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Protein structure predictions by enhanced conformational sampling methods


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In this Special Festschrift Issue for the celebration of Professor Nobuhiro Gō’s 80th birthday, we review enhanced conformational sampling methods for protein structure predictions. We present several generalized-ensemble algorithms such as multicanonical algorithm, replica-exchange method, etc. and parallel Monte Carlo or molecular dynamics method with genetic crossover. Examples of the results of these methods applied to the predictions of protein tertiary structures are also presented. Fullsize Image
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Biophysics and Physicobiology
Review Article
Special Issue 
“Progress of Theoretical and Computational Biophysics”
Vol. 16, pp. 344–366 (2019)
doi: 10.2142/biophysico.16.0_344
Corresponding author: Yuko Okamoto, Department of Physics, Gradu-
ate School of Science, Nagoya University, Furo-cho, Chikusa-ku,
Nagoya, Aichi 464-8602, Japan.
Protein structure predictions by enhanced conformational
sampling methods
Yuko Okamoto1,2,3,4,5
1Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan
2Structural Biology Research Center, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan
3Center for Computational Science, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
4Information Technology Center, Nagoya University, Nagoya, Aichi 464-8601, Japan
5JST-CREST, Nagoya, Aichi 464-8602, Japan
Received July 5, 2019; accepted August 7, 2019
In this Special Festschrift Issue for the celebration of
Professor Nobuhiro Gō’s 80th birthday, we review
enhanced conformational sampling methods for protein
structure predictions. We present several generalized-en-
semble algorithms such as multicanonical algorithm,
replica-exchange method, etc. and parallel Monte Carlo
or molecular dynamics method with genetic crossover.
Examples of the results of these methods applied to the
predictions of protein tertiary structures are also pre-
Key words: molecular simulation, protein structure
prediction, multicanonical algorithm,
replica-exchange method, genetic algorithm
Professor Nobuhiro Gō is probably most well-known for
his consistency principle in protein folding [1], which states
that various types of interactions that contribute to the stabi-
lization of the native conformation of a protein are consis-
tent with each other. This principle was deduced from the
results of Monte Carlo simulations of a lattice protein model.
This principle was reinterpreted as the principle of minimal
frustration [2,3]. They showed that the phase space of the
protein system is characterized by two parameters, folding
temperature TF and glass transition temperature TG and that a
protein can fold into a unique native structure if TG/TF<<1.
Another two-parameter argument is that a protein folds into
the native structure if (TθTF)/Tθ<<1, where Tθ is the
coil-globule transition temperature [4,5]. These parameters
characterize the free energy landscape of protein systems
[1–6]. While these arguments were mainly given from simu-
lations of lattice models and coarse-grained models, we
conrmed these properties by calculating TG, TF, and Tθ of a
small peptide and its free energy landscape by simulations of
an all-atom model [7,8].
Besides these fundamental works on protein folding,
Professor Gō has also shown that protein structures can be
determined in atomistic details by utilizing the results of
NMR experiments [9], which drastically improved the dis-
tance geometry methods (see, e.g., [10]). Before that time,
protein structures were determined mainly by X-ray diffrac-
This article reviews various enhanced conformational sampling methods for protein structure predictions. Several
generalized-ensemble algorithms and a method based on the genetic algorithm are presented. These methods can
sample much wider conformational space of biomolecules than conventional methods. Mathematical details are
given so that those who are not familiar with these methods can understand them. Some examples of simulation
results of protein structure predictions by these methods are also presented in order to elucidate the eectiveness
of the methods.
Okamoto: Enhanced sampling methods 345
encouraged them to develop a modied force eld (namely,
CHARMM22 with CMAP [32]) [33]. We have also pro-
posed several methods for improving the force elds [34–37]
(for a review, see Ref. [38]).
This article is organized as follows. We rst describe the
generalized-ensemble algorithms. We then present the
results by some of the generalized-ensemble simulations.
Finally, we draw conclusions.
Simulation Methods
Multicanonical algorithm
Let us consider a system of N atoms of mass mk (k=1, ...,
N) with their coordinate vectors and momentum vectors
denoted by q≡{q1, ..., qN} and p≡{p1, ..., pN}, respectively.
The Hamiltonian H(q, p) of the system is the sum of the
kinetic energy K(p) and the potential energy E(q):
H(q, p) = K(p) + E(q), (1)
K(p) =
. (2)
In the canonical ensemble at temperature T each state
x≡(q, p) with the Hamiltonian H(q, p) is weighted by the
Boltzmann factor:
WB(x; T) = exp (−βH(q, p)), (3)
where the inverse temperature β is dened by β=1/kBT
(kB is the Boltzmann constant). The average kinetic energy
at temperature T is then given by
T =
= 3
2 NkBT. (4)
Because the coordinates q and momenta p are decoupled
in Eq. (1), we can suppress the kinetic energy part and can
write the Boltzmann factor as
WB(x; T) = WB(E; T) = exp (−βE). (5)
The canonical probability distribution of potential energy
PB(E; T) is then given by the product of the density of states
n(E) and the Boltzmann weight factor WB(E; T):
PB(E; T) n(E)WB(E; T). (6)
Because n(E) is a rapidly increasing function and the
Boltzmann factor decreases exponentially, the canonical
ensemble yields a bell-shaped distribution which has a
maximum around the average energy at temperature T. The
conventional MC or MD simulations at constant tempera-
ture are expected to yield PB(E; T). A MC simulation based
on the Metropolis method [39] is performed with the follow-
tion experiments, and structures determined by NMR exper-
iments were emerging for the rst time. Professor Gō’s
method was one of very effective ones that were used to
determine the three-dimensional structures of proteins from
NMR experiments. After Professor Gō’s works, many groups
worked on this problem. One example is the uses of simu-
lated annealing (SA) [11] to determine protein structures
from the results of X-ray and NMR experiments [12–15].
SA was also applied to the protein folding simulations (see,
e.g., Refs. [16–20] for earlier applications).
In this article, we discuss our methods for prediction of
protein structures by Monte Carlo (MC) and molecular
dynamics (MD) simulations. Conventional simulations of
biomolecules suffer from the multiple-minima problem: The
canonical xed-temperature simulations at low temperatures
tend to get trapped in a huge number of local-minimum-
energy states, which will give wrong results. We have been
advocating the uses of the generalized-ensemble algorithms,
which overcomes the multipole-minima problem (for reviews
see, e.g., Refs. [21–25]). In the generalized-ensemble algo-
rithm, each state is weighted by an articial, non-Boltzmann
probability weight factor so that a random walk in potential
energy space and/or other physical quantities (such as vol-
ume, etc.) may be realized. The random walk allows the
simulation to escape from any energy-local-minimum state
and to sample much wider conformational space than by
conventional methods. From a single simulation run, one
can obtain accurate ensemble averages as functions of
temperature and/or other parameters (such as pressure, etc.)
of the system by the single-histogram [26] and multiple-
histogram [27,28] reweighting techniques (an extension of
the multiple-histogram method is also referred to as the
weighted histogram analysis method (WHAM) [28]).
Sucessful predictions of the three-dimensional structures
of proteins are possible when both enhanced sampling tech-
niques and accurate potential energy (or, force elds) for the
protein systems are employed. If one has sufcient computa-
tional power, the former may not be necessary (see, e.g.,
Ref. [29] for examples of folding proteins into their native
structures by conventional canonical-ensemble simulations).
However, generalized-ensemble algorithms will save com-
putation time for this purpose. Generalized-ensemble algo-
rithms can give accurate thermodynamic averages and can
also be used to judge which existing force elds are good.
Several existing force elds were compared by general-
ized-ensemble simulations [30,31]. Those works showed
that AMBER99 forms 310-helices and CHARMM22 forms
π-helix in small peptides, which are not observed in experi-
ments [30,31]. The author visited the Laboratory of Professor
Charles L. Brooks, III in August, 2001, and gave a seminar
talk, which included unpublished results of Ref. [30,31]
(especially, Fig. 4(e) of Ref. [31], which clearly showed the
formation of π-helix with CHARMM22). Although they
have also independently observed tendency of π-helix for-
mations with CHARMM22, our convincing results above
346 Biophysics and Physicobiology Vol. 16
can also be considered as a sophisticated, ideal realization of
a class of algorithms called umbrella sampling [50]. Also
closely related methods are Wang-Landau method [51,52],
which is also referred to as density of states Monte Carlo
[53], and metadynamics [54], which can be considered as
an extension of Wang-Landau method where a random walk
in reaction coordinate, or collective variable, instead of
potential energy is performed [55]. While MUCA and its
generalizations have been rst applied to spin systems,
MUCA was also introduced to the molecular simulation eld
[56]. Since then MUCA and its generalizations have been
extensively used in many applications in protein and other
biomolecular systems [56–86]. Molecular dynamics version
of MUCA has also been developed [49,62,63]. MUCA has
been extended so that at distributions in other variables
instead of potential energy may be obtained (see, e.g., Refs.
