Macromolecular crowding plays a critical role in the kinetics of enzymatic reactions. Dynamic compartmentalization of biological components in living cells due to liquid-liquid phase separation represents an important cell regulatory mechanism that can increase enzyme concentration locally and influence the diffusion of substrates. In the present study, we probed partitioning of two enzymes (horseradish-peroxidase and urate-oxidase) in a poly(ethylene glycol)-dextran aqueous two-phase system (ATPS) as a function of salt concentration and ion position in the Hofmeister series. Moreover, we investigated enzymatic cascade reactions and their kinetics within the ATPS, which revealed a strong influence of the ion hydration stemming from the background electrolyte on the partitioning coefficients of proteins following the Hofmeister series. As a result, we were able to realize cross-partitioning of two enzymes because of different protein net charges at a chosen pH. Our study reveals a strong dependency of the enzyme activity on the substrate type and crowding agent interaction on the final kinetics of enzymatic reactions in the ATPS and therefore provides substantial implications en route toward dynamic regulation of reactivity in synthetic protocells.
Macromolecular crowding plays a critical role in the kinetics of enzymatic reactions. Dynamic compartmentalization of biological components in living cells due to liquid-liquid phase separation represents an important cell regulatory mechanism that can increase enzyme concentration locally and influence the diffusion of substrates. In the present study, we probed partitioning of two enzymes (horseradish-peroxidase and urate-oxidase) in a poly(ethylene glycol)-dextran aqueous two-phase system (ATPS) as a function of salt concentration and ion position in the Hofmeister series. Moreover, we investigated enzymatic cascade reactions and their kinetics within the ATPS, which revealed a strong influence of the ion hydration stemming from the background electrolyte on the partitioning coefficients of proteins following the Hofmeister series. As a result, we were able to realize cross-partitioning of two enzymes because of different protein net charges at a chosen pH. Our study reveals a strong dependency of the enzyme activity on the substrate type and crowding agent interaction on the final kinetics of enzymatic reactions in the ATPS and therefore provides substantial implications en route toward dynamic regulation of reactivity in synthetic protocells.
The immense complexity
of cell cytosol is responsible for a strong
discrepancy between “in vitro” and “in vivo”
experimental results on enzymatic reaction kinetics. Macromolecular
crowding is one of the main reasons causing this discrepancy,[1] as up to 40% of a cell volume can be occupied
by crowding agents, with a concentration as high as 400 g/L.[2] Such a crowded environment has a significant
influence on various reaction rates,[3,4] protein conformation
and aggregation,[5] substrate diffusion,[6] and water activity.[7] As a result, reactions can be accelerated[8−10] or impaired[11] depending on the rate-limiting step in macromolecular
crowded environments. If the reaction is diffusion-limited, an increasing
crowding agent concentration results in a drop of diffusion and causes
decreased reaction rates. In contrast, if the rate-determining step
is the substrate conversion to the transition (dimer) state, reaction
rates can be increased by promotion of the substrate association.
An additional phenomenon arising from macromolecular crowding is molecular
confinement. Often caused by either cytoskeleton or numerous chaperonin
proteins, this effect is related to the presence of the macromolecules
in small compartments inside a cell. Such confined space can successfully
promote protein folding and prevent escaping of encapsulated polypeptide.[12] Moreover, a high macromolecular content alters
the properties of water by interfering with hydrogen bonding[13] or leaving less solvent molecules for the substrate
and the enzyme that can further cause changes in the enzyme kinetics.More recently, phase separation inside the cell has attracted the
focus of scientists, which is another important phenomenon representing
a consequence of elevated fractions of crowding agents.[14,15] Demixing is the main requirement for the formation of the numerous
membrane-less organelles (P bodies, stress granules, Cajal bodies,
nucleoli, etc.).[16−18] In this way, cells ensure locally high concentrations
of substrates and/or enzymes, while the absence of membranes allows
rapid mass transfer across the permeable phase boundary at the same
time, thus maximizing diffusion. In addition, as a result of only
minor external stimuli, these organelles rapidly mix, rendering them
extremely dynamic. For instance, it has been recently reported that
phase transition is the main trigger for the development of some diseases
such as amyotrophic lateral sclerosis.[19] While it took some time for biology to link this phenomenon to several
aspects of cell behavior, chemists were using phase separation in
a solely aqueous environment as a tool in separation chemistry frequently,
especially via polymer demixing.[20−25] This similarity to living cells represents a basis for an efficient
way to separate proteins, either from other proteins or from debris,
while keeping their native structure intact. As such, the effect of
phase separation in an aqueous two-phase system (ATPS) has been utilized
