MINIREVIEW
Barrier passage and protein dynamics in enzymatically catalyzed
reactions
Dimitri Antoniou
1
, Stavros Caratzoulas
1,
*, C. Kalyanaraman
1
, Joshua S. Mincer
1
and Steven D. Schwartz
1,2
1
Department of Biophysics, Albert Einstein College of Medicine, Bronx, NY, USA;
2
Department of Biochemistry, Albert Einstein
College of Medicine, Bronx, NY, USA
This review describes studies of particular enzymatically
catalyzed reactions to investigate the possibility that catalysis
is mediated by protein dynamics. That is, evolution has
crafted the protein backbone of the enzyme to direct vibra-
tions in such a fashion to speed reaction. The review presents
the theoretical approach we have used to investigate this
problem, but it is designed for the nonspecialist. The results
show that in alcohol dehydrogenase, dynamic protein
motion is in fact strongly coupled to chemical reaction in
such a way as to promote catalysis. This result is in concert
with both experimental data and interpretations for this and
other enzyme systems studied in the laboratories of the two
other investigators who have published reviews in this issue.

for enzyme catalytic action along with promoting vibra-
tions. Following this, we will describe the mathematical
foundation for our theories in some detail. This section will
be written for nonexperts, but will contain the necessary
formulae for the specialist as well. It will include the
relationship between the current theories and a well-known
approach to charged particle transfer in biological reactions,
namely the Marcus theory. In this section we will also
describe a simple nonbiological chemical system in which
the physical features of promoting vibrations may be easily
understood – proton transfer in organic acid crystals. We
will then describe how we have used these concepts to fit
seemingly anomalous kinetic data for enzymatic reactions.
In the next section, we explore how one might rigorously
identify the presence of such a promoting vibration in any
enzymatic reaction, and illustrate the concepts with appli-
cations to specific enzyme systems. The paper then con-
cludes with discussions of future directions for this research.
POTENTIAL MODES OF ENZYMATIC
ACTION
The exact physical mechanisms by which enzymes cause
catalysis is still a topic for vigorous dialogue [1–3]. The
research described in this paper will argue for a strong
contribution from a nontraditional source, i.e. directed
protein motions. In order to place this concept into a
context, we will briefly review other potential mechanisms
for enzymes to cause catalysis. We emphasize that none of
these mechanisms are mutually exclusive, and are probably
all involved in catalysis to a greater or lesser extent in each
enzyme system.

fact that the transition state is strongly bound. It has been
argued, however, that the electrostatic character of the
active site during the catalytic event is largely determined by
whatever charge stabilization is needed as the reaction
progresses. If an inhibitor is designed with the complement-
ary charges, it will bind strongly to the active site. However,
this does not imply that the method by which the enzyme
produced catalysis was transition state binding and con-
comitant release of energy [1].
A second approach, which might be viewed as the
converse of transition state stabilization, is ground state
destabilization. In this picture [7], the role of the enzyme is to
make the reactants less stable rather than making the
transition state more stable. Thus the energetic hill that must
be climbed with thermal activation is lowered. Energies are
all relative and so the end effect of this and the first
mechanism are the same; lowering the relative energy
difference between reactants and transition state. But it is
clear that this view presents a very different physical
mechanism. Recent calculations [8] seem to show that this
model may well be dominant for the most efficient enzyme
known, orotidine monophosphate decarboxylase.
A third concept that has been also suggested. In solution,
reactants are strongly solvated by water, the dominant
component of most living cells. When enzymes bind
reactants, they often exclude water, and this lowered
dielectric environment may be more conducive to reaction
[9–11]. This approach to catalysis tends to treat the catalytic
event much like an electron transfer reaction in solution.
The dominant description of electron transfer in solution is

