While the chemical basis of enzyme catalysis is largely under-stood the same cannot be said about the influence of the intrinsic protein structural dynamics on enzyme catalysis [1–4].. Al
Trang 1kinetics of a-chymotrypsin catalysis
Insights through chemical glycosylation, molecular dynamics and domain motion analysis
Ricardo J Sola´ and Kai Griebenow
Laboratory for Applied Biochemistry and Biotechnology, Department of Chemistry, University of Puerto Rico, Rı´o Piedras Campus, San Juan,
PR, USA
Unraveling the general mechanisms by which enzymes
catalyze chemical reactions is fundamental to the
understanding of biochemical processes While the
chemical basis of enzyme catalysis is largely
under-stood the same cannot be said about the influence of
the intrinsic protein structural dynamics on enzyme catalysis [1–4] Although it has been known for dec-ades that proteins are highly dynamic molecules which undergo a variety of structural motions [5,6] only recently has the relationship between protein structural
Keywords
a-chymotrypsin; enzyme catalysis;
glycosylation; molecular dynamics; serine
protease
Correspondence
K Griebenow, Department of Chemistry,
University of Puerto Rico, Rı´o Piedras
Campus, Facundo Bueso Bldg
Laboratory-215, San Juan 23346, PR 00931-3346, USA
Fax: +1 787 756 7717
Tel: +1 787 764 0000 ext.7815
E-mail: griebeno@adam.uprr.pr
(Received 5 July 2006, revised 26
September 2006, accepted 4 October 2006)
doi:10.1111/j.1742-4658.2006.05524.x
Although the chemical nature of the catalytic mechanism of the serine pro-tease a-chymotrypsin (a-CT) is largely understood, the influence of the enzyme’s structural dynamics on its catalysis remains uncertain Here we investigate whether a-CT’s structural dynamics directly influence the kinet-ics of enzyme catalysis Chemical glycosylation [Sola´ RJ & Griebenow K (2006) FEBS Lett 580, 1685–1690] was used to generate a series of glycosyl-ated a-CT conjugates with reduced structural dynamics, as determined from amide hydrogen⁄ deuterium exchange kinetics (kHX) Determination
of their catalytic behavior (KS, k2, and k3) for the hydrolysis of N-succinyl-Ala-Ala-Pro-Phe p-nitroanilide (Suc-Ala-Ala-Pro-Phe-pNA) revealed decreased kinetics for the catalytic steps (k2 and k3) without affecting sub-strate binding (KS) at increasing glycosylation levels Statistical correlation analysis between the catalytic (DG„ki) and structurally dynamic (DGHX) parameters determined revealed that the enzyme acylation and deacylation steps are directly influenced by the changes in protein structural dynamics Molecular modelling of the a-CT glycoconjugates coupled with molecular dynamics simulations and domain motion analysis employing the Gaussian network model revealed structural insights into the relation between the protein’s surface glycosylation, the resulting structural dynamic changes, and the influence of these on the enzyme’s collective dynamics and catalytic residues The experimental and theoretical results presented here not only provide fundamental insights concerning the influence of glycosylation on the protein biophysical properties but also support the hypothesis that for a-CT the global structural dynamics directly influence the kinetics of enzyme catalysis via mechanochemical coupling between domain motions and active site chemical groups
Abbreviations
a-CT, a-chymotrypsin; exchange, kinetics (kHX); GNM, Gaussian network model; H ⁄ D, hydrogen ⁄ deuterium; MD, molecular dynamics; pNA, p-nitroanilide; QM, quantum mechanics; Suc, N-succinyl; SBzl, thio-benzyl; SS-mLac, mono-(lactosylamido)-mono-(succinimidyl) suberate; SS-mDex, mono-(dextranamido)-mono-(succinimidyl) suberate; VDW, Van der Waals.