In the multicanonical ensemble [42,43], each state is
weighted by a non-Boltzmann weight factor Wmu(E) (which
we refer to as the multicanonical weight factor) so that a
uniform potential energy distribution Pmu(E) is obtained:
Pmu(E) n(E)Wmu(E) ≡ const. (16)
The at distribution implies that a free random walk in the
potential energy space is realized in this ensemble. This
allows the simulation to escape from any local minimum-
energy states and to sample the congurational space much
more widely than the conventional canonical MC or MD
The denition in Eq. (16) implies that the multicanonical
weight factor is inversely proportional to the density of
states, and we can write it as follows:
Wmu(E) ≡ exp [−β0Emu(E; T0)] = 1
n(E) , (17)
where we have chosen an arbitrary reference temperature,
T0=1/kBβ0, and the “multicanonical potential energy” is
dened by
Emu(E; T0) ≡ kBT0 ln n(E) = T0S(E). (18)
Here, S(E) is the entropy in the microcanonical ensemble.
Since the density of states of the system is usually unknown,
the multicanonical weight factor has to be determined numer-
ically by iterations of short preliminary runs [42,43].
A multicanonical MC simulation is performed, for instance,
with the usual Metropolis criterion [39]: The transition prob-
ability of state x with potential energy E to state x′ with
potential energy E′ is given by
w(x x) = min
1, Wmu(E)
= min
1, n(E)
= min (1, exp (−β0Emu)) , (19)
ing transition probability from a state x of potential energy E
to a state x′ of potential energy E′:
w(x x) = min
1, WB(E′; T)
WB(E; T)
= min (1, exp (−βE)). (7)
E = E′ E. (8)
A MD simulation, on the other hand, is based on the follow-
ing Newton equations of motion:
qk = pk
, (9)
k = ∂E
= fk , (10)
where fk is the force acting on the k-th atom (k=1, ..., N).
This set of equations actually yield the microcanonical
ensemble, and we have to add a thermostat in order to obtain
the canonical ensemble at temperature T. Here, we just fol-
low Nosé’s prescription [40,41], and we have
qk = pk
, (11)
k = ∂E
s pk = fk
s pk , (12)
ṡ = s
Q , (13)
s =
− 3NkBT = 3NkB(T(t) T) , (14)
where s is Nosé’s scaling parameter, Q is its mass, Ps is its
conjugate momentum, and the “instantaneous temperature”
T(t) is dened by
T(t) = 1
. (15)
However, in practice, it is very difcult to obtain accurate
canonical distributions of complex systems at low tem-
peratures by conventional MC or MD simulation methods.
This is because simulations at low temperatures tend to get
trapped in one or a few of local-minimum-energy states. The
generalized-ensemble algorithms overcome this difculty
by performaing random walks in physical quantities such as
potential energy and volume, etc. (or their conjugate param-
eters such as temperature and pressure, etc., respectively).
One of the most well-known generalized-ensemble algo-
rithms is perhaps the multicanonical algorithm (MUCA)
[42,43] (for reviews see, e.g., Refs. [44,45]). The method is
also referred to as entropic sampling [46,47] and adaptive
umbrella sampling [48] of the potential energy [49]. MUCA
Okamoto: Enhanced sampling methods 347
functions of potential energy) at any temperature T (=1/kBβ)
can now be obtained as long as one stores the “trajectory” of
congurations (and A) from the production run. Namely, we
have [74]
T =
−1(E(x(k))) exp [−βE(x(k))]
−1(E(x(k))) exp [−βE(x(k))] , (25)
where x(k) is the conguration at the k-th MC (or MD) step
and n0 is the total number of congurations stored. Note that
when A is a function of E, Eq. (25) reduces to Eq. (22) where
the density of states is given by Eq. (24).
Replica-exchange method
The replica-exchange method (REM) [87,88] is one of
the most widely used method in biomolecular simulations.
(REM is also referred to as parallel tempering [89] and
multiple Markov chain method [90]) In this method, a num-
ber of non-interacting copies (or, replicas) of the original
system at different temperatures are simulated independently
and simultaneously by the conventional MC or MD method.
Every few steps, pairs of replicas are exchanged with a
specied transition probability. The weight factor is just the
product of Boltzmann factors, and so it is essentially known.
REM has already been used in many applications in
protein systems [91–104]. Other molecular simulation elds
have also been studied by this method in various ensembles
[105–108]. Moreover, REM and other generalized-ensemble
algorithms were introduced to the quantum chemistry eld
[109–113]. The details of molecular dynamics algorithm for
REM, which is referred to as the Replica-Exchange Molecu-
lar Dynamics (REMD) have been worked out in Ref. [92],
and this led to a wide application of REM in the protein
folding and related problems (see, e.g., Refs. [114–128]).
The system for REM consists of M non-interacting copies
(or, replicas) of the original system in the canonical ensem-
ble at M different temperatures Tm (m=1, ..., M). We arrange
the replicas so that there is always exactly one replica at each
temperature. Then there exists a one-to-one correspondence
between replicas and temperatures; the label i (i= 1, ..., M)
for replicas is a permutation of the label m (m=1, ..., M) for
temperatures, and vice versa:
i = i(m) f(m),
m = m(i) f−1(i), (26)
where f(m) is a permutation function of m and f1(i) is its
Let X= {x1
[i(1)], ..., xM
(1), ..., xm
(M)} stand for a
“state” in this generalized ensemble. Each “substate” xm
[i] is
specied by the coordinates q[i] and momenta p[i] of N atoms
in replica i at temperature Tm:
[i] ≡ (q[i], p[i])m. (27)
Emu = Emu(E′; T0) Emu(E; T0). (20)
The MD algorithm in the multicanonical ensemble also nat-
urally follows from Eq. (17), in which the regular constant
temperature MD simulation (with T=T0) is performed by
replacing E by Emu in Eq. (12) [62,63]:
k = Emu(E; T0)
s pk = Emu(E; T0)
E fk
s pk .
If the exact multicanonical weight factor Wmu(E) is known,
one can calculate the ensemble averages of any physical
quantity A at any temperature T (=1/kBβ) as follows:
T =
A(E)PB(E; T)
PB(E; T) =
A(E)n(E) exp (−βE)
n(E) exp (−βE) ,
where the density of states is given by (see Eq. (17))
n(E) = 1
. (23)
The summation instead of integration is used in Eq. (22),
because we often discretize the potential energy E with
step size ϵ(E=Ei; i=1, 2, ...). Here, the explicit form of
the physical quantity A should be known as a function of
potential energy E. For instance, A(E)=E gives the average
potential energy
T as a function of temperature, and
T)2 gives specic heat.
In general, the multicanonical weight factor Wmu(E), or
the density of states n(E), is not a priori known, and one
needs its estimator for a numerical simulation. This esti-
mator is usually obtained from iterations of short trial multi-
canonical simulations. (The details of this process are
described, for instance, in Refs. [44,45]). However, the iter-
ative process can be non-trivial and very tedius for complex
Once a satsifactory multicanonical weight factor is
obtained, we perform with this weight factor a multicanoni-
cal simulation with high statistics (production run) in order
to get even better estimate of the density of states. Let Nmu(E)
be the histogram of potential energy distribution Pmu(E)
obtained by this production run. The best estimate of the
density of states can then be given by the single-histogram
reweighting techniques [26] as follows (see the proportion-
ality relation in Eq. (16)):
n(E) = Nmu(E)
. (24)
By substituting this quantity into Eq. (22), one can calculate
ensemble averages of physical quantity A(E) as a function of
temperature. Moreover, ensemble averages of any physical
quantity A (including those that cannot be expressed as
348 Biophysics and Physicobiology Vol. 16
Replica exchange is accepted according to the usual
Metropolis criterion [39]:
X) ≡ w(xm
[j]) = min
(1, exp (−∆)), (33)
where in the second expression (i.e., w(xm
[j])) we explicitly
wrote the pair of replicas (and temperatures) to be exchanged,
∆ = βm(E(q[j]) E(q[i])) βn(E(q[j]) E(q[i])) (34)
= (βm βn)(E(q[j]) E(q[i])). (35)
Without loss of generality we can assume T1<T2<...<TM.
A REM simulation is then realized by alternately performing
the following two steps:
1. Each replica in canonical ensemble of the xed tempera-
ture is simulated simultaneously and independently for a
certain MC or MD steps.
2. A pair of replicas at neighboring temperatures, say xm
[i] and
m+1, are exchanged with the probability w(xm
m+1) in Eq.
Note that in Step 2 we exchange only pairs of replicas
corresponding to neighboring temperatures, because the
acceptance ratio of the exchange process decreases exponen-
tially with the difference of the two βs (see Eqs. (35) and
(33)). Note also that whenever a replica exchange is accepted
in Step 2, the permutation functions in Eq. (26) are updated.