and cause an increase in reaction rate. This is not simply
preparation of a reactive species, but rather dynamic
coupling. It is important to note that this is an entirely
different view of the method by which the enzyme
accomplishes rate acceleration. In this view, evolution has
created a protein structure that moves in such a way as to
lower a barrier and make it less wide. It must be emphasized
that this lowering of the barrier is not statistical lowering of
a potential of mean force through the release of binding
energy, but rather the use of highly directed energy (a
vibration) in a specific direction. Furthermore, this is not
simply the statistical preparation of reactive species as in the
NAC concept. Here, protein dynamics directly affect the
reaction coordinate potential. Although this effect can be
quite apparent for a tunneling system (the probability to
tunnel increases exponentially with a reduction of the width
of the tunneling barrier), it is equally important for systems
where the reaction proceeds through classical transfer,
because as the barrier is made narrower, it is also lowered.
In order to understand how directed protein motions may
cause catalysis, we need a theory of chemical reactions in a
condensed phase. Our group has developed theories over
the past 10 years, and this work, initially developed for
simple condensed phases, such as polar media, forms the
basis for our analysis. We now describe these theories in
some detail.
AN ENZYME AS A CONDENSED PHASE:
THEORETICAL FORMULATION FOR
THE STUDY OF CHEMICAL REACTION
There are two requirements to enable the study of a

Gao [8]. We have chosen a different approach, largely on
stylistic grounds. Rather than treating the full collection of
atoms as a mixture of quantum and classical objects
(something that is difficult to define rigorously), we have
developed approximate approaches to treat the entire
collection of atoms as a quantum mechanical entity. As
mentioned above, both approaches are approximate, but we
prefer to make the approximation uniform for the entire
system.
We have called our approach the ÔQuantum KramersÕ
methodology [17,18]. Our ideas were motivated by the
following approximations developed for the study of the
classical mechanics of large, complex systems. It is known
that for a purely classical system [19,20], an accurate
approximation of the dynamics of a tagged degree of
freedom (for example a reaction coordinate) in a condensed
phase can be obtained through the use of a generalized
Langevin equation. The generalized Langevin equation is
given by Newtonian dynamics plus the effects of the
environment in the form of a memory friction and a random
force [21]. Thus, all the complex microscopic dynamics of all
degrees of freedom other than the reaction coordinate are
included only in a statistical treatment, and the reaction
coordinate plus environment is treated as a modified one-
dimensional system. What allows a realistic simulation of
complex systems is that the statistics of the environment can
in fact be calculated from a formal prescription. This
prescription is given by the Ôfluctuation-dissipation the-
oremÕ, which yields the relationship between friction and
random force. In particular, this theory enables us to

k
x
2
k
q
k
À
c
k
s
m
k
x
2
k

2
ð1Þ
The first two terms in this Hamiltonian represent the kinetic
and potential energy of the reaction coordinate, and the last
set of terms similarly represent the kinetic and potential
energy for an environmental bath. Here, s represents some
coordinate that measures progress of the reaction (for
example, in alcohol dehydrogenase where the chemical
step is transfer of a hydride, s might be chosen to represent
the relative position of the hydride from the alcohol to the
NAD cofactor.) c
k
is the strength of the coupling of the
environmental mode to the reaction coordinate, and m

frequency positions of the environmental modes, weighted
by the strength of the coupling of these modes to the
reaction coordinate. Note that this infinite collection of
oscillators is purely fictitious; they are chosen to reproduce
the overall physical properties of the system, but do not
necessarily represent specific physical motions of the atoms
in the system. It would seem that we have not made a huge
amount of progress; we began with a many-dimensional
system (classical) and found out that it could be accurately
approximated by a one-dimensional system in a frictional
environment (the generalized Langevin equation.) We have
now recreated a many-dimensional system (the Zwanzig
Hamiltonian). The reason we have done this is twofold.
First, there is no true quantum mechanical analogue of
friction, and so there really is no way to use the generalized
Langevin approach for a quantum system, such as we
would like to do for an enzyme. Second, the new quantum
Hamiltonian given in Eqn (1) is much simpler than the
Hamiltonian for the full enzymatic system. Harmonic
oscillators are a problem that can easily be solved by
quantum mechanics. Thus, the prescription is, given a
potential for the enzymatic reaction, we model the exact
problem using Zwanzig Hamiltonian, as in Eqn (1), with
the distribution of harmonic modes given by the spectral
density in Eqn (2), and found through a simple classical
computation of the frictional force on the reaction coordi-
nate. Then, using methods to compute quantum dynamics
developed in our group [24–29], quantities such as rates or
kinetic isotope effects may be computed. Thus, the quantum
Kramers method, developed in our group, consists of the