Trang 2dynamics and enzyme catalysis become generally
evi-dent within multiple enzyme systems [7–11] Due to
this it has been proposed that enzymes can accelerate
chemical reactions by lowering the transition state
free-energy of activation barrier (DGTS) through
the influence of global thermally coupled structural
motions (DGDyn) on the turnover step [12–15] One
such enzyme for which this phenomenon has been
pro-posed to occur but has not been fully experimentally
shown is a-chymotrypsin (a-CT; EC 3.4.21.1) [16–19]
Being a representative member of the
chymotrypsin-fold serine protease family, it catalyzes the selective
hydrolysis of amide bonds adjacent to bulky
hydro-phobic side chains (Tyr, Trp, and Phe) from its peptide
and protein substrates Its catalytic cycle (Fig 1) first
involves the formation of a substrate–enzyme complex
(ES), followed by formation and breakdown of the
first tetrahedral intermediate (ES)TET1 leading to the
liberation of the reaction’s first product and enzyme
acylation The catalytic cycle ends with the hydrolysis
of the acyl-enzyme intermediate, followed by
forma-tion and breakdown of a second tetrahedral
intermedi-ate (EP2)TET2, and liberation of the reaction’s second
product with restoration of the original free enzyme
From a structural perspective a-CT is composed of
two six-stranded b-barrel domains with the nature of
its collective structural dynamics being attributed to
interdomain hinge-bending motions [16,20,21] Due to
the location of the active site residues at the interface
between these two structurally rigid b-sheet domains it
has been suggested that global structural flexibility
could directly influence their displacements, thus
impacting the reaction kinetics [16,21–23] Theoretical
free-energy calculations of the catalytic cycle for
struc-turally related serine proteases (trypsin, elastase) have
also suggested the necessity of structural displacements
for the catalytic residues so that acylation and
deacyla-tion can take place [24–29] Thus, both local active site
residues and global domain motions are thought to be
implicated in the catalytically relevant structural
dynamics of the enzyme
The influence of structural dynamics on the
enzyme’s kinetics has also been suggested in previous
experimental works From 1H-NMR studies on the
His57–Asp102 low barrier hydrogen bond, Frey and
coworkers proposed the involvement of a
conforma-tional change during the formation of the tetrahedral
intermediate [30] Kawai et al also studied the effect
of medium viscosity on the hydrolysis of p-nitrophenyl ester and p-nitroanilide amide substrates [19,31] While for ester substrates the acylation and deacylation rates were found to decrease with increasing viscosity, for amide substrates they found the acylation step to be viscosity-independent From these results they pro-posed a catalytic mechanism in which induced-fit con-formational changes occur during the formation of the first tetrahedral intermediate and during the break-down of the second tetrahedral intermediate Alternat-ively, thermodynamic kinetic work by Stein and coworkers revealed that the enzyme displays convex Eyring plots only for the acylation step (k2) during the hydrolysis of amide substrates of differing peptide chain length [17] From these results the researchers proposed that the convex Eyring plots could arise from the coupling of protein structural isomerizations to the active site chemistry [17,18] While all of these experi-mental works suggest the possible involvement of structural dynamics in the various kinetic steps of a-CT catalysis, no actual measurements of protein structural dynamics were performed to explain the observed kinetic catalytic behavior Thus, the question
of whether the kinetics of a-CT catalysis are influenced
by the enzyme’s intrinsic structural dynamics still remains experimentally unanswered
Due to the well documented effect of natural glycans
in modulating glycoprotein structural dynamics and function [32–35], chemical glycosylation represents a straightforward methodology to study the role of pro-tein structural dynamics on enzyme catalysis [36] Herein we designed a series of differentially
glycosylat-ed a-CT variants with sequentially rglycosylat-educglycosylat-ed structural dynamics through chemical glycosylation with mono-functionally activated glycans of differing molecular masses [36,37] These were employed in this work to address experimentally the questions of whether and how the enzyme’s structural dynamics influence the kinetics of a-CT catalysis This was done by determin-ing the changes in the global structural dynamics (DGHX) [38] for the various chemically glycosylated a-CT conjugates through amide hydrogen⁄ deuterium (H⁄ D) exchange kinetic (kHX) experiments and then performing statistical correlation analysis with their kinetic parameters (KS, k2, and k3) for the hydrolysis
of N-succinyl-Ala-Ala-Pro-Phe p-nitroanilide
(Suc-Ala-Fig 1 General mechanism of serine prote-ase catalysis.