After a long REM production run, the canonical expectation
value of a physical quantity A at temperature Tm (m=1, ..., M)
can be calculated by the usual arithmetic mean as follows:
Tm = 1
k=1 A(xm(k)), (36)
where xm(k) (k=1, ..., nm) are the congurations obtained at
temperature Tm and nm is the total number of measurements
made at T=Tm. The expectation value at any intermediate
temperature can also be obtained from Eq. (22), where
the density of states is given by the multiple-histogram
reweighting techniques [27,28] as follows. Let Nm(E) and
nm be respectively the potential-energy histogram and the
total number of samples obtained at temperature Tm=1/kBβm
(m=1, ..., M). The best estimate of the density of states is
then given by [27,28]
n(E) =
nm exp (fm βmE)
, (37)
where we have for each m(=1, ..., M)
exp (−fm) =
n(E) exp (−βmE). (38)
Because the replicas are non-interacting, the weight factor
for the state X in this generalized ensemble is given by the
product of Boltzmann factors for each replica (or at each
(q[i], p[i])}
(q[i(m)], p[i(m)])}
= exp
(q[i], p[i])
= exp
(q[i(m)], p[i(m)])
, (28)
where i(m) and m(i) are the permutation functions in Eq.
We now consider exchanging a pair of replicas in the gen-
eralized ensemble. Suppose we exchange replicas i and j
which are at temperatures Tm and Tn, respectively:
X = {..., xm
[i], ..., xn
[j], ...} → X= {..., xm
[j], ..., xn
[i], ...}.
Here, i, j, m, and n are related by the permutation functions
in Eq. (26), and the exchange of replicas introduces a new
permutation function f:
i = f(m) j = f(m),
j = f(n) i = f(n). (30)
The exchange of replicas can be written in more detail as
[i] ≡ (q[i], p[i])m xm
[j](q[j], p[j])m,
[j](q[j], p[j])n xn
[i]≡ (q[i], p[i])n, (31)
where the denitions for p[i] and p[j] will be given below. In
the original implementation of the replica-exchange method
(REM) [87,88], Monte Carlo algorithm was used, and only
the coordinates q (and the potential energy function E(q))
had to be taken into account. In molecular dynamics algo-
rithm, on the other hand, we also have to deal with the
momenta p. We proposed the following momentum assign-
ment in Eq. (31) [92]:
p[j], (32)
which we believe is the simplest and the most natural. This
assignment means that we just rescale uniformly the veloci-
ties of all the atoms in the replicas by the square root of the
ratio of the two temperatures so that the temperature condi-
tion in Eq. (4) may be satised.
Okamoto: Enhanced sampling methods 349
Emu(E; T0)
(EE1)+Emu(E1; T0), for E < E1,
Emu(E; T0), for E1 E EM ,
Emu(E; T0)
(EEM)+Emu(EM; T0), for E > EM .
The multicanonical MC and MD runs are then performed
respectively with the Metropolis criterion of Eq. (19) and
with the modied Newton equation in Eq. (21), in which
{0}(E) in Eq. (42) is substituted into Emu(E; T0). We expect
to obtain a at potential energy distribution in the range
of Eq. (40). Finally, the results are analyzed by the single-
histogram reweighting techniques as described in Eq. (24)
(and Eq. (22)).
Hence, our choice of Ɛmu
{0}(E) in Eq. (42) results in a
canonical simulation at T=T1 for E<E1, a multicanonical
simulation for E1EEM, and a canonical simulation at T=TM
for E>EM. Note also that the above arguments are indepen-
dent of the value of T0, and we will get the same results,
regardless of its value.
For Monte Carlo method, the above statement follows
directly from the following equation. Namely, our choice of
the multicanonical potential energy in Eq. (42) gives from
Eq. (17)
Wmu(E) = exp [−β0Ɛmn
exp (−β1E), for E < E1 ,
, for E1 E EM ,
exp (−βME), for E > EM . (43)
Multicanonical replica-exchange method
In the previous subsection we presented REMUCA, which
uses a short REM run for the determination of the multi-
canonical weight factor. Here, we present the multi canoni-
cal replica-exchange method (MUCAREM) [73–75]. In
MUCAREM the production run is a REM simulation with
a few replicas not in the canonical ensemble but in the
multicanonical ensemble, i.e., different replicas perform
MUCA simulations with different energy ranges. While
MUCA simulations are usually based on local updates, a
replica-exchange process can be considered to be a global
update, and global updates enhance the sampling further.
We now describe MUCAREM. Let
be the number of
replicas. Here, each replica is in one-to-one correspondence
not with temperature but with multicanonical weight factors
of different energy range. Note that because multicanonical
simulations cover much wider energy ranges than regular
canonical simulations, the number of required replicas for
the production run of MUCAREM is much less than that for
the regular REM (
<< M). The weight factor for this gener-
Note that Eqs. (37) and (38) are solved self-consistently
by iteration [27,28] to obtain the density of states n(E) and
the dimensionless Helmholtz free energy fm. Namely, we can
set all the fm (m=1, ..., M) to, e.g., zero initially. We then use
Eq. (37) to obtain n(E), which is substituted into Eq. (38) to
obtain next values of fm, and so on.
Moreover, ensemble averages of any physical quantity A
(including those that cannot be expressed as functions of
potential energy) at any temperature T (=1/kBβ) can now be
obtained from the “trajectory” of congurations of the pro-
duction run. Namely, we rst obtain fm (m=1, ..., M) by
solving Eqs. (37) and (38) self-consistently, and then we
have [74]
T =
nexp[ f
nexp[ f
where xm(k) (k=1, ..., nm) are the congurations obtained at
temperature Tm.
Replica-exchange multicanonical algorithm
MUCA and REM can be combined. In the replica-
exchange multicanonical algorithm (REMUCA) [73–75]
we rst perform a short REM simulation (with M replicas)
to determine the multicanonical weight factor and then
perform with this weight factor a regular multicanonical
simulation with high statistics. The rst step is accomplished
by the multiple-histogram reweighting techniques [27,28].
Let Nm(E) and nm be respectively the potential-energy histo-
gram and the total number of samples obtained at tempera-
ture Tm (=1/kBβm) of the REM run. The density of states n(E)
is then given by solving Eqs. (37) and (38) self-consistently
by iteration.
Once the estimate of the density of states is obtained, the
multicanonical weight factor can be directly determined from
Eq. (17) (see also Eq. (18)). Actually, the density of states
n(E) and the multicanonical potential energy, Emu(E; T0),
thus determined are only reliable in the following range:
E1EEM , (40)
E1 =
T1 ,
EM =
TM , (41)
and T1 and TM are respectively the lowest and the highest
temperatures used in the REM run. Outside this range we
extrapolate the multicanonical potential energy linearly: [73]
350 Biophysics and Physicobiology Vol. 16
tively, are exchanged: X={..., xm
[i], ..., x[j]
m+1, ...}
X={..., xm
[j], ..., x[i]
m+1, ...}. The transition probability of this
replica exchange is given by the Metropolis criterion:
w(X X) = min (1, exp (−∆)), (48)
where we now have (see Eq. (34)) [73]
∆ = βm{Ɛmu
{m}(E(q[j])) Ɛmu
{m+1}(E(q[j])) Ɛmu
{m+1}(E(q[i]))}. (49)
Here, E(q[i]) and E(q[j]) are the potential energy of the i-th
replica and the j-th replica, respectively.
Note that in Eq. (49) we need to newly evaluate the multi-
canonical potential energy, Ɛmu
{m}(E(q[j])) and Ɛmu
because Ɛmu
{m}(E) and Ɛmu
{n}(E) are, in general, different func-
tions for mn.
In this algorithm, the m-th multicanonical ensemble actu-
ally results in a canonical simulation at T=TL
{m} for E<EL
a multicanonical simulation for EL
{m}, and a canoni-
cal simulation at T=TH
{m} for E>EH
{m}, while the replica-
exchange process samples states of the whole energy range
For obtaining the canonical distributions at any inter-
mediate temperature T, the multiple-histogram reweighting
techniques [27,28] are again used. Let Nm(E) and nm be
respectively the potential-energy histogram and the total
number of samples obtained with the multicanonical weight
factor Wmu
{m}(E) (m=1, ...,
). The expectation value of a
physical quantity A at any temperature T (=1/kBβ) is then
obtained from Eq. (22), where the best estimate of the den-
sity of states is obtained by solving the WHAM equations,
which now read [73]
n(E) =
nm exp (fm)Wmu
nm exp (fm βmƐmu
, (50)
and for each m (=1, ...,
exp (−fm) =
n(E) exp (− βmƐmu
{m}(E)). (51)
Note that Wmu
{m}(E) is used instead of the Boltzmann factor
exp(−βmE) in Eqs. (37) and (38).