for a two-dimensional version of this potential, when the q
degree of freedom is exactly equal and opposite in sign to
cs
mx
2
, and the minimum of the potential energy profile along
the reaction coordinate is unaffected by this coupling.
Within Marcus’ theory, which is a deep tunneling theory,
transfer of the charged particle occurs at the value of the
bath coordinates that cause the total potential to become
symmetrized. Thus, if the bare reaction coordinate potential
is symmetric, then the total potential is symmetrized at the
position of the Ôbath plus couplingÕ minimum. When this
configuration is achieved, the particle tunnels; the activation
energy for the reaction is largely the energy to bring the bath
into this favorable tunneling configuration.
While Marcus’ theory and our microscopic quantum
Kramers theory are highly successful in many cases, in other
cases, it is not possible to reproduce experimental results
using such an approach. The reason for this is that the
antisymmetric coupling contained within the Zwanzig
Hamiltonian does not physically represent all possible
important motions in a complex reacting system. In fact,
such a reality was pointed out some time ago in seminal
work of the Hynes group [31]. In some of our earlier work
on hydrogen transfer in enzymatic systems, we were able to
show that one could reasonably fit experimental kinetic data
in such enzymatic systems with phenomenological applica-
tion of the Hynes theories [32]. We became interested in a
microscopic study of such systems in the examination of

k
2m
k
þ
1
2
m
k
x
2
k
q
k
À
c
k
s
m
k
x
2
k

2
þ
P
2
Q
2M
þ

result of this study is that symmetrically coupled vibrations
can significantly enhance rates of light particle transfer, and
also significantly mask kinetic isotope signatures of tunnel-
ing. A physical origin for this masking of the kinetic isotope
effect may be understood from a comparison of the two-
dimensional problem comprised of a reaction coordinate
coupled symmetrically and antisymmetrically to a vibration.
As Fig. 2 shows, antisymmetric coupling causes the minima
(the reactants and products) to lie on a line; the minimum
energy path, which passes through the transition state. In
contrast, symmetric coupling causes the reactants and
products to be moved from the reaction coordinate axis in
such a way that a straight line connection of reactant and
products would pass no where near the transition state.
This, in turn, results in the gas phase physical chemistry
phenomenon known as corner cutting [40–42]. Physically,
the quantity to be minimized along any path from reactant
to products is the action. This is an integral of the energy,
and so loosely speaking, it is a product of distance and depth
under the barrier that must be minimized to find an
approximation to the tunneling path. The action also
includes the mass of the particle being transferred, and so in
the symmetric coupling case, a proton will actually follow a
very different physical path from reactants to products in a
reaction than a deuteron. (Not just in the trivial sense that
one tunnels more than another). It is this following of a
different physical path, even when tunneling dominates,
Fig. 1. A benzoic acid dimer. Thereactioncoordinateinthiscaseisthe
symmetric transfer of the hydroxyl protons to the carbonyl oxygen.
The promoting vibration is the symmetric motion of the oxygens

Alcohol dehydrogenases are NAD
+
-dependent enzymes
that oxidize a wide variety of alcohols to the corresponding
aldehydes. After successive binding of the alcohol and
cofactor, the first step is generally accepted to be complex-
ation of the alcohol to one of the two bound Zinc ions [45].
This complexation lowers the pK
a
of the alcohol proton and
causes the formation of the alcoholate. The chemical step is
then transfer of a hydride from the alkoxide to the NAD
+
cofactor. They [46] have found a remarkable effect on the
kinetics of yeast alcohol dehydrogenase (a mesophile) and a
related enzyme from Bacillus stereothermophilus, a thermo-
phile. A variety of kinetic studies from this group have
found that the mesophile [47] and many related dehydro-
genases [48–51] show signs of significant contributions of
quantum tunneling in the rate-determining step of hydride
transfer. Remarkably, their kinetic data seem to show that
the thermophilic enzyme actually exhibits less signs of
tunneling at lower temperatures. Recent data of Kohen &
Klinman [52] also show, via isotope exchange experiments,
that the thermophile is significantly less flexible at mesophi-
lic temperatures, as in the results of Petsko et al. [53], who
conducted studies of 3-isopropylmalate dehydrogenase
from the thermophilic bacteria Thermus thermophilus.These
data have been interpreted in terms of models similar to
those we have described above, in which a specific type of

