Dynamic disorder and stepwise deactivation in a chymotrypsin catalyzed hydrolysis reaction

Article abstract of DOI:10.1021/ja077621d

In situ observation of the catalytic activity of individual α-chymotrypsin enzymes reveals a novel pathway for spontaneous deactivation. Rather than deactivating abruptly in a one-step process, the enzyme seems to struggle for life; the activity decreases

Full text of DOI:10.1021/ja077621d

Published on Web 11/22/2007
Dynamic Disorder and Stepwise Deactivation in a Chymotrypsin Catalyzed
Hydrolysis Reaction
Gert De Cremer,† Maarten B. J. Roeffaers,† Mukulesh Baruah,‡ Michel Sliwa,‡ Bert F. Sels,†
Johan Hofkens,*,‡ and Dirk E. De Vos*,†
Department of Microbial and Molecular Systems, Katholieke UniVersiteit LeuVen, Kasteelpark Arenberg 23,
B-3001 LeuVen, Belgium, and Department of Chemistry, Katholieke UniVersiteit LeuVen, Celestijnenlaan 200F,
B-3001 LeuVen, Belgium
Received October 3, 2007; E-mail: johan.hofkens@chem.kuleuven.be; dirk.devos@biw.kuleuven.be
Insight into the detailed functioning of enzymes is crucial for
understanding their metabolic role and their use as industrial
biocatalysts. Not only the activity of a working enzyme but also
its transition to a deactivated state is of great interest. Until now,
deactivation was generally described as a two-state all-or-nothing
process, in which the individual enzyme’s activity abruptly disap-
pears.1,2 In early work on the activity of heat-exposed single
galactosidase enzymes, Rotman distinguished two subpopulations:
Figure 1. Structure of the profluorescent probe 1 that is converted by
chymotrypsin, yielding the strongly fluorescent rhodamine 110 (2).
one had retained 100% activity while the other had completely lost
its activity.3 However, in these experiments, no information could
be extracted on the deactivation pathway because the deactivation
took place in an ex situ incubation process. While NMR, X-ray
diffraction, or hydrogen exchange experiments are useful for
understanding the complex structural protein dynamics that drive
deactivation processes,2 such techniques lack either the time
resolution to study fast dynamic processes or the sensitivity to
identify low abundant transient species during spontaneous enzyme
deactivation. A recent technique that combines ultrasensitivity with
appropriate high time resolution for in situ measurements is single-
molecule fluorescence spectroscopy (SMFS). Lately, this powerful
technique was successfully applied for proving activity fluctuations
of active single enzymes, a phenomenon known as dynamic
disorder,4-9 and for single-turnover counting on various types of
Figure 2. Histogram of the waiting times between two successive turnovers
for a typical R-chymotrypsin enzyme during its active period. The nonlinear
decay in the semilogarithmic representation indicates dynamic disorder.
catalysts.10,11 Here, we advantageously employ SMFS to prove the
existence of fluctuations in the activity of single R-chymotrypsin
enzymes and to show that, during its deactivation, the enzyme is
altered stepwise before it eventually deactivates irreversibly.
Our approach to monitor single turnovers relies on the R-chy-
Inset: Representative time transient for a single R-chymotrypsin enzyme
catalyzing the hydrolysis of 1 (30 µM in PBS buffer at pH 7.4). The detected
photons are binned in intervals of 1 ms.
motrypsin (from bovine pancreas) catalyzed hydrolytic formation
of strongly fluorescent rhodamine 110 (2) from the profluorescent
rhodamine 110 bis(suc-Ala-Ala-Pro-Phe) substrate (1) (Figure 1).
The fluorescence of individual product molecules 2 released by
single R-chymotrypsin monomers, immobilized by entrapment in
agarose polymer, was collected through a fluorescence microscope
in confocal mode and detected on an avalanche photodiode (see
Supporting Information).
In order to determine the optimal reaction conditions for the
single-molecule measurements, the ensemble kinetics of the R-chy-
motrypsin catalyzed hydrolysis of 1 were studied by monitoring
the fluorescence increase due to formation of rhodamine 110 (2)
in a fluorimeter cuvette. Rates were adequately described by the
Michaelis-Menten model, proving the absence of substrate inhibi-
tion effects or allosteric interactions. The ensemble-averaged kinetic
parameters are 3.99 µM for KM and 3.86 s-1 for kcat (see Supporting
Information; Figure S1). Consequently, for the single-enzyme
measurements, a substrate concentration of around 30 µM was
maintained to eliminate artifacts from substrate depletion.
Next, activity traces were measured for more than 100 single-
enzyme molecules. An example is shown in the inset of Figure 2
(see also Supporting Information; Figure S4). The analysis of the
waiting times between successive individual turnovers shows that
there is dynamic disorder, even if the activity fluctuations are rather
small compared to previous work on other enzymatic systems.6,7
In the case of an enzyme with a nonfluctuating activity and one
first-order rate-limiting step in its catalytic cycle, the waiting times
are expected to be distributed according to a monoexponential
decay. It is clear, however, that in this case the waiting time
distribution is stretched over several orders of magnitude due to
dynamic disorder (Figure 2). It has been shown previously that
mathematical functions like a stretched exponential are more
adequate to fit such distributions.8
Autocorrelation analysis on the waiting times reveals the time
scale of the activity fluctuations. An elegant method for further
analysis is the construction of two-dimensional autocorrelation plots
of pairs of waiting times, also called joint probability distributions
† Department of Microbial and Molecular Systems.
‡ Department of Chemistry.
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10.1021/ja077621d CCC: $37.00 © 2007 American Chemical Society