Trang 3Ala-Pro-Phe-pNA) Molecular modelling of the a-CT
glycoconjugates coupled with molecular dynamics
(MD) simulations and domain motion analysis
employing the Gaussian network model (GNM) was
additionally employed to provide structural insights
into the relation between the protein’s surface
glycosy-lation, the resulting structural dynamic changes, and
the influence of these on the enzyme’s collective
dynamics and catalytic residues
Results and Discussion
Chemical glycosylation of a-CT
Chemical glycosylation was recently introduced by us
as a useful methodology for the sequential modulation
of protein structural dynamics without altering the
protein’s internal amino acid composition, thus
allow-ing the study of its impact on the protein fundamental
biophysical properties [36] It was employed in this
work to study the influence of structural dynamics
on the kinetics of a-CT catalysis Two glycans of
contrasting molecular mass
[mono-(lactosylamido)-mono-(succinimidyl) suberate (SS-mLac; 500 Da) and
mono-(dextranamido)-mono-(succinimidyl) suberate
(SS-mDex; 10 kDa)] were employed to highlight any
steric effects induced by the chemical glycosylation
that could potentially alter the substrate binding
affinities of the conjugates and thus impact their
cata-lytic behavior The chemistry used for chemical
glyco-sylation is based on the succinimidyl functionality
(Fig 2) which allows coupling of the glycans to the
protein surface via the lysine e-amino groups at pH 9
and above (Table 1) The resulting conjugates are
het-erogeneous mixtures of noncrosslinked single protein
species characterized by a variable distribution of
gly-cans attached to the protein’s surface Average glycan
molar contents for these a-CT glycoconjugates were
sequentially increased to levels of around 7–8 mol of
glycan per mol of protein This is approximately 50–
60% of the total glycan content that can theoretically
be attached to a-CT by the chemistry employed
because the protein has 14 surface accessible lysine
residues Previous structural characterizations revealed that protein structural integrity was not adversely impacted during the chemical glycosylation and that the thermodynamic stability of the conjugates was increased with increasing glycosylation [36,37]
Changes in a-CT’s structural dynamics upon chemical glycosylation
Determination of H⁄ D exchange kinetics represents one of the principal techniques for the experimental measurement of changes in protein structural dynamics [9,34,36,38–46] Due to the heterogeneous nature of the glycoconjugates we chose to determine the global amide H⁄ D exchange rates by FTIR spectroscopy [7,36,44,45] These measurements thus represent the average dynamic nature of the enzyme Figure 3 shows the spectroscopic results from a typical FTIR H⁄ D exchange experiment for a-CT including both the spec-tra of the undeuterated and completely deuterated protein H⁄ D exchange kinetics were determined by following the decrease in the absorbance of the amide
II band (N-H, 1500–1600 cm)1) relative to the non-exchanging amide I band (C¼ O, 1600–1700 cm)1) From thermodynamic analysis (EX2 exchange mechan-ism; pH 7.1) of the H⁄ D exchange kinetic plots (Fig S1), the global Gibbs free-energy of microscopic unfolding (DGHX,1) for the various glycoconjugates prepared was calculated This parameter is representa-tive of the global structural dynamic free-energy of the protein (DGDyn DGHX,1) [13,38,47,48] The results (Table 2) show the reduced global structural dynamic free-energy of a-CT as a function of the glycosylation levels independent of the glycan size as had been previ-ously described by us [36]
Additionally, molecular models of the Lac-a-CT glycoconjugates (Fig 4) were constructed based on the lysine reactivity index presented in Table 1 (see below)
to provide a detailed picture of the possible changes in structural dynamics upon chemical glycosylation These glycoconjugate structures were then subjected to conformational energetic equilibration by molecular dynamics (MD) simulation methods (Fig S2) Models
Fig 2 Succinimidyl activated lactose
mole-cule (SS-mLac) employed for the chemical
glycosylation of a-CT and for the molecular
modelling and molecular dynamics
simula-tions The succinimidyl functionality serves
as leaving group during the glycosylation
reaction.