decrease in tunneling is seen as the temperature is raised.)
A final set of enzymes now thought to exhibit dynamic
protein control of tunneling hydrogen transfer is that in the
amine dehydrogenase family. Scrutton and coworkers have
extensively studied these enzymes [58]. Though similarly
named and having a similar end effect as the alcohol
dehydrogenases, they employ radically different chemistry.
These enzymes catalyze the oxidative deamination of
primary amines to aldehydes and free ammonia. In this
case, however, rather than a chemical step of hydride
transfer, the rate determining chemical step is proton
transfer; and in fact these enzymes catalyze a coupled
electron proton transfer reaction. Electrons are coupled to
some cofactor, for example, in the case of aromatic amine
dehydrogenase, the cofactor is tryptophan-tryptophyl qui-
none. Kinetic studies have shown that methylamine dehy-
drogenase exhibits not only relatively large primary kinetic
isotope effects (unlike the alcohol dehydrogenases), but also
very strong temperature dependence in the measured
activation energy. This experimental data has been inter-
preted as showing that the enzyme works via a promoting
vibration [59], as we have suggested for bovine serum amine
oxidase [32], and for various forms of HLADH [60]. Here,
the primary kinetic isotope effect is % 17, rather than 3 or 4.
s
q
s
0
+s
0

overall importance of barrier shape rather than just barrier
height in biochemistry.
It seems then that there is a growing body of evidence that
protein dynamics could well play a central role in enzymatic
catalysis, well beyond standard pictures of loop motions
that cause substrate binding and change electrostatic
environments as substrates are transformed to products.
In fact, in cases where tunneling seems to play a significant
role, as indicated by kinetic isotope effect experiments,
directed motion of the protein could well be responsible for
a significant fraction of the catalytic mechanism. What is
lacking in the ongoing analysis, is a tool that allows,
through a knowledge of protein structure and an assump-
tion of a potential function for the protein, the rigorous
identification of the presence or absence of such a symmet-
rically coupled/promoting vibration. Such a theoretical
approach is especially important in light of the fact that
there is currently no general experimental method to detect
such a protein motion as it impacts catalysis. While
spectroscopic methods can, with extraordinary sensitivity,
interrogate localized motions in proteins, as we have
described above, the defining nature of a promoting
vibration is to be found in the nature of the coupling of
that motion to the reaction coordinate. There is no
experimental tool available to directly measure this coup-
ling. The next section details our theoretical approach to the
problem, and a recent application to alcohol dehydroge-
nase.
THE DETECTION OF PROMOTING
VIBRATIONS IN PROTEINS

shown in Figs 3 and 4 [62]. These are spectral densities
calculated for the proton in a potential for proton transfer
between two carbon centers immersed in argon; shown in
Fig. 3 at the transition state, and in Fig. 4 with the proton at
a position near the reactant well. A more stringent test of the
approach is to be found in a similar computation when,
rather than explicitly including a symmetrically coupled
vibration, we simply create a system in which proton
transfer occurs between two vibrating atoms of a complex.
There we expect to find a promoting vibration, but the
identity of this vibration is not manifest in the model form,
rather it is buried in the dynamics of the atomic motions. In
fact, when we compute the spectral density for such a
proton transfer system with the proton held in the reactant
well and the effective mass of the vibrating system equal to
100 amu, we obtain the result shown in Fig. 5. Given the
0
50
100
150
200
250
300
0
5e-05
0.0001
0.00015
0.0002
0.00025
J(ω)

two reasons: first, there is as yet no crystal structure for
YADH, and such a structure is needed as a starting
point for any dynamics study of a protein. Second, there
are a number of mutants of HLADH, which allow
detailed study of the influence of protein composition on
protein dynamics, and how dynamics relates to kinetics
of catalysis.
Our analysis began with the 2.1-A
˚
crystal structure of
Plapp and coworkers [64]. This crystal structure contains
both NAD
+
and 2,3,4,5,6-pentafluorobenzyl alcohol com-
plexed with the native HLADH (metal ions and both the
substrate and cofactor.) The fluorinated alcohol does not
react and go onto products because of the strong electron
withdrawing tendencies of the flourines on the phenyl ring,
and so it is hypothesized that the crystal structure
corresponds to a stable approximation of the Michaelis
complex. We then replaced the fluorinated alcohol with the
unfluorinated compound to obtain the reactive species as in
Luo et al. [56]. This structure was used as input into the
CHARMM
program [65]. Both crystallographic waters [64]
(there are 12 buried waters in each subunit) and environ-
mental waters were included via the TIP3P potential [66].
The substrates were created from the
MSI/
charmm param-