Moreover, ensemble averages of any physical quantity A
(including those that cannot be expressed as functions of
alized ensemble is now given by (see Eq. (28))
[i(m)])), (44)
where we prepare the multicanonical weight factor (and the
density of states) separately for m regions (see Eq. (17)):
[i])) = exp [−βmƐmu
. (45)
Here, we have introduced
arbitrary reference tempera-
tures Tm=1/kBβm (m=1, ...,
), but the nal results will be
independent of the values of Tm, as one can see from the
second equality in Eq. (45) (these arbitrary temperatures are
necessary only for MD simulations).
Each multicanonical weight factor Wmu
{m}(E), or the
density of states n{m}(E), is dened as follows. For each m
(m=1, ...,
), we assign a pair of temperatures (TL
{m}, TH
Here, we assume that TL
{m} and arrange the tempera-
tures so that the neighboring regions covered by the pairs
have sufcient overlaps. Without loss of generality we can
assume TL
} and TH
}. We dene the fol-
lowing quantities:
{m} =
{m} =
{m}, (m=1, ...,
). (46)
Suppose that the multicanonical weight factor Wmu(E) (or
equivalently, the multicanonical potential energy Emu(E; T0)
in Eq. (18)) has been obtained as in REMUCA or by any
other methods in the entire energy range of interest
}). We then have for each m (m=1, ...,
) the
following multicanonical potential energies (see Eq. (42)):
{m}(E) =
{m}; Tm)
{m}; Tm), for E < EL
Emu(E; Tm), for EL
{m}; Tm)
{m}; Tm), for E > EH
Finally, a MUCAREM simulation is realized by alternately
performing the following two steps.
1. Each replica of the xed multicanonical ensemble is
simulated simultaneously and independently for a certain
MC or MD steps.
2. A pair of replicas, say i and j, which are in neighboring
multicanonical ensembles, say m-th and (m+1)-th, respec-
Okamoto: Enhanced sampling methods 351
considered as a multidimensional extension of the original
replica-exchange method where the “parameter space” is
one-dimensional (i.e., Λm=Tm). Because the replicas are
non-interacting, the weight factor for the state X in this
new generalized ensemble is again given by the product of
Boltzmann factors for each replica (see Eq. (28)):
WMREM(X) = exp
βm(i)Hm(i)(q[i], p[i])
= exp
βmHm(q[i(m)], p[i(m)])
, (54)
where i(m) and m(i) are the permutation functions in Eq.
(26). Then the same derivation that led to the original replica-
exchange criterion follows, and the transition probability of
replica exchange is given by Eq. (33), where we now have
(see Eq. (34)) [94]
∆ = βm(Eλm(q[j]) Eλm(q[i])) βn(Eλn(q[j]) Eλn(q[i])). (55)
Here, Eλm and Eλn are the total potential energies (see Eq. (53)).
Note that we need to newly evaluate the potential energy for
exchanged coordinates, Eλm(q[j]) and Eλn(q[i]), because Eλm and
Eλn are in general different functions.
We remark that MUCAREM in the previous subsection
is a special case of MREM. We also remark that a general
formalism for multidimensional generalized-ensemble algo-
rithms was presented in Refs. [138,139].
Parallel Monte Carlo or molecular dynamics using
genetic crossover
Besides generalized-ensemble algorithms explained above,
we have also proposed conformational search method using
genetic crossover (PSA/GAc) [140–142]. While genetic
algorithm is usually used for nding the global-minimum
energy state, we have proposed to use genetic crossover to
just introduce global update of conformations to enhance
conformational sampling in conventional MC or MD simula-
tions [143–145]. We refer to these methods as parallel Monte
Carlo using genetic crossover (PMC/GAc) and parallel
molecular dynamics using genetic crossover (PMD/GAc).
In Figure 1, we show the ow charts of the PMC/GAc
or PMD/GAc method [143–145] and the original GA for
We rst prepare M initial conformations of the system in
study, where M is the total number of ‘individuals’ in GA
and is usually taken to be an even integer. We then alter-
nately perform the following two steps:
1. For the M individuals, regular canonical MC or MD sim-
ulations at temperature T are carried out simultaneously
and independently for certain MC or MD steps.
2. M/2 pairs of conformations are selected from “parental”
group randomly, and the crossover operation is performed
to create “child” conformations. The obtained “child”
potential energy) at any temperature T (=1/kBβ) can now
be obtained from the “trajectory” of congurations of the
production run. Namely, we rst obtain fm (m=1, ...,
) by
solving Eqs. (50) and (51) self-consistently, and then we
have [74]
T =
nexp( f)Wmu
nexp( f)Wmu
where the trajectories xm(k) (k=1, ..., nm) are taken from each
multicanonical simulation with the multicanonical weight
factor Wmu
{m}(E) (m=1, ...,
) separately.
As seen above, both REMUCA and MUCAREM can be
used to obtain the multicanonical weight factor, or the den-
sity of states, for the entire potential energy range of inter-
est. For complex systems, however, a single REMUCA or
MUCAREM simulation is often insufcient. In such cases
we can iterate MUCA (in REMUCA) and/or MUCAREM
simulations in which the estimate of the multicanonical
weight factor is updated by the single- and/or multiple-
histogram reweighting techniques, respectively [75].
Multidimensional replica-exchange method
We now present our multidimensional extension of REM,
which we refer to as multidimensional replica-exchange
method (MREM) [94]. (The method is also referred to as
Hamiltonian replica-exchange method [95], generalized
parallel sampling [129], and Model Hopping [130].) Some
other examples of multidimensional generalized-ensemble
algorithms can be found in, e.g., Refs. [120,131–137]. Another
special realization of MREM is replica-exchange umbrella
sampling (REUS) [94] and it is particularly useful in free
energy calculations (see also Ref. [96] for a similar idea).
REUS can be applied to ligand docking simulations and free
energy calculations of ligand binding afnity [97–99].
The crucial observation that led to the new algorithm is:
As long as we have M noninteracting replicas of the original
system, the Hamiltonian H(q, p) of the system does not
have to be identical among the replicas and it can depend
on a parameter with different parameter values for different
replicas. Namely, we can write the Hamiltonian for the i-th
replica at temperature Tm as
Hm(q[i], p[i]) = K(p[i]) + Eλm(q[i]). (53)
While replica i and temperature Tm are in one-to-one
correspondence in the original REM, replica i and “parame-
ter set” Λm≡(Tm, λm) are in one-to-one correspondence in
the new algorithm. Hence, the present algorithm can be
352 Biophysics and Physicobiology Vol. 16
We need to deal with the produced “child” conformations
with care. Because the produced preliminary conformations
often have unnatural structures by the crossover operation,
they have high potential energy and are unstable. This is
particularly a serious problem for simulations with explicit
solvent and lipid molecules. Therefore, a propagation pro-
cess is introduced before the selection operation. As the
propagation process, we perform a short MC or MD simu-
lation with restraint potentials Erst(θ) of the (backbone)
dihedral angle θ in the selected n amino acids as follows:
Erst(θ) = kθ(θ θchild)2 (57)
where kθ is the force constant, and θchild is a dihedral angle
proposed by exchanging dihedral angles between “parent”
conformations by the crossover operation. The initial con-
formations for these propagation simulations are the ones
before the crossover. Namely, by these propagation simula-
tions, the corresponding backbone conformations of the n
amino acids gradually transform from the ones before the
crossover to the ones after the crossover.
We remark that PMC/GAc and PMD/GAc can be com-
bined with REM for further enhancement of conformatonal
space [144].
conformations are “selected” or accepted from the parents
with the following Metropolis criterion:
w(p c) = min (1, exp{−β[Ec Ep]}), (56)
where Ep and Ec stand for the potential energy of the
parental conformation and the nal child conformation of
the parent-child pair, respectively.
In Step 2, we can employ various kinds of GAc operations.
Here, we just present a case of the two-point crossover [142].
The following procedure is carried out (see Fig. 2):
1. Consecutive amino acids of length n residues in the
amino- acid sequence of the conformation are selected
randomly for each pair of selected conformations.
2. Dihedral angles (in only backbone or all dihedral angles)
in the selected n amino acids are exchanged between the
selected pair of conformations.
Note that the length n of consecutive amino-acid residues
can, in general, be different for each pair of selected confor-
mations. Motivated by the fragment assembly method [146],
we take n to be an integer ranging from 2 to 10.
Figure 1
Flow charts of general genetic algorithm (left side) and PMC/GAc or PMD/GAc (right side).