Trang 4for the dextran modified protein could not be
construc-ted due to the technical limitations involved in
model-ling linear polymeric molecules of such large size
(> 300 A˚) While molecular modelling and MD
simula-tions have previously been employed with great success
to provide a deeper mechanistic understanding towards
the roles of glycans on glycoprotein and
glycocon-jugates structure, stability, dynamics, and function
[13,49–55], the influence of the degree of glycosylation
on the protein biophysical properties has remained
unexplored To obtain a general thermodynamic and
entropic picture from the MD simulations we calculated
the global energetic parameters and Debye–Waller
temperature B-factors for the protein portion of the thermodynamically optimized a-CT glycoconjugate structures (Table 3) Comparison with the parameters for the full conjugates (protein-glycan) revealed that these changes are not due to the presence of the glycans because many of the energy parameters remained unchanged when calculated with and without the gly-cans (Table S1) The results from the MD simulations show how the total energy of the protein decreases at increasing glycosylation levels This is in accord with the increased thermodynamic stability exhibited by natural glycoproteins [34,56–59] and also with data obtained by differential scanning calorimetry for our glycoconjugates [36,37] Examination of the individual energy parameters contributing to the decrease in total energy of the glycoconjugates revealed that the bond, angle, and Van der Waals (VDW) energy parameters increased due to glycosylation with a decrease in the dihedral and the coulombic electrostatic energy parame-ters Because the protein portion of the conjugates remains constant for these models, the changes in bond, angle, and dihedral energy must arise from a rearrange-ment of their noncovalent interactions While the contributions of the VDW and coulombic energy parameters to these changes are evident from the results, other noncovalent interactions such as internal hydrogen bonds could also contribute to the increase in these parameters Analysis of the changes in internal hydrogen bond composition for the protein-glycan con-jugates indicates that for all of the concon-jugates there was also an increase in these internal hydrogen bonds formed due to glycosylation (Table S2) However, they are too small to sustain the observed changes in the bond and angle parameters These are most probably increased due to the increased VDW interactions The changes in some of these parameters (e.g reduced dihedral and increased VDW energies) also suggest a more rigid and compact protein structure for the glyco-conjugates This increase in rigidity due to glycosylation can be also be appreciated from the decrease in the cal-culated Debye–Waller temperature B-factors (Table 3, [60]) This reduction in dynamics due to chemical glyco-sylation does not appear to be caused by the modified lysine residue charges as it has been well established that natural glycosylation also reduces substantially the dynamics of natural glycoproteins where the modifica-tion occurs in noncharged residues [32–34] However, future experiments will be performed to investigate this The observed changes in the coulombic energy parameter also highlight the large contribution that the internal electrostatics have towards decreasing the total energy of the conjugates, which agrees with the hypothesis of global electrostatics being relevant to
Table 1 Reactivity order based on the calculated electrostatic
pot-entials (EP) for the N e of the lysine residues of a-CT at pH 9 EP,
EwaldEi EP is the Ewald energy of placing a charge of +1 at the
location of the i th ionizable atom [89].
Reactivity order Lysine no EP (kcalÆmol)1)
Fig 3 Measurement of global amide H ⁄ D exchange rates by FTIR
spectroscopy Results from a typical H ⁄ D exchange experiment
for a-chymotrypsin (pD 7.1 at 25 C) Arrows highlight both the
decreasing amide II band (N-H; 1550 cm)1) and the increasing
amide II¢ band (N-D; 1450 cm)1).