frequency modes remain manifest in autocorrelation func-
tions, and it is advantageous to employ spectral densities
calculated from Fourier transforms of the velocity function.
We will not have an ÔexactÕ reaction coordinate at our
disposal, but this does not affect the calculation. The
diagnostic of the promoting vibration is simply the presence
of a strong variation in the spectral density as the reacting
particle (in this case the hydride) is moved from the reactant
well to the product well. As long as it is moved on a vector
that contains some component of the reaction coordinate, a
sharp spike will appear in the spectral density at a frequency
corresponding to the promoting vibration, possibly shifted
by a small amount [63]. Thus, appearance of a strong peak
in the spectral density along the line connecting the
alcoholate and the NAD
+
should be found close to the
actual frequency of the promoting vibration. We then
calculated the force or velocity autocorrelation function on
the transferring particle, i.e. the hydride. A search through
position space in the vicinity of the transition state will yield
spectral densities in which a peak moves to ever-smaller
frequencies. The result with the smallest frequency should
be very close to the bare frequency of the promoting
vibration, and incidentally would locate the transition state
in the enzymatic environment. If the hypothesized promo-
ting vibration is present, we can immediately check the
frequency to ascertain if the predicted frequency is similar to
the frequency of motion of an expected residue, which is in
the putative protein motion. For example, a position

Fig. 5. The spectral density in the reactant well for a similar model, but
with no manifestly symmetrically coupled promoting vibration. In this
case the carbon centers move toward each other, and their motion
creates a promoting vibration similar to the benzoic acid system shown
in Fig. 1.
Ó FEBS 2002 Barrier passage and protein dynamics (Eur. J. Biochem. 269) 3109
our computations as a smaller peak in the spectral density at
positions remote from the transition state.
The results from these calculations are shown in Figs 6, 7,
and 8, with the spectral density now indicated by G(x),
showing that we compute this spectral density from a
velocity autocorrelation function rather than a force auto-
correlation function. We now employ a velocity autocorre-
lation function for a purely technical reason. In a simple
fluid, relatively slow frequency motions dominate all envi-
ronmental modes. Note for example, the first peak in Fig. 3
occurs at about 50 cm
)1
, and the spectral density is
essentially zero by 200 cm
)1
. In a protein, there are
vibrational modes extending up to CH stretches in the
thousands of wavenumbers. It can be shown that the
relationship between the force and the velocity autocorre-
lation functions is simply multiplication by the square of the
frequency. Thus in the force autocorrelation function, even
very weakly coupled modes, can be dominant when their
frequency is very high. When the methodology is applied to
the enzyme, we find exactly the expected results. First, Fig. 6

systems, there seemed to be cases of significant involvement
of quantum tunneling without the expected high primary
kinetic isotope effect.
0 500 1000 1500 2000 2500 3000
ω (cm
1
)
0.0
10.0
20.0
30.0
G
S
(ω)
(MC)
Fig. 7. The spectral density computed at the point of minimal coupling in
Fig. 6, shown alone. Note that the spectral density is an order of
magnitude smaller at the point of minimal coupling than in the reac-
tant or product wells. This result is similar in this respect to the result
obtainedinFigs3and4.
0 500 1000 1500 2000 2500 3000
ω (cm
1
)
50.0
50.0
150.0
250.0
350.0
450.0