C O M M U N I C A T I O N S
The existence of a transient phase in an enzyme’s deactivation
process has been postulated before13 and is supported by the
observation of genuine temperature optima in bulk kinetic studies
of enzymatic activity.14,15 In Daniel’s equilibrium model, an inactive
state precedes the deactivated state; while the transition from the
active state to the inactive state is reversible, the transition to the
deactivated state is irreversible.
kd
Eact yksz Einact
98 Edeact
(1)
k-s
Figure 3. Two-dimensional joint probability graphs for pairs of the
logarithm of the waiting times for event lags 1 and 5. The graphs are
corrected according to the formula in the text.
On the basis of our observations, a tentative model for a single
enzyme’s deactivation is proposed (Figure 4b): from its active state,
the enzyme can reversibly switch to an inactive conformation. Only
when residing in or during the formation of this inactive state is
the enzyme susceptible to important structural changes in such a
way that, after switching back to an active state, the averaged
activity is lower than before. Even at room temperature, thermal
energy seems sufficient to cause transitions between these states.
The proposed model in Figure 4b is in agreement with the
equilibrium model as described in eq 1 and refines it at a single-
molecule level. Extension of these experiments to other enzymatic
reactions will reveal whether or not such a stepwise deactivation
pathway is common in enzyme chemistry.
Summarizing, the analysis of the chymotrypsin dynamics at the
single-enzyme level not only reveals dynamic disorder during the
active period but also shows that the deactivation occurs stepwise,
rather than by an all-or-nothing event.
Figure 4. (a) Turnover frequency (TOF, averaged over 10 s) of a
deactivating R-chymotrypsin enzyme (see Supporting Information; Figure
S6). The red line indicates the different states and serves as a guide to the
eye. (b) The extended single-molecule deactivation model for enzymes. A
reversible conformational change causes the enzyme to switch between
active (green; with dynamic disorder) and inactive (red) states. During this
equilibrium, stepwise inactivation occurs before the enzyme deactivates
irreversibly.
Acknowledgment. The authors are grateful to the Belgian
Federal Government (IAP-VI) and to the KULeuven Research Fund
(CECAT and GOA). G.D.C. and M.B.J.R. are grateful to FWO
and IWT, respectively, for fellowships.
Supporting Information Available: Experimental procedures, data
analysis methods, Figures S1-S6. This material is available free of
charge via the Internet at http://pubs.acs.org.
(p(τj, τj+i) for the correlation between waiting times separated by
i turnovers).4 To identify the correlation effect between pairs of
waiting times more clearly, an additional correction can be made,
as illustrated by Lerch et al., yielding the difference distribution of
waiting times, expressed as δ(τj, τj+i) ) p(τj, τj+i) - p(τj) × p(τj+i).12
Because of the broad time range of the waiting times distribution,
the logarithm of the waiting times was used for constructing the
2D joint probability graphs in Figure 3. For R-chymotrypsin,
correlation between waiting times was observed for event lags lower
than 15, thus in the time range of a few seconds (see also Supporting
Information; Figure S5).
Approximately 95% of the measured enzymes maintained stable
activity over the measurement window of (3 h. The remaining
5% of the enzymes lost its activity according to the trajectory in
Figure 4a. This low percentage is the result of the high stability of
the immobilized chymotrypsin enzymes. Surprisingly, rather than
an abrupt or a continuous gradual deactivation, we observed a
transient phase with discrete inactive and active periods, before
the enzyme was irreversibly deactivated. After each inactive period,
only a fraction of the original activity was recovered. The discrete
activity levels in Figure 4a indicate that the consecutive activity
decreases are related to events during the inactive periods rather
than during the active periods themselves.
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