Chem. Theory Comput., Vol. 5, No. 5, 2009 1397
chains13,14
based on the Nose´-Hoover thermostat15,16
(which
by itself is not guaranteed to produce a canonical distribu-
tion),9,30
or the hybrid Monte Carlo method.17
Acknowledgment. We thank Dr. Attila Szabo for many
helpful and stimulating discussions and Dr. Nicolas Fawzi
for his valuable comments. This research used the Biowulf
Linux cluster at the National Institutes of Health (NIH), and
the Irish Centre for High-End Computing (ICHEC). E.R. and
G.H. were supported by the Intramural Research Program
of the National Institute of Diabetes and Digestive and
Kidney Diseases, NIH.
Appendix: Protein Folding in REMD
Simulations
We use a simple model to analyze the effects of using W-C
thermostats in protein-folding REMD simulations. In this
model, the protein is assumed to have two states, a lowenergy
folded state and a high-energy unfolded state, Uf <
Uu, with relative populations pf(Ti) at temperature Ti. For
simplicity, we first consider only two replicas at two
temperatures, T1 < T2. There are four distinct folding states
overall: ff, fu, uf, and uu, where the first position indicates
the folding state at T1 and the second at T2. At equilibrium,
the populations of the four states are pff, pfu, puf, and puu,
respectively. An accepted replica exchange switches the
states X ∈{f,u} and Y ∈{f,u} at temperatures T1 and T2. After
an exchange attempt, the probability distribution of the four
states changes to pXY
REMD
pff
REMD
) pff
pfu
REMD
) pfu[1 - pacc
(fu f uf)] + pufpacc
(uf f fu)
puf
REMD
) puf[1 - pacc
(uf f fu)] + pfupacc
(fu f uf)
puu
REMD
) puu
(3)
where pXY
REMD
) pXY in the canonical case. The acceptance
probabilities pacc
of replica exchanges depend on the potential
energy difference. For simplicity, we assume that the solventenergy
distributions at each temperature Ti are Gaussians,
gi(U), with means 〈U〉i and standard deviations σi, independent
of the state of the protein. We thus ignore the nonGaussian
tails of the distributions. The distributions of the
potential energies at temperature Ti in the folded and unfolded
states are then pi(U|f) ) gi(U - Uf) and pi(U|u) ) gi(U Uu),
respectively. If the solvent-energy fluctuations are fast
relative to folding and unfolding, they can be integrated out.
The probability of accepting an exchange XY f YX then
becomes
pacc
(XY f YX) )
∫dU∫dWp1(U|X)p2(W|Y)min{1, e(β2-β1)(W-U)
} (4)
This model can easily be extended to more than two replicas.
As in the model for bulk water, the coupled equations
corresponding to eq 3 can be iterated to self-consistency.
We applied this model to the REMD simulations of Ala5
folding in water. To calculate the replica-exchange acceptance
probabilities, we assumed a constant enthalpy
difference of folding, ∆H, and linear temperature dependences
of σ and 〈U〉. The parameters were extracted from
MD simulations of Ala5 using a W-C thermostat with a 1
ps coupling constant at 300 and 350 K, respectively. ∆H
and the folding probabilities at different temperatures were
determined from the folding and unfolding rates extracted
from the MD simulations3,21
using a two-state model and
assuming an Arrhenius temperature dependence. As in the
REMD simulations, we used 12 temperatures spaced equally
between 295 and 350 K. The iterative scheme corresponding
to eq 3 was applied by alternating exchange attempts between
replicas at neighboring temperatures. Consistent with the
REMD simulations, the converged model led to an increase
in the folded population at low temperatures and a decrease
at high temperatures. However, the magnitude of the effect
was smaller, resulting in ∼5% changes in the folded
population at the two extreme temperatures.
References
(1) Sugita, Y.; Okamoto, Y. Chem. Phys. Lett. 1999, 314, 141–
151.
(2) Garcı´a, A. E.; Sanbonmatsu, K. Y. Proc. Natl. Acad. Sci.