3110 D. Antoniou et al. (Eur. J. Biochem. 269) Ó FEBS 2002
Having understood this puzzle, it is important to mention
that new problems have arisen. The first and foremost is the
large timescale separation between the promoting vibration
and the chemical turnover of the enzyme systems involved.
The dominant peaks in the spectral densities indicate
motions on the 150-cm
)1
frequency scale. This corresponds
to sub-picosecond vibrations. Clearly, many cycles of the
promoting vibration must occur before it is effective in
helping to cause chemical turnover. This is, of course, not
without precedence; motions such as loop closures in
proteins often happen many times before catalysis occurs.
The generally accepted explanation is that such ineffective
motions are the result of incorrectly placed groups or
substrate in the enzyme active site. In many ways, this issue
corresponds to finding the actual Ôreaction coordinateÕ in
any condensed phase problem. For example, in a proton
transfer in a polar solvent, reaction is not actually limited by
movement of the proton, but actually by rearrangement of
the solvent around the moving charged particle. Thus, what
specific motions and placements of atoms within the enzyme
and substrates are needed for catalysis will be a subject of
significant concern for theoretical research.
A second question of almost philosophical import is the
extent to which evolution has utilized protein dynamics in
concert with quantum tunneling to craft enzymes. It should
certainly come as no surprise that tunneling is used in
catalysis. Evolution knows nothing about which equation,

biological interest. Nature 161, 707–709.
5. Schramm, V.L. (1999) Enzymatic transition state analysis and
transition-state analogues. Methods Enzymol. 308, 301–354.
6. Schowen, R.L. (1978) Transition States of Biochemical Processes.
Plenum Press, New York.
7. Jencks, W.P. (1975) Binding energy, specificity and enzymatic
catalysis, the circle effect Adv. Enzymol. 43, 219–310.
8. Wu, N., Mo, Y., Gao, J. & Pai, E.F. (2000) Electrostatic stress in
catalysis, structure and mechanism of the enzyme orotidine
monophosphate decarboxylase. Proc.NatlAcad.Sci.USA97,
2017–2022.
9. Warshel, A. (1978) Energetics of enzyme catalysis. Proc. Natl
Acad.Sci.USA75, 5250–5254.
10. Cannon, W.R. & Benkovic, S.J (1998) Solvation, reorganization
energy and biological catalysis. J. Biol. Chem. 273, 26257–25260.
11. Bruice, T.C. & Torres, R.A. (2000) The mechanism of phospho-
diester hydrolysis: near in-line attack conformations in the ham-
merhead ribozyme. J. Am. Chem. Soc. 122, 781–791.
12. Marcus, R.A. (1964) Chemical and electrochemical electron
transfer theory. Ann. Rev. Phys. Chem. 15, 155–181.
13. Babamov, V. & Marcus, R.A. (1981) Dynamics of hydrogen atom
and proton transfer reactions: symmetric case. J. Chem. Phys. 74,
1790.
14. Lau, E. & Bruice, T.C. (1998) The importance of correlated
motions in forming highly reactive near attack conformations in
catechol O-methyltransferase. J. Mol. Biol. 120, 12387–12394.
15. Torres, R.A., Schiott, B.S. & Bruice, T.C. (1999) Molecular
dynamics simulations of ground and transition states for the
hydride transfer from formate to NAD
+

26. Antoniou, D. & Schwartz, S.D. (1995) Vibrational energy transfer
in linear hydrocarbon chains: new quantum results. J. Chem. Phys.
103, 7277–7286.
27. Schwartz, S.D. (1996) The interaction representation and non-
adiabatic corrections to adiabatic evolution operators. J. Chem.
Phys. 104, 1394–1398.
28. Antoniou, D. & Schwartz, S.D. (1996) Nonadiabatic effects in a
method that combines classical and quantum mechanics. J. Chem.
Phys. 104, 3526–3530.
29. Schwartz, S.D. (1996) The interaction representation and non-
adiabatic corrections to adiabatic evolution operators II: nonlin-
ear quantum systems. J. Chem. Phys. 104, 7985–7987.
Ó FEBS 2002 Barrier passage and protein dynamics (Eur. J. Biochem. 269) 3111
30. Karmacharya, R., Antoniou, D. & Schwartz, S.D. (2001) None-
quilibrium solvation, and the quantum kramers problem: proton
transfer in aqueous glycine. J. Phys. Chem. B105, 2563–2567.
31. Borgis, D. & Hynes, J.T. (1996) Curve Crossing Formulation for
Proton Transfer Reactions in Solution. J. Chem. Phys. 100, 1118.
32. Antoniou, D. & Schwartz, S.D. (1997) Large kinetic isotope effects
in enzymatic proton transfer, and the role of substrate oscillations.
Proc. Natl Acad. Sci. USA 94, 12360–12365.
33. Fuke, K. & Kaya, K. (1989) Dynamics of double proton transfer
reactions in the excited state model of hydrogen bonded base pairs.
J. Phys. Chem. 93,614.
34. Brougham, D.F., Horsewill, A.J., Ikram, A., Ibberson, R.M.,
McDonald, P.J. & Pinter-Krainer, M. (1996) The correlation
between hydrogen bond tunneling dynamics, and the structure of
benzoic acid dimers. J. Chem. Phys. 105,979.
35. Meier, B.H., Graf, F. & Ernst, R.R. (1982) Structure, and
dynamics of intramolecular hydrogen bonds in carboxylic acid