Trang 5protein stability [61] The decrease in structural
dynamics due to glycosylation could also be attributed
to the decrease in the coulombic energy parameter
because electrostatics are also known to influence
protein dynamics [62] This decrease in the internal
electrostatic energy of the protein as a result of glyco-sylation and its consequences on protein dynamics and stability seems to be in agreement with the notion that glycosylation perturbs the protein’s surrounding solva-tion-shell [36] This could lead to solvent dielectric
Table 2 Kinetic and thermodynamic parameters derived from amide H ⁄ D exchange rates for a-CT and for the various lactose-a-CT and dextran-a-CT conjugates at pH 7.1, 25 C.
(kcalÆmol)1) Lac-a-CT a
Dex-a-CT a
a Average moles of lactose and dextran per mole of a-CT b Aiare the fractions of amide protons in the i th population that exchange with a rate constant k HX,i cGibbs free-energy of microscopic unfolding per mol of peptide hydrogen for the fast exchanging amide protons [48].
Fig 4 Representative a-CT and Lac-a-CT glycoconjugates structures after equilibration of conformational energetics by MD simulations with YASARA Dynamics Coloring scheme: domain 1 (blue), domain 2 (red), catalytic triad (yellow), and mLac glycans (grey) Structures were ren-dered with PYMOL [92].
Trang 6shielding [63] thereby transforming the protein
bio-physical properties from being solvent slaved to
non-slaved [64,65] We have analyzed the effect that
glycosylation has on the protein-solvent hydrogen
bonds and the solvent accessible surface areas for the
protein portion of the conjugates to provide evidence
for this concept within our system While the total
number of hydrogen bonds and solvent accessible area
increases for the conjugates with increased
glycosyla-tion levels, the actual number of protein-solvent
hydro-gen bonds and solvent accessible area decreases for the
protein portion of the conjugates (Tables S2 and S3)
providing support to this notion While it is
tradition-ally believed that increased glycan-protein hydrogen
bonds are responsible for the changes in protein
dynamics and stability, our results clearly show that
this is not necessarily the case These results thus
high-light an alternative fundamental mechanism by which
glycans can modulate the protein’s biophysical
proper-ties (dielectric shielding due to decreased contact of the
protein’s surface with the bulk solvent) This could
have profound implications for the design of novel
protein stabilization strategies as these effects in
princi-ple could be achieved by other types of chemical
modi-fications
Next we performed a statistical analysis of variance
(anova) to determine if the changes in the theoretical
conformational dynamics and energetics parameters
for the modeled structures accurately reflect the
chan-ges in the experimental parameters of the
glycoconju-gates (Fig 5) This was confirmed by the significant
statistical correlation (P < 0.05) found These results
also provide theoretical and experimental support to
the hypothesis that glycosylation leads to the
thermo-dynamic stabilization of proteins through a decrease
in their structural dynamics [7,34,36,58,66,67] These
experimental and theoretical results thus provide
evi-dence that chemical glycosylation does indeed decrease
the global conformational dynamics of the protein
This allowed us to then examine the effects of chemical
glycosylation on the kinetics of enzymatic catalysis from both an experimental and theoretical perspective
Changes in the kinetics of a-CT catalysis upon chemical glycosylation
The catalytic behavior of a-CT after chemical glyco-sylation was determined from the hydrolysis of
Table 3 Global energetic parameters and Debye–Waller temperature factors calculated for the protein portion of a-CT and the various lac-tose-a-CT conjugate structures modeled and submitted MD simulations at pH 7.1, 25 C Energy values in McalÆmol)1.
Glycoconjugate
Energy
Global B-Factor
Fig 5 Statistical correlation analysis ( ANOVA ) between the theoret-ical (*) and experimental global conformational (A) dynamics and (B) energetics parameters determined for the Lac-a-CT conjugates TM values used from [36].