U.S.A. 2002, 99, 2782–2787.
(3) Buchete, N. V.; Hummer, G. Phys. ReV. E 2008, 77, 030902.
(4) Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.;
DiNola, A.; Haak, J. R. J. Chem. Phys. 1984, 81, 3684–3690.
(5) D’Alessandro, M.; Tenenbaum, A.; Amadei, A. J. Phys.
Chem. B 2002, 106, 5050–5057.
(6) Morishita, T. J. Chem. Phys. 2000, 113, 2976–2982.
(7) Allen, M.; Tildesley, D. Computer Simulation of Liquids;
Clarendon Press: Oxford, U.K., 1987.
(8) Frenkel, D.; Smit, B. Understanding Molecular Simulation.
From Algorithms to Applications; Academic Press: San
Diego, CA, 2002.
(9) Cooke, B.; Schmidler, S. C. J. Chem. Phys. 2008, 129,
164112.
(10) Calvo, F.; Neirotti, J. P.; Freeman, D. L.; Doll, J. D. J. Chem.
Phys. 2000, 112, 10350–10357.
(11) Pastor, R. W.; Brooks, B. R.; Szabo, A. Mol. Phys. 1988,
65, 1409–1419.
(12) Andersen, H. C. J. Chem. Phys. 1980, 72, 2384–2393.
(13) Martyna, G. J.; Klein, M. L.; Tuckerman, M. J. Chem. Phys.
1992, 97, 2635–2643.
(14) Tobias, D. J.; Martyna, G. J.; Klein, M. L. J. Phys. Chem.
1993, 97, 12959–12966.
(15) Nose´, S. J. Chem. Phys. 1984, 81, 511.
(16) Hoover, W. Phys. ReV. A 1985, 31, 1695–1697.
(17) Duane, S.; Kennedy, A. D.; Pendleton, B. J.; Roweth, D. Phys.
Lett. B 1987, 195, 216–222.
(18) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey,
R. W.; Klein, M. L. J. Chem. Phys. 1983, 79, 926–935.
(19) Case, D. A.; Cheatham, T., III; Darden, T.; Gohlke, H.; Luo,
R.; Merz, K. M., Jr.; Onufriev, A.; Simmerling, C.; Wang,
B.; Woods, R. J. J. Comput. Chem. 2005, 26, 1668–1688.
(20) Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee,
H.; Pedersen, L. G. J. Chem. Phys. 1995, 103, 8577.
(21) Buchete, N. V.; Hummer, G. J. Phys. Chem. B 2008, 112,
6057–6069.
1398 J. Chem. Theory Comput., Vol. 5, No. 5, 2009 Rosta et al.
(22) Lindahl, E.; Hess, B.; van der Spoel, D. J. Mol. Model. 2001,
7, 306–317.
(23) Nymeyer, H.; Garcı´a, A. E. Proc. Natl. Acad. Sci. U.S.A.
2003, 100, 13934–13939.
(24) Sorin, E. J.; Pande, V. S. Biophys. J. 2005, 88, 2472–2493.
(25) Paschek, D.; Gnanakaran, S.; Garcı´a, A. E. Proc. Natl. Acad.
Sci. U.S.A. 2005, 102, 6765–6770.
(26) Hess, B.; Bekker, H.; Berendsen, H. J. C.; Fraaije, J. G. E. M.
J. Comput. Chem. 1997, 18, 1463–1472.
(27) Sindhikara, D.; Meng, Y. L.; Roitberg, A. E. J. Chem. Phys.
2008, 128, 024103.
(28) Zheng, W.; Andrec, M.; Gallicchio, E.; Levy, R. M. Proc.
Natl. Acad. Sci. U.S.A. 2007, 104, 15340–15345.
(29) Berezhkovskii, A. M.; Szabo, A.; Weiss, G. H. J. Chem. Phys.
1999, 110, 9145–9150.
(30) Tuckerman, M. E.; Liu, Y.; Ciccotti, G.; Martyna, G. J.
J. Chem. Phys. 2001, 115, 1678–1702.
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