for the purification of proteins and nanoparticles,[26,27] as well as for the generation of water-in-water emulsions using
Pickering stabilizers.[28−30]Dispersed ATPS is particularly useful for enzymatic
catalysis because
of the existence of fully permeable boundaries together with selective
compartmentalization.[31−37] To this end, a sophisticated control of multienzyme cascades requires
control over the enzyme partitioning in individual phases. Tuning
ATPS by varying pH, adding simple salts, and changing polymer molar
masses or volume ratios can impart crucial differences between the
phases in order to separate enzymes. At the same time, low interfacial
tension and high water content allow maintainance of enzyme activity
and their native state.[38] Owing to electrochemical,
hydrophobic, biospecific, size, and conformational contributions,[24] the partitioning coefficients of proteins in
the ATPS can be influenced by the polymer type and composition. In
this context, enzymatic activity in crowded milieu has been studied
for the purpose of separation.[39] While
the majority reports an increase in the Michaelis–Menten constant
(Km) by addition of crowding agents, others
suggest an amplified affinity of the substrates toward enzymes.[40] Although a tuning of enzymatic reactivity is
conventionally described as a function of their conformation in crowded
microenvironments, the additional effect of limited substrate diffusion
poses a challenge in predicting the kinetics of enzymatic reactivity
and requires optimization toward dynamic regulation of enzymatic reactivity
and the design of consistent and reproducible cascade reactions.In the present study, we investigated the kinetics of an enzymatic
cascade reaction inside an enzyme-loaded ATPS as a function of the
substrate type and crowding agent interaction (Scheme ). Specifically, we found that horseradish-peroxidase
(HRP) partitioning critically depends on the type of background electrolyte,
and the entire trend can be predicted via the position of the respective
ions in the Hofmeister series. En route toward a dynamic regulation
of enzymatic reactivity in crowded environments, we started with an
optimization of the cross-partitioning of enzymatic constituents in
different ATPSs with variations in ionic strength and the type of
added salt, followed by investigations of the effect crowded milieu
has not only on the enzyme’s native conformation but also on
the translational diffusion of several substrates. In addition, insights
on the cross-partitioning of HRP and urate-oxidase (UO) in phase-separated
aqueous mixtures of salts and polymers were combined with investigations
of the translational diffusion of different HRP substrates [guaiacol
and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)
(ABTS) diammonium salt]. A subsequent study of the kinetics of cascade
reactions in the phase-separated aqueous mixtures revealed a strong
influence of the substrate–polymer interactions on the diffusion
rates and enzyme kinetics, while the native structure of the enzymes
remained unaffected, which provides substantial implications for both,
a better understanding of the enzymatic reactions inside living cells
and for the realization of artificial systems capable of dynamically
regulating enzymatic reactivity.
Scheme 1
Schematic Outline of the Experimental
Procedure for Investigating
the Kinetics of Enzymatic Cascade Reactions Inside Phase-Separated
Polymer Mixtures Starting from Unstable Poly(ethylene glycol) (PEG)–Dextran
(Dex) Emulsion through Phase Separation and Enzyme Partitioning Until
Enzymatic Activity Measurements (HRP: Horseradish-Peroxidase; UO:
Urate Oxidase)
Experimental
Section
Materials
HRP (type VI, EC number 1.11.1.7) and UO
(EC number 1.7.3.3) were purchased in the form of lyophilized powder
from Sigma-Aldrich and used without any further purification. ABTSdiammonium salt, deuterium oxide, ethanol, guaiacol, PEG of different
molar masses, phosphoric acid, and various simple salts were obtained
by the same supplier. Coomassie Brilliant Blue G-250 (98%) dye for
Bradford assay was obtained from Thermo Fisher. Dexpolymers of 500k
and 40k were purchased from TCI Deutschland GmbH. Phosphate buffer
0.2 M and pH 8 was obtained from Alfa Aesar.
Partitioning Coefficient
Partitioning coefficients
(K) were determined by comparing the enzyme concentration
in the top and bottom phases (eq ) by performing the Bradford test, as described previously.[41,42]Ctop and Cbottom represent the enzyme concentration in
the top and bottom phases. In brief, Bradford reagents were prepared
by dissolving Coomassie Brilliant Blue G-250 dye in 50 mL of 95% ethanol.
Dye solution was prepared with 100 mL of 85% phosphoric acid and 850
mL of Milli-Q water. Initially, stock solutions of 1000 mg/L HRP and
UO were prepared, and two calibration curves were plotted in the range
from 1 to 20 mg/L. Absorption spectra were recorded with a T70+ UV/vis
spectrometer (PG Instruments Ltd). Two characteristic absorption bands,
at 465 and 595 nm, originate from the free dye and dye–enzyme
complex, respectively. Difference between these absorptions was determined
and plotted as a function of the enzyme concentration, resulting in
the calibration curves. Local enzyme concentrations were determined
by diluting 100 μL of the tested phase with 100 μL of
Milli-Q water and 800 μL of the Bradford reagent. The partitioning
coefficient was then quantified by linearly fitting the measured absorbance
ratios and comparing them to the calibration curve.