Trang 7Suc-Ala-Ala-Pro-Phe-pNA (Table 4) These
experi-ments revealed that for the a-CT glycoconjugates only
the turnover rate (kcat) was reduced as a function of
the glycan molar content independent of the glycan’s
molecular mass; similarly to the behavior observed for
the global protein dynamics, while the substrate
bind-ing affinity (KM) remained unchanged This reduction
in kcatwith constant KMvalues upon chemical
glycosy-lation agrees with the results found previously during
the study of the catalytic behavior of natural
glycopro-teins [34,68] Interestingly, this reduction in catalysis
was not caused due to inactivation during the chemical
glycosylation of the enzyme because it was previously
demonstrated that native-like activity and dynamics
could be restored at increased temperature regimes for
these glycoconjugates [36] Evaluation of the
glycocon-jugates surface potential reveals that the decreased
kinetics are also not due to a perturbation of the
enzyme’s active site groove electrostatics due to lysine
charge modification (Fig S3)
Because for the substrate used the kcat and KM
parameters are a combination of the reaction’s
individ-ual rate constants (KS, k2, and k3) we determined these
by kinetic chemical dissection with a thio-benzyl (SBzl)
functionalized substrate as previously described by
Stein and coworkers [17] This experiments revealed
that both the kinetics of enzyme acylation (k2) and
deacylation (k3) are reduced by chemical glycosylation,
also as a function of the glycan molar content of the
conjugates (Table 4) In contrast, the substrate binding
step (KS) was unaffected by the chemical glycosylation;
even for the high molecular mass dextran modified
a-CT conjugates, revealing that this type of
modifica-tion did not lead to any active-site steric effects that
could affect the catalytic steps Here we want to point
out that while the values for the acylation and deacyla-tion rates appear similar under the experimental condi-tions employed in this work (25C, pH 7.1, Ca+2 free), acylation does become slightly larger than deacy-lation when the experimental conditions become more traditional (30C, pH 8.0, 10 mm Ca+2) [17] Although the similarity in k2 and k3 values for this substrate might appear strange due to the notion that acylation is rate limiting for amide substrates (k2>k3) and deacylation is rate limiting for ester sub-strate (k2?k3) this generalized assumption is not always accurate for all substrates as previously pointed out by Hedstrom [16] This can be appreciated experi-mentally in the already mentioned work by Stein [17], where they measured the changes in kS, k2, and k3 as a function of pH and temperature for three different sized amide substrates (Suc-F-pNA, Suc-AF-pNA, and Suc-AAPF-pNA) While for the two smaller substrates
k2 is generally smaller than k3, for the larger substrate that we use in our study k2is equivalent to k3
Correlation between the changes in a-CT’s global structural dynamics and enzyme kinetics
Next we performed a statistical correlation analysis (Fig 6) between the structural dynamic (DGHX,1) and catalytic (DG„k2, DG„k3) thermodynamic parameters (Tables 2 and 5) for the glycoconjugates to determine the dependence of the individual rate constants on the changes in the enzyme’s structural dynamics The parameters for both the lactose and dextran conjugates were combined within the analysis of variance to pro-vide a larger and thus statistically more significant sample group This combination was possible because both the dynamic and catalytic parameters derived
Table 4 Kinetic parameters for the a-CT-, lactose-a-CT-, and dextran-a-CT catalyzed hydrolysis of Suc-Ala-Ala-Pro-Phe-pNA at pH 7.1, 25 C.
K S ¼ K M [(k 2 + k 3 ) ⁄ k 3 ] k 2 ¼ k 3 k cat ⁄ (k 3 – k cat ) k 3 is equal to k cat for the hydrolysis of Suc-AAPF-SBzl.