Okamoto: Enhanced sampling methods 353
this multicanonical weight factor into four multicanonical
weight factors that cover different energy regions [73–75]
and assigned these multicanonical weight factors into four
replicas (the weight factors cover the potential energy ranges
from −13791.5 to −11900.5
kcal/mol, from −12962.5 to
Examples of Simulation Results
The rst example is the C-peptide of ribonuclease A
in explicit water [85]. In the model of simulations, the
N-terminus and the C-terminus of the C-peptide analogue
were blocked with the acetyl group and the N-methyl group,
respectively. The number of amino acids is 13 and the
amino- acid sequence is: Ace-Ala-Glu-Thr-Ala-Ala-Ala-
Lys+-Phe-Leu-Arg+-Ala-His+-Ala-Nme [147,148]. The initial
conguration of our simulation was rst generated by a high
temperature molecular dynamics simulation (at T=1000
in gas phase, starting from a fully extended conformation.
We randomly selected one of the structures that do not have
any secondary structures such as α-helix and β-sheet. The
peptide was then solvated in a sphere of radius 22
Å, in
which 1387 water molecules were included (see Fig. 3).
Harmonic restraint was applied to prevent the water mole-
cules from going out of the sphere. The total number of
atoms was 4365. The dielectric constant was set equal to 1.0.
The force-eld parameters for protein were taken from the
all-atom version of AMBER parm99 [151], which was found
to be suitable for studying helical peptides [30], and TIP3P
model [155] was used for water molecules. The unit time
step, ∆t, was set to 0.5
In Table 1 the essential parameters in the simulations per-
formed in this example are summarized.
We rst performed a REMD simulation with 32 replicas
for 100
psec per replica (REMD1 in Table 1). During this
REMD simulation, replica exchange was tried every 200
MD steps. Using the obtained potential-energy histogram of
each replica as input data to the multiple-histogram analysis
in Eqs. (4) and (5), we obtained the rst estimate of the mul-
ticanonical weight factor, or the density of states. We divided
Figure 2
Schematic process of the two-point crossover operation. In this process, all dihedral angles (in backbone and side chains) within the
randomly selected n consecutive amino acids are exchanged between a pair of conformations.
Figure 3
The initial conguration of C-peptide in explicit water,
which was used in all of the 32 replicas of the rst REMD simulation
(REMD1 in Table 1). The red lled circles stand for the oxygen atoms
of water molecules. The number of water molecules is 1387, and they
are placed in a sphere of radius 22
Å. As for the peptide, besides the
backbone structure (in blue), side chains of only Glu-2, Phe-8, Arg+-10,
and His+-12 are shown (in yellow). The gure was created with
Molscript [149] and Raster3D [150].
354 Biophysics and Physicobiology Vol. 16
analysis (PCA) [156–158] (for a review, see Ref. [159]). In
Figure 6 the potential of mean force (PMF), or free energy,
along the rst two principal component axes at 300
K is
shown. There exist three distinct minima in the free-energy
landscape, which correspond to three local-minimum- energy
states. We show representative conformations at these
minima in Figure 7. The structure of the global-minimum
free-energy state (GM) has a partially distorted α-helix with
the salt bridge between Glu-2 and Arg+-10. The structure is
in good agreement with the experimental structure obtained
by both NMR and X-ray experiments. In this structure there
also exists a contact between Phe-8 and His+-12. This con-
tact is again observed in the corresponding residues of the
X-ray structure. At LM1 the structure has a contact between
Phe-8 and His+-12, but the salt bridge between Glu-2 and
Arg+-10 is not formed. On the other hand, the structure at
LM2 has this salt bridge, but it does not have a contact
kcal/mol, from −11900.5 to −9524.5
kcal/mol, and
from −10796.5 to −8293.5
kcal/mol). We then carried out
a MUCAREM simulation with four replicas for 1
nsec per
replica (MUCAREM1 in Table 1), in which replica exchange
was tried every 1000 MD steps. We again used the potential-
energy histogram of each replica as the input data to the
multiple- histogram analysis and nally obtained the multi-
canonical weight factor with high precision. As a production
run, we carried out a 15
nsec multicanonical MD simulation
with one replica (REMUCA1 in Table 1) and the results of
this production run were analyzed in detail.
In Figure 4 we show the probability distributions of
potential energy that were obtained from the above three
generalized- ensemble simulations, namely, REMD1,
MUCAREM1, and REMUCA1. We see in Figure 4(a) that
there are enough overlaps between all pairs of neighboring
canonical distributions, suggesting that there were suf-
cient numbers of replica exchange in REMD1. We see in
Figure 4(b) that there are good overlaps between all pairs
of neighboring multicanonical distributions, implying that
MUCAREM1 also performed properly. Finally, the multi-
canonical distribution in Figure 4(c) is completely at between
around −13000
kcal/mol and around −8000
kcal/mol. The
results suggest that a free random walk was realized in this
energy range.
In Figure 5a we show the time series of potential energy
from REMUCA1. We indeed observe a random walk cover-
ing as much as 5000
kcal/mol of energy range (note that
eV). We show in Figure 5(b) the average
potential energy as a function of temperature, which was
obtained from the trajectory of REMUCA1 by the reweight-
ing techniques. The average potential energy monotonically
increases as the temperature increases.
We have analyzed the data by the principal component
Figure 4
Probability distributions of potential energy of the
C-peptide system obtained from (a) REMD1, (b) MUCAREM1, and
(c) REMUCA1. See Table 1 for the parameters of the simulations.
Dashed curves in (c) are the reweighted canonical distributions at 290,
300, 500, and 700
K (from left to right).
Table 1
Summary of parameters in REMD, MUCAREM,
and REMUCA simulations
Number of
replicas, M
Tm (K) (m=1, ..., M)
MD steps
per replica
250, 258, 267, 276,
286, 295, 305, 315,
326, 337, 348, 360,
372, 385, 398, 411,
425, 440, 455, 470,
486, 502, 519, 537,
555, 574, 593, 613,
634, 655, 677, 700
MUCAREM1 4 360, 440, 555, 700 2.0×106
REMUCA1 1 700 3.0×107
* REMD1 stands for the replica-exchange molecular dynamics
simulation, MUCAREM1 stands for the multicanonical replica-
exchange molecular dynamics simulation, and REMUCA1 stands for
the nal multicanonical molecular dynamics simulation (the produc-
tion run) of REMUCA. The results of REMD1 were used to determine
the multicanonical weight factors for MUCAREM1, and those of
MUCAREM1 were used to determine the multicanonical weight
factor for REMUCA1.
Okamoto: Enhanced sampling methods 355
3513. The computer code developed in Refs. [73,92,160,161],
which is based on the version 2 of PRESTO [162], was used
after modication for calculatoin with the CHARMM force
eld. The MD time step was 1.0
fsec. We made two produc-
tion runs of about 1
μsec, each of which was a MUCAREM
simulation with eight replicas. They are referred to as
MUCAREM1 and MUCAREM2. The former consisted of
μsec covering the temperature range between 269
and 699
K, and the latter 1.157
μsec covering the tempera-
ture range between 289
K and 699
In Figure 8 we show the time series of the mainchain
root-mean-square deviation (RMSD) during MUCAREM1
We consider that the backbone folded into the native
structure from unfolded ones if the mainchain RMSD
becomes ≤3.0
Å. The folding event is counted separately if
it goes through an unfolded structure (with the backbone
RMSD ≥6.5
Å). With this criterion, we observed 11 folding
events in seven different replicas (namely, Replicas 5, 7,
and 8 in MUCAREM1 and Replicas 1, 2, 4, and 5 in
In Figure 9 we show the snapshots of the replicas folding
into native-like conformations for the two MUCAREM
production runs.
In Figure 10 we compare the obtained low-RMSD confor-
mations and the native structure. They are indeed very close
to the native structure.
The third example is a REM MC simulation for the pre-
diction of membrane protein structures [163]. Here, we used
another implicit membrane model [164–169], where four
elementary harmonic restraints are added to the original
CHARMM potential energy function in order to mimic
restrained membrane environment. Only the transmembrane
helices are used in our simulations, and loop regions of the
membrane proteins as well as lipid and water molecules
were neglected. While membrane environment enhances
the stability of helix structures, it also restricts sampling in
between Phe-8 and His+-12. Thus, only the structures at GM
satisfy all of the interactions that have been observed by the
X-ray and other experimental studies.
The second example is a MUCAREM simulation of fold-
ing of a small protein [127]. The system that we simulated
is chicken villin headpiece subdomain in explicit water. The
number of amino acids is 36. The force eld CHARMM22
[152] with CMAP [153,154] and TIP3P water model
[152,155] were used. The number of water molecules was
Figure 6
Potential of mean force (kcal/mol) of the C-peptide
system along the rst two principal components at 300
K. The free
energy was calculated from the results of REMUCA production run
(REMUCA1 in Table 1) by the single-histogram reweighting tech-
niques and normalized so that the global-minimum state (GM) has the
value zero. GM, LM1, and LM2 represent three distinct minimum
free-energy states.