4803–4812.
46. Kohen, A., Cannio, R., Bartolucci, S. & Klinman, J.P. (1999)
Enzyme dynamics and hydrogen tunneling in a thermophilic
alcohol dehydrogenase. Nature 399, 496–499.
47. Cha, Y., Murray, C.J. & Klinman, J.P. (1989) Hydrogen tunneling
in enzyme reactions. Science 243, 1325.
48. Grant, K.L. & Klinman, J.P. (1989) Evidence that both protium
and deuterium undergo significant tunneling in the reaction cat-
alyzed by bovine serum amine oxidase. Biochemistry 28, 6597.
49. Kohen, A. & Klinman, J.P. (1998) Enzyme catalysis: beyond
classical paradigms. Accounts Chem. Res. 31,397.
50. Bahnson, B.J. & Klinman J.P. (1995) Hydrogen tunneling in
enzyme catalysis. Methods Enzymol 249, 373.
51. Rucker, J., Cha, Y., Jonsson, T., Grant, K.L. & Klinman, J.P.
(1992) Role of internal thermodynamics in determining hydrogen
tunneling in enzyme-catalyzed hydrogen transfer reactions. Bio-
chemistry 31, 11489.
52. Kohen, A., Klinman J.P. (2000) Protein flexibility correlates with
degree of hydrogen tunneling in thermophilic, and mesophilic
alcohol dehydrogenases. JACS 122, 10738–10739.
53. Zavodsky, P., Kardos, J., Svingor, A. & Petsko, G.A. (1998)
Adjustment of conformational flexibility is a key event in the
thermal adaptation of proteins. Proc. Natl Acad. Sci. USA 95,
7406–7411.
54. Bahnson, B.J., Colby, T.D., Chin, J.K., Goldstein, B.M. &
Klinman, J.P. (1997) A link between protein structure, A. and
enzyme catalyzed hydrogen tunneling. Proc.NatlAcad.Sci.USA
94, 12797–12802.
55. Bahnson,B.J.,Park,D H.,Kim,K.,Plapp,B.V.&Klinman,J.P.
(1993) Unmasking of hydrogen tunneling in the horse liver alcohol

63. Caratzoulas, S., Mincer, J. & Schwartz, S.D. (2002) Identification
of a protein promoting vibration in the reaction catalyzed by horse
liver alcohol dehydrogenase. JACS, 124, 3270–3276.
64. Ramaswamy, S., Elkund, H. & Plapp, B.V. (1994) Structures of
horse liver alcohol dehydrogenase complexed with, NAD
+
and
substituted benzyl alcohols. Biochemistry 33, 5230–5237.
65. Brooks,B.R.,Bruccoleri,R.E.,Olafson,B.D.,States,D.J.,Swa-
minathan, S. & Karplus, M. (1983) CHARMM, a program for
macromolecular energy, minimization, dynamics calculations.
J. Comp. Chem. 4, 187–217.
66. Jorgensen, W. Chandrasekher, J., Madura, J.D., Impey, R.W. &
Klein, M.L. (1983) Comparison of simple potential functions for
simulating liquid water. J. Chem. Phys. 79, 926.
67. Pavelites, J.J., Gao, J., Bash, P.A., Alexander, D. & Mackerell, J.
(1997) A molecular mechanics force field for NAD+ NADH and
the pyrophosphate groups of nucleotides. J. Comput. Chem. 18,
221–239.
3112 D. Antoniou et al. (Eur. J. Biochem. 269) Ó FEBS 2002