Lac-a-CT
Dex-a-CT
Trang 8were independent of the size of the glycan (Tables 2
and 5, [36]) The analysis revealed that the changes
in these parameters statistically correlate for both
the acylation and deacylation steps (DGHX,1⁄ DG„k2:
R¼ 0.9245, P < 0.0001; DGHX,1⁄ DG„k3: R¼ 0.9370,
P< 0.0001) Interestingly, the reaction’s activation
energy for both steps increases linearly with a decrease
in the structural dynamics of the enzyme (DG„k2¼
1.06DGHX,1+ 9.94; DG„k3¼ 1.12DGHX,1+ 9.65)
This linear relation can be rationalized if one considers
that the enzyme’s dynamical free-energy can be
trans-ferred to the reaction’s activation energy by influencing
the transition-state activation energy (DG„¼ DGTS±
DGDyn) [13–15,42,69,70] Here we want to point out
that although DGHX,1 is an experimental parameter
representative of DGDyn, these two free-energy
func-tions are most probably not on the same energetic
scale, because the timescales of H⁄ D exchange measured in this work (kHX,1) are 103times slower that those observed during catalysis (k2 and k3) This dis-crepancy in timescales between the observed catalytic rates and the rates of the H⁄ D exchange process was previously noted by Klinman and coworkers in their correlation studies on a thermophilic alcohol dehy-drogenase [71] This was attributed to the fact that during the employment of a composite global exchange constant, the rates of the catalytically relevant residues will probably be masked by the rates of slower resi-dues and that the protein conformational fluctuations responsible for H⁄ D exchange are not necessarily in the same timescales as the protein motions of catalysis Nevertheless, the slope values for the linear correla-tions obtained here [which are close to unity (m 1)] clearly support the notion that the dynamical energy
of the enzyme is transferred directly into catalysis The correlations thus provide direct experimental evidence indicating that both acylation and deacylation rates are influenced by the changes in an enzyme’s structural dynamics This observed similar response for k2and k3
to the changes in the enzyme’s structural dynamics could be attributed to the fact that the enzyme employs similar structural and chemical mechanisms for proton transfer during the acylation and deacyla-tion steps but just in a reverse order [16] These results provide support to the kinetic mechanism previously presented by Kawai et al [19,31] in which a substrate-induced conformational change occurs during the for-mation of the first tetrahedral intermediate and during the breakdown of the second tetrahedral intermediate Nonetheless, an observation that becomes clearly evident from our results is that to some degree the
Fig 6 Statistical correlation analysis (ANOVA) between the Gibbs
free-energy of microscopic unfolding per mol of peptide hydrogen
for the fast exchanging amide protons (DG HX,1 ) and the Gibbs
free-energy of activation for reactions (A) acylation (DG „ k2) and (B)
deacylation steps (DG„k 3 ) for the Lac-a-CT (s) and Dex-a-CT (n)
conjugates.
Table 5 Thermodynamic activation parameters derived from the k 2 and k 3 steps for the hydrolysis of Suc-Ala-Ala-Pro-Phe-pNA by a-CT and the various lactose-a-CT, and dextran-a-CT conjugates DG „
ki¼ –RTln(k i h ⁄ k B T).
Glycoconjugate DG„k2(kcalÆmol)1) DG„k3(kcalÆmol)1) Lac-a-CT
Dex-a-CT
Trang 9catalytically relevant dynamics of a-CT appear to be
an intrinsic structural feature of the protein This
notion is indirectly supported by more detailed NMR
N15-relaxation experiments in another enzyme system
(cyclophilin A; prolyl cis-trans isomerase) with the
Suc-AAPF-pNA substrate employed in this work [11],
as this enzyme has similar substrate binding specificity
as a-CT From these experiments it was deduced that
the presence of this type of substrate in the enzyme’s
active site during catalysis does not lead to new
cata-lytically relevant motions that were not already present
within the enzyme Because we previously showed that
for a-CT these catalytically relevant motions are
thermally activated [36] we suggest a minor correction
to the mechanism proposed by Kawai et al in which
the substrate triggered induced-fit conformational
pro-cess is modified by the enzyme’s intrinsic thermally
activated structural mobility (Fig 7) While the results
presented here experimentally highlight the importance
of structural dynamics to rate acceleration by the
enzyme this is clearly not the only contributor to
cata-lysis as it is well known that other phenomena, such as
electrostatic stabilization of the transition state,
forma-tion of covalent intermediates, steric strain, near attack
conformations, substrate desolvation, low barrier
hydrogen bonds, and entropic effects are present in the
mechanism of serine protease catalysis [16,72]
Interest-ingly, the observed relation between the changes in the