Figure 5
Time series of potential energy of the C-peptide system from the REMUCA production run (REMUCA1 in Table 1) (a) and the aver-
age potential energy as a function of temperature (b). The latter was obtained from the trajectory of REMUCA1 by the single-histogram reweighting
356 Biophysics and Physicobiology Vol. 16
their functions. Thus, we also focus our interests in this work
on the reproduction of their distortions in larger proteins
such as bacteriorhodopsin which has seven distorted helices.
To solve this problem we extended the above method to
include the exible treatment of backbone structures and
reproduced small membrane protein structures: glycophorin
A of a transmembrane helix dimer and phospholamban of
a distorted transmembrane helix [169].
Our method for membrane structure predictions is as
follows. We rst obtain the amino-acid sequences of trans-
membrane helices of the target protein by bioinformatics
servers such as SOSUI [174], TMHMM [175], MEMSAT
[176], and HMMTOP [177]. In the present work, however,
the amino-acid sequences of transmembrane helices were
taken from the experimental structure as in the previous
work [167,168]. Different servers may predict different
conformational space in the membrane region. Thus, this
model greatly reduces the search area in the conformational
space during folding processes. This model is supported by
many experimental data such as two-stage model (for a
review, see Ref. [170]). As for a simulation method, we
employed the replica-exchange Monte Carlo (MC).
Although the previous method treated helix structures as
rigid bodies and required the known helix structures from
the native structure, this approach reproduced nativelike
structures of bacteriorhodopsin [167,168], which has seven
transmembrane helices with 249 amino acids and a retinal
from Halobacterium salinarum (PDB ID: 1IW6 [171], 1PY6
[172], 1BRR [173]). This protein has a function of proton
pump in bio-membrane with excitations of the retinal mole-
cule by light. However, 60% of all transmembrane helix
structures are distorted in PDB, which seems to be related to
Figure 7
The representative structures at the global-minimum free-energy state ((a) GM) and the two local-minimum states ((b) LM1 and
(c) LM2). As for the peptide structures, besides the backbone structure, side chains of only Glu-2, Phe-8, Arg+-10, and His+-12 are shown in ball-
and-stick model.
Figure 8
Time series of the backbone RMSD from the native structure of villin headpiece during MUCAREM1 (left) and MUCAREM2 (right).
Okamoto: Enhanced sampling methods 357
and replica-exchange Monte Carlo method was implemented
in it. Only the transmembrane helices were used in our sim-
ulations, and loop regions of the membrane proteins as well
as lipid and water molecules were neglected. The membrane
environment for this protein for the membrane thickness
and the region of transmembrane region of the helices was
taken from Orientation of Proteins in Membrane (OPM)
[180]. The amino-acid sequences of the transmembrane
helix ends. In order to study end dependence we also made
shorter simulations with a little shorter helices and con-
rmed that we obtained similar results. We then perform
REM simulations of these transmembrane helices. Our pro-
cedure for membrane protein structure predictions is sum-
marized in Figure 11 [169]. The MC program is based on
CHARMM macromolecular mechanics program [178,179],
Figure 9
Snapshots of villin headpiece during the MUCAREM production runs that folded into native-like conformations: MUCAREM1
(above) and MUCAREM2 (below).
Figure 10
Low-RMSD conformations of villin headpiece obtained
in MUCAREM1 and MUCAREM2 (colored in orange). The X-ray
structure (PDB ID: 1YRF) is also superimposed (colored in blue and
green). Here, the α-helices in the X-ray structure are colored in green
and the rest in blue. Three phenylalanine side chains (Phe7, Phe11, and
Phe18), which form a hydrophobic core, are shown in ball-and-stick
representation. (a) The lowest-backbone-RMSD conformation observed
in the two MUCAREM production runs (Replica 5 of MUCAREM2).
The backbone RMSD value is 1.1
Å (for non-terminal 34 residues).
(b) A low-RMSD conformation observed in MUCAREM1 (Replica 8).
The RMSD value is 1.0
Å for residues 9 to 32 and 3.3
Å for non-termi-
nal 34 residues.
Figure 11
Our prediction procedure for membrane protein
358 Biophysics and Physicobiology Vol. 16
boundary z-coordinate value of the membrane planes, respec-
tively, and here they depend on each helix atoms due to the
known data from OPM [180] although constant membrane
plane region is also possible like a previous research condi-
tion. dL and dU are the corresponding central value constants
of the harmonic restraints. This term has a non-zero value
only when the Cα atoms of the N-terminus or C-terminus of
the i-th helix are apart more than di
L (or di
U). This restraint
energy was introduced so that the helix ends are not too
much apart from the membrane boundary planes.
Erestr3 is the energy that restrains all Cα atoms within the
sphere (centered at the origin) of radius dCα. rCα is the distance
of Cα atoms from the origin, and k3 and dCα are the force con-
stant and the central value constant of the harmonic restraints,
Erestr4 is the energy that restraints the dihedral angles of the
main chain so that helix structures may not deviate too much
from ideal helix structures, preventing them from forming
random-coil structures. NBD is the total number of (ϕ, ψ)
angles in the helix backbones. Here, all the backbone dihe-
dral angles ϕj and ψj (j= 1, ..., NBD) are restrained. ϕ0 and ψ0
are the reference value of the harmonic restraint to keep the
helix structures without forming random coil structure, and
ϕ, αj
ψ are the anges of the harmonic restraints.
We set k1= 5.0, di,i+1 =(46, 53, 34, 19, 95, 30) where
i=1, 2, ..., 6, k2=5.0, zL
0,i = (−14, −16, −20, −15, −19, −24,
−18) where i=1, 2, ..., 7, zU
0,i = (12, 14, 15, 15, 14, 11, 12)
where i=1, 2, ..., 7, dU=dL=2.0, k3=0.5, dCα= 80, k4=30.0,
k5=30.0, ϕ0=−62, ψ0=−40, αj
ϕ =16, and αj
ψ =13.
We used 40 replicas and the following temperatures: 400,
415, 435, 455, 485, 518, 552, 589, 629, 671, 716, 764, 815,
870, 928, 990, 1056, 1127, 1202, 1283, 1369, 1460, 1558,
1662, 1774, 1892, 2019, 2154, 2298, 2452, 2616, 2791,
2978, 3177, 3390, 3616, 3808, 4050, 4250, and 4500
K. We
remark that because short simulations suggested that the
choice of the minimum temperature of 300
K did not change
overall helix orientations compared to 400
K, we set the
minimum temperature to 400
K instead of 300
K to reduce
the number of replicas. We used rather high temperature val-
ues compared to experimental conditions. This is because
our implicit membrane model guarantees the helix stability
and enhances conformational sampling. Replica exchange
was attempted at every 50 MC steps. We performed four
independent simulations in total of 1,363,925,000 MC steps
(the number of MC steps in each simulation was 308,000,000,
250,000,000, 343,200,000, and 462,775,000 MC steps).
We used the CHARMM19 parameter set (polar hydrogen
model) for the potential energy of the system [178,179]. No
cutoff was introduced to the non-bonded terms. Each helix
structure was rst minimized subjected to harmonic restraint
on all the heavy atoms. In order to prepare random initial
conformations, we rst performed regular constant tempera-
ture MC simulations of all the replicas for 3,000,000 MC
steps at 4500
K. We then performed equilibrium MC simula-
tion for 3,000,000 MC steps at the above 40 temperatures,
which are the same as in Refs. [167,168], where two
numbers in parentheses after each sequence correspond to
the rst and last amino-acid numbers of each helix. The
N-terminus and the C-terminus of each helix were blocked
with the acetyl group and the N-methyl group (rst residue
and last residue), respectively. The initial structure for each
helix was an ideal helix structure and they were placed in the
membrane region randomly. We added the following four
elementary harmonic restraints as a simple implicit mem-
brane model to the original CHARMM potential energy
function of bond length, bond angle, torsion angle, van der
Waals, electrostatic interaction, etc. in order to mimic the
restrained membrane environment. The restraint energy
function is given by
Erestr = Erestr1 + Erestr2 + Erestr3 + Erestr4 , (58)
where each term is dened as follows:
Erestr1 =
k1 θ(ri,i+1 di,i+1)[ri,i+1 di,i+1]2, (59)
Erestr2 =
{k2 θ(|zi
L zL
0,i| dL)[|zi
L zL
0,i| dL]2
+ k2 θ(|zi
U zU
0,i| dU)[|zi
U zU
0,i| dU]2}, (60)
Erestr3 =
k3 θ(rCα dCα)[rCα dCα]2, (61)
Erestr4 =
k4 θ(|ϕj ϕ0| αj
ϕ)[|ϕj ϕ0| αj
k5 θ(|ψj ψ0| αj
ψ)[|ψj ψ0| αj
ψ]2. (62)
Erestr1 is the energy that restrains pairs of adjacent helices
along the amino-acid chain not to be apart from each other
too much (loop restraints), where ri,i+1 is the distance between
the C atom of the C-terminus of the i-th helix and the Cα
atom of the N-terminus of the (i+1)-th helix, and k1 and di,i+1
are the force constant and the central value constant of the
harmonic restraints, respectively. Each di,i+1 is proportional
to the loop length connected between helices. θ(x) is the step
function, which has 1 when x is larger than or equal to 0,
otherwise zero. NH is the total number of transmembrane
helices in the protein.
Erestr2 is the energy that restrains helix N-terminus and
C-terminus to be located near membrane boundary planes.
Here, the z-axis is dened to be the direction perpendicular
to the membrane boundary planes. k2 is the force constant
of the harmonic restraints. zL
0,i and zU
0,i are the z-coordinate
values of the Cα atom of the N-terminus or C-terminus of
the i-th helix near the xed lower membrane boundary and
the upper membrane boundary, respectively. zL
0,i and zU
0,i are
the xed lower boundary z-coordinate value and the upper
Okamoto: Enhanced sampling methods 359
similar structures and at the temperature of 400
K. The rows
of Cluster 1, Cluster 2, Cluster 3, Cluster 4 and Cluster 5
represent various average values for the structures that
belong to each cluster. The number of structures in each
cluster (the total number was 42238 structures) was 9123,
13146, 7457, 5121, and 4418 for Cluster 1, Cluster 2, Cluster
3, Cluster 4, and Cluster 5, respectively. Thus, the global-
minimum free energy state is Cluster 2, and the second-
lowest minimum state is Cluster 1.
Figure 13 shows the representative structure in each clus-
ter from the highest density region. The root-mean-square-
deviation (RMSD) value of each representative structure
with respect to the Cα atoms was 3.6
Å, 8.8
Å, 15.8
Å, 15.9
and 16.6
Å for Cluster 1, Cluster 2, Cluster 3, Cluster 4, and
Cluster 5, respectively. From these RMSD values, we see
that the native-like structure is the second-lowest free energy
state (Cluster 1) and that the global-minimum free energy
state (Cluster 2) is the second closest to the native structure.
In the structure of Cluster 2, the space where the retinal mol-
ecule occupies in the native structure is lled with a helix,
and the last conformation for each replica was the initial
structure for the REM simulations. We repeated this process
four times for four independent REM simulations with dif-
ferent seeds. In those simulations, the dielectric constant was
set to ϵ=1.0 as in the previous works [164–169], because
these works conrmed that the results with ϵ= 1.0 value were
in better agreement with the experimental structures than
ϵ=4.0. The interpretation was that in the native structure
there are few lipid molecules between helices. In MC
move, we updated conformations with a rigid translation
and rotation of each α-helix, a rotation of torsion angles of
backbones by directional manipulation and concerted rota-
tion [181–183], and torsion rotations of side-chains. There
are 2NH+NSD+NBD+NCR kinds of MC moves, where NSD is
the total number of dihedral angles in the side-chains of NH
helices and NCR is the total number of the combination of
seven successive backbone torsion angles by the concerted
rotation in the helix backbone. One MC step in this article is
dened to be an update of one of these degrees of freedom,
which is accepted or rejected according to the Metropolis
We investigated the free energy landscape obtained by the
principal component analysis. We classied the sampled
structures at the minimum temperature of 400
K into clusters
of similar structures by the k-means clustering method [184].
Although we can express the system more accurately as
we use more principal axes in k-means clustering, we here
classify and analyze the sampled structures at the lowest
temperature by the rst three principal components. In
Figure 12, the projection of sampled structures from the
REM simulations at 400
K on the rst, second, and third
principal component axes. We obtained ve distinct clusters
of similar structures. If we perform constant temperature
simulations at the lowest temperature, the simulations will
get trapped in any of the clusters in Figure 12, depending on
the initial conformations of the simulations. However, each
replica during the REM simulations did not get trapped in
one of the local-minimum free energy states, by going
through high temperature regions. Every replica could over-
come energy barriers at higher temperatures during the
simulations. This is the advantage of the replica-exchange
method. Table 2 lists average quantities of ve clusters of
Figure 12
Projection of sampled structures at temperature 400
on the rst, second, and third principal axes from the REM simulations.
Structures are classied into clusters of similar structures by k-means
method and analyzed in detail. Clusters are highlighted by different
colors: red, blue, yellow, green, and black. PCA1, PCA2, and PCA3
represent the principal component axes 1, 2, and 3, respectively.
Table 2
Various average quantities for each cluster and at the temperature of 400
Str Etot Eelec Evdw Edih Egeo RMSD
Cluster 1 9123 −7587±355 −7006±51 −1197±83 202 ±14 106±215 6.2±5.6
Cluster 2 13146 −7422±182 −6976±45 −1166±44 213±11 147±123 12.2±2.4
Cluster 3 7457 −7287±210 −6943±31 −1143±71 211±10 229± 142 14.9±2.5
Cluster 4 5121 −7421±109 −7010±36 −1145±43 213 ±10 160±71 16.3±1.1
Cluster 5 4418 −7300±309 −6954±41 −1096±67 207±11 184±258 16.7±1.0
K 42240 −7412±263 −6979±48 −1158±68 210±12 166±170 12.5 ±4.8
The following abbreviations are used: Str: number of structures, Etot: average total potential energy, Eelec: average electrostatic energy, Evdw: average
Lennerd-Jones energy, Edih: average dihedral energy, Egeo: average constraint energy (all in kcal/mol), RMSD: average root-mean-square deviation
of all Cα atoms (in Å).
360 Biophysics and Physicobiology Vol. 16
larger RMSD values came from the neglect of the retinal
molecule in our simulations.
In order to study bendings of helices more quantitatively,
we analyzed the data by the software HELANAL-Plus. They
are in good agreement between PDB and Cluster 1 struc-
tures. Most properties of helices are similar between them,
and because of the similarity in maximum bending and
average bending with standard errors, this suggests that the
tendency of bending was highly reproduced, although over-
all helix geometry is assigned differently (L vs. K) in Helix
D, Helix E, and Helix G. However, there is disagreement in
data about the maximum bending location in Helix D (85T
for PDB and 77G for Cluster 1). This seems to result from
the lack of interactions with the retinal molecule because the
retinal binding positions are 79D, 82M, and 86G near the
C-terminus side.
These results imply that the position of the kinks of helices
are determined by their amino-acid sequences and that the
amount of bends of helices are determined by the inter-
actions with surrounding molecules such as other helices,
retinal, and lipid molecules.
The fourth example is a PMD/GAc simulation of a small
and this increases the contact between helices and seems to
stabilize this structure more than the native-like structure of
Cluster 1 with the empty space for the retinal molecule.
Moreover, this result that a helix occupies the retinal space is
consistent with previous works [167,168] which did not
include the exibility of helix structures. However, the pre-
vious works were not able to obtain the native-like structure
such as Cluster 1. Hence, the extension of including the free-
dom of helix structure distortion has improved the accuracy
of prediction for membrane protein structure determination
by simulation. Our results suggest that in the simulations
without a retinal molecule the structures can interchange
between the structures of Cluster 1 and Cluster 2. After an
insertion of a retinal, it then stabilizes the native-like struc-
ture. It is important that the association of helices enabled
them to make a room for an insertion of a retinal molecule.
This is consistent with the experimental results of bacterior-
hodopsin, which observed the spontaneous insertion of a
retinal molecule by a helix association [185].
We now examine the distortions of each helix of native-
like structure in Cluster 1. They are compared with the native
structures in Figure 14. Note that the positions of the kinks
of helices are all correctly reproduced. The RMSD values
with respect to backbone atoms are 0.7
Å, 0.9
Å, 1.6
Å, 1.1
Å, 2.2
Å, and 0.4
Å for Helix A, Helix B, Helix C,
Helix D, Helix E, Helix F, and Helix G, respectively. These
helix structures reproduced the bending of helices with
smaller RMSD values for the helices apart from the retinal
molecule in the native state (Helices A, B, and G), whereas
the helices with larger RMSD values of about 2.0
Å are for
the helices in close contact with the retinal in the native
structure (Helices C, D, E, and F). They suggest that the
Figure 13
Typical structures in each cluster selected in the highest
density region. The RMSD from the native conformation with respect
to all Cα atoms is 3.6
Å, 8.8
Å, 15.8
Å, 15.9
Å, and 16.6
Å for Cluster 1,
Cluster 2, Cluster 3, Cluster 4, and Cluster 5, respectively. Helices are
colored from the N-terminus to the C-terminus: blue (Helix A), light-
blue (Helix B), green (Helix C), deepgreen (Helix D), yellow (Helix E),
orange (Helix F), and red (Helix G).