enzyme’s internal electrostatics and its structural
dynamics suggests that some of these phenomena may
be interconnected within the catalytic mechanism of
the enzyme
Structural insights into the mechanochemical
nature of a-CT catalysis
A more detailed analysis of the influence of chemical
glycosylation on the dynamics of a-CT from the
theor-etical simulations was additionally performed to gain a
deeper perspective into the mechanism of coupling
between the structural dynamic and functional
proper-ties of the enzyme Although decreases in the dynamics
of catalytically important regions (e.g catalytic triad,
S1 binding site, and L1 specificity site) can certainly be
observed from the analysis of the MD trajectories (Fig S4 and Table S4), these changes are not necessar-ily relevant to the changes in catalysis as the timescales that are accessible to MD simulation techniques are computationally limited so that catalytically important phenomena which occur on larger time scales (e.g col-lective domain motions) are not accurately sampled The Gaussian network model (GNM) was developed
to provide a simple and computationally inexpensive yet accurate description of residue mobilities within the collective vibrational modes of proteins and supra-molecular structures [73,74] Results from this type of calculation have been found to be in excellent agree-ment with X-ray crystallographic B-factors, H⁄ D exchange free energies of amide protons, and NMR-relaxation order parameters [75,76] Due to this GNM has been extensively used to describe the influence of collective structural motions on the functional proper-ties of proteins Because these calculations are tradi-tionally performed on crystal structures their only drawback is that they do not take into consideration environmental variables relevant to protein dynamics (i.e pH, pressure, temperature, and ions) [77] We overcame this problem in our analysis because we per-formed our GNM calculations with structures for which the protein-solvent system was previously equili-brated with these variables during the MD simulations Figure 8 displays the average relative residue mobilities ([(DRi)2]1)2) for the two slowest collective vibrational modes of a-CT and the interresidue mobility cross-correlations within the structure These two slowest modes correspond to the most collective ones which have been found to be the most significant to enzyme function [78,79] The relative mobility plot (Fig 8A) displays that a-CT’s structure is composed of two rigid domains [domain 1 (residues 1–119) and domain 2 (residues 151–245)] linked by an interdomain hinge (residues 120–150) Interestingly, the connection between the two domains and the hinge appears to be via two highly mobile loops (85–105, 160–175) located
at the structural edges of the two domains Positive cross-correlations within the two domains (Fig 8B) reveal how the motions of the residues comprising both domains are correlated and thus move in the
Fig 7 Catalytic steps influenced by the
enzyme’s intrinsic structural dynamics.
Trang 10same direction (squared patterns in the upper left and
in the lower right areas of plot) within the collective
vibrational modes Additionally, significant
cross-correlations are observed between catalytically relevant
residues (Cys42–Ser195, His57–Asp102, His57–Ser195,
Gly140–Ser195, Cys182–Ser214) present in similar and
in separate domains The motion of these collective
vibrational modes can be better appreciated from a
movie generated with the normal mode analysis morph
server at Yale University (http://molmovdb.org)
(Video S1) [80]
When the GNM analysis was applied to the a-CT
glycoconjugates (Fig 9), the relative mobilities of both
interdomain connecting loops (85–105, 160–175) were
largely reduced with an increase in the relative mobility
of the interdomain hinge residues (120–150) and the C-terminal a-helix (230–245) (Fig 10) While most of the glycosylation sites occur in these interdomain con-necting loop regions (Fig 9) we can also see a reduc-tion in the dynamics of regions far away from the glycosylation sites This is most probably due to the very well known fact that the network of hydrogen bonds within the protein’s interior can relay informa-tion to other distant regions of the protein Also the large increase in the mobility of some regions can be expected to occur due to a redistribution of the pro-tein’s configurational dynamics as to minimize the potential entropy loss due to glycosylation [81]
20
A
B
0.5
0.4
0.3
0.2
0.1
0
–0.1
–0.2
40
60
80
100
120
Residue i
140
160
180
200
220
50 100 150 200
Fig 8 Relative mobility ([(DRi) 2 ]1)2) in the slowest two collective vibrational modes versus residue index (A) and interresidue cross-correlation map (B) for a-CT calculated
by GNM Coloring scheme on scale: positive correlations (red), negative correlations (blue).