Figure 14
Comparison of distortions of each helix structure in
Cluster 1 with the native helix structure by two different representation
methods, (a) cartoon and (b) ribbon. The transparent structures are the
native structures. The RMSD from the native conformation with
respect to backbone atoms is 0.7
Å, 0.9
Å, 1.6
Å, 2.7
Å, 1.1
Å, 2.2
and 0.4
Å for Helix A, Helix B, Helix C, Helix D, Helix E, Helix F, and
Helix G, respectively. The coloring of helices is the same as in Figure
Okamoto: Enhanced sampling methods 361
namely, REMUCA, MUCAREM, and MREM. We also
presented PMC/GAc and PMD/GAc, which are based on
genetic crossover.
These methods can be used not only to predict the tertiary
structures of proteins but also to calculate free energy land-
scapes of protein folding and protein-ligand docking, etc.
The author thanks his co-workers for useful discussions.
In particular, he is grateful to Drs. Ulrich H.
E. Hansmann,
Tomoyuki Hiroyasu, Katsuya Ishii, Akio Kitao, Hironori
Kokubo, Mitsunori Miki, Ayori Mitsutake, Yoshitake Sakae,
John E. Straub, Yuji Sugita, Ryo Urano, and Takao Yoda
for collaborations that led to the results presented in the
present article. Some of the results were obtained by the
computations on the super computers at Nagoya University
Supercomputer Center, the Institute for Molecular Science,
Okazaki, and the Institute for Solid State Physics, University
of Tokyo, Japan.
Conict of Interest
The author declares no conict of interest.
Author Contributions
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In this Festschrift Issue for the celebration of Professor
Nobuhiro Gō’s 80th birthday, we reviewed enhanced confor-
mational sampling methods for protein structure predictions.
We rst introduced two well-known generalized-ensemble
algorithms, namely, MUCA and REM, which can greatly
enhance conformational sampling of biomolecular systems.
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Figure 15
Structures of protein A. (a) PDB structure (PDB ID:
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which has the lowest RMSD value from the PDB structure
(RMSD= 1.7
362 Biophysics and Physicobiology Vol. 16
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... The content will be organized as follows. Part 2 describes fundamental challenges in molecular modeling; Part 3 summarizes application of these two fundamental algorithmic principles in two lines of methodological research, coarse graining (CG) [18][19][20][21][22][23][24][25][26][27] and enhanced sampling (ES) [28][29][30][31]; Part 4 covers how machine learning, particularly deep learning, facilitates DC and "caching" in CG and ES [29,30,[32][33][34][35], Part 5 introduces local free energy landscape (LFEL) approach, a new framework for computational molecular science based on partially transferable in resolution "caching" of local sampling. The first implementation of this new framework in protein structural refinement based on generalized solvation free energy (GSFE) theory [36] is briefly discussed; and Part 6 discusses connections among these three lines of algorithmic development, their specific advantages and prospective explorations. ...
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Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes “dividing and conquering” and/or “caching” in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of “dividing and conquering” and “caching” along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution “caching” of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for “dividing and conquering” and “caching” in complex molecular systems.
Along with various experimental methods, a combination of theoretical and computational methods is essential to explore different length-scale and time-scale processes in the biological system. The functional mechanism of dynein,...
We propose a molecular simulation method using genetic algorithm (GA) for biomolecular systems to obtain ensemble averages efficiently. In this method, we incorporate the genetic crossover, which is one of the operations of GA, to any simulation method such as conventional molecular dynamics (MD), Monte Carlo, and other simulation methods. The genetic crossover proposes candidate conformations by exchanging parts of conformations of a target molecule between a pair of conformations during the simulation. If the candidate conformations are accepted, the simulation resumes from the accepted ones. While conventional simulations are based on local update of conformations, the genetic crossover introduces global update of conformations. As an example of the present approach, we incorporated genetic crossover to MD simulations. We tested the validity of the method by calculating ensemble averages and the sampling efficiency by using two kinds of peptides, ALA3 and (AAQAA)3. The results show that for ALA3 system, the distribution probabilities of backbone dihedral angles are in good agreement with those of the conventional MD and replica‐exchange MD simulations. In the case of (AAQAA)3 system, our method showed lower structural correlation of α‐helix structures than the other two methods and more flexibility in the backbone ψ angles than the conventional MD simulation. These results suggest that our method gives more efficient conformational sampling than conventional simulation methods based on local update of conformations. © 2018 Wiley Periodicals, Inc.
In GAMESS-US quantum chemistry package, both replica-exchange molecular dynamics (REMD) and the umbrella sampling (US) methods are available. However, prior to this work, the replica-exchange umbrella sampling (REUS) method, combining REMD and US, was not implemented. REUS affords a more efficient conformational sampling than the conventional US method and is often used in MD simulations. We implemented REUS in GAMESS-US and improved some routines of the existing REMD code. A density-functional tight-binding REMD (DFTB-REMD) simulation of alanine deca-peptide and a DFTB-MD REUS simulation of proton transfer in malonaldehyde were performed. The results of DFTB-MD REUS simulations performed with three different simulation packages were compared to validate our GAMESS-US implementation.
In complex systems with many degrees of freedom such as peptides and proteins, there exists a huge number of local-minimum-energy states. Conventional simulations in the canonical ensemble are of little use, because they tend to get trapped in states of these energy local minima. A simulation in generalized ensemble performs a random walk in potential energy space and can overcome this difficulty. From only one simulation run, one can obtain canonical-ensemble averages of physical quantities as functions of temperature by the single-histogram and/or multiple-histogram reweighting techniques. In this article we review uses of the generalized-ensemble algorithms in biomolecular systems. Three well-known methods, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described first. Both Monte Carlo and molecular dynamics versions of the algorithms are given. We then present three new generalized-ensemble algorithms that combine the merits of the above methods. The effectiveness of the methods for molecular simulations in the protein folding problem is tested with short peptide systems. © 2001 John Wiley & Sons, Inc. Biopolymers (Pept Sci) 60: 96–123, 2001
Transitions between metastable states govern many fundamental processes in physics, chemistry, and biology, from nucleation events in phase transitions to the folding of proteins. The free energy surfaces underlying these processes can be obtained from simulations using enhanced sampling methods. However, their altered dynamics makes kinetic and mechanistic information difficult or impossible to extract. Here we show that with replica-exchange molecular dynamics (REMD), one can not only sample equilibrium properties, but also extract kinetic information. For systems that strictly obey first-order kinetics, the procedure to extract rates is rigorous. For actual molecular systems whose long-time dynamics is captured by kinetic rate models, accurate rate coefficients can be determined from the statistics of the transitions between the metastable states at each replica temperatures. We demonstrate the practical applicability of the procedure by constructing master equation (Markov state) models of peptide and RNA folding from REMD simulations.
A new algorithm for revealing the free energy barrier among stable states was developed by combining the multicanonical ensemble with the Weighted Histogram Analysis Method. The method was applied to the cis-trans imide isomerization of a blocked peptide dimer, -Ala-Pro-.
In biomolecular systems with many degrees of freedom such as proteins and nucleic acids, there exists an astronomically large number of local-minimum free energy states. Conventional simulations in the canonical ensemble encounter with great difficulty, because they tend to get trapped in states of these local minima. Enhanced conformational sampling techniques are thus in great demand. A simulation in generalized ensemble performs a random walk in potential energy, volume, and other physical quantities or their corresponding conjugate parameters such as temperature, pressure, etc. and can overcome this difficulty. From only one simulation run, one can obtain canonical ensemble averages of physical quantities as functions of temperature, pressure, etc. by the reweighting techniques. In this chapter, we review uses of the generalized-ensemble algorithms in biomolecular systems. A well-known method, namely, replica-exchange method, is described first. We then present various extensions of the replica-exchange method. The effectiveness of the methods is tested with protein folding and ligand docking simulations.
The replica-exchange umbrella sampling (REUS) method combines replica-exchange and umbrella sampling methods and allows larger conformational sampling than conventional simulation methods. This method has been used in many studies to understand docking mechanisms and the functions of molecules. However, REUS has not been combined with quantum chemical codes. Therefore, we implemented the REUS simulation technique in the DFTB. + quantum chemistry code utilizing approximate density functional theory. We performed REUS simulations of an intra-molecular proton transfer reaction of malonaldehyde and a formation of a phthalocyanine from four phthalonitriles and one iron atom to validate the reliability of our implemented REUS-DFTB. + combination.
A procedure for local deformation of a polymer by concerted rotation of several main-chain dihedral angles has been adapted recently to be an elementary move in Monte Carlo simulations. We expand the applicability of the move by generalizing the formalism to allow fixed dihedral angles that sequentially interrupt the rotatable bonds to be nonplanar. The method is applied to the simulation of a small protein in which the dihedral angles of the peptide bonds are allowed to deviate from their ideal values and to the simulation of an RNA hairpin loop in which the main chain (C3'-C4') bonds that are constrained by the sugar rings are rigid but nonplanar. The move is found to increase the rate at which the systems explore their accessible configuration spaces. The relation of the results to previous studies and possible enhancements of the method are discussed. (C) 2000 John Wiley & Sons, Inc.