Enzymatic Assay
Determination of the cascade reaction
kinetics was performed in a slightly modified manner in comparison
to the standard guaiacol and ABTS assay for peroxidase activity. Specifically,
H2O2 was not added as a separate reagent but
produced by the first enzymatic reaction of oxidation of uric acid
to 5-hydroxyisourate catalyzed by UO as a side product. For this purpose,
0.2 U of both enzymes were mixed with 0.03 mm uric acid,
5 mm phosphate buffer, and guaiacol concentration that was
varied from 0.5 to 50 mm inside the spectrophotometry cell.
Initial increase in absorbance at 470 nm was monitored as a function
of time, and the results were analyzed using the Michaelis–Menten
model (eq )[43]in which v stands for the
rate of the reaction, [S] for the substrate concentration, and Km and vmax refer
to Michaelis–Menten constant and maximum rate of the reaction,
respectively. In the case of a cascade reaction, inhibition by polymers,
guaiacol, and ABTS concentration in separate assays was set to 25
mm or 1 mm, respectively, and w/v % of either PEG
or Dex was set to be between 3 and 15. The initial rate of the formation
of the colored product was recorded at 470 nm for guaiacol, or at
405 nm for ABTS, and plotted as a function of polymer concentration.
Circular Dichroism
Circular dichroism (CD) spectra
were recorded using a 2 mm path length quartz cuvette and an Applied
Photonics Chirascan qCD spectrometer. Samples were prepared by dissolving
10 mg/mL of both enzymes separately, in the absence or presence of
10 w/v % of either PEG or Dex. Entire spectra were recorded in the
far UV range (200–250 nm).
Diffusion-Ordered Spectroscopy-NMR
In order to compare
the translational diffusion coefficients of substrate molecules in
buffer to polymer crowded solutions, diffusion-ordered NMR (DOSY-NMR)
spectroscopy was employed. A 100 mm solution of substrate
(guaiacol or ABTS) was prepared in a total volume of 600 μL
with D2O as solvent. This solution was used as prepared
or enriched by either 60 mg of PEG or Dex, which corresponds to 10
w/v % of polymer in actual samples. DOSY-NMR was performed at 600
MHz (Agilent 600 premium shielded) with the Dbppste_CC pulse sequence,
and the obtained data is presented in the diffusion ordered representation.
Results and Discussion
Tuning the ATPS for Cross-Partitioning
With the purpose
to investigate enzymatic cascade reactions in environments with high
crowding agent concentration, we started with an investigation of
the cross-partitioning of two enzymes in a phase-separated aqueous
polymer mixture comprising a PEG-rich upper and Dex-rich lower phase.
For the cascade reaction scheme, we opted for enzymatic oxidation
of uric acid by UO, which produced peroxide that was subsequently
converted in a peroxidase-mediated oxidation of two different substrates,
namely, guaiacol and ABTS. To investigate the cross-partitioning and
enzyme activity of UO and HRP in aqueous polymer mixtures of neutral
pH, the entire cascade reaction was performed in the individual phase-separated
compartments to facilitate spectrometrical monitoring of the reaction
progress in a transparent medium.Both enzymes display relatively
low molar masses of 33.4 and 44.5 kDa, respectively. One critical
distinction between them is the isoelectric point (IP) of these enzymes,
and therefore, their charge at neutral pH. UO has an IP of 5, while
the IP of HRP utilized in this study is 9. As a consequence, at pH
7, UO is predominantly negatively charged, while at the same time,
the net charge of HRP was positive. This fact enabled an efficient
cross-partitioning of these catalysts. Many previous studies employed
the polymer–saltATPS for partitioning because of very good
partitioning coefficients (K). Nevertheless, enzymes
and proteins, in general, tend to have strong partitioning to the
salt-rich phase, which is beneficial for purification processes but
not for cross-partitioning. In the present case, the polymer–saltATPS gave strong enrichment of the salt-phase with both enzymes, which
has been reported earlier for HRP.[44] In
contrast, partitioning coefficients can be tuned by varying molar
mass of the polymers in the polymer–polymerATPS. In order
to achieve higher K values of HRP, it is necessary
to use lower molar mass PEGs and higher molar mass Dex. In our study,
3k PEG and 500k Dex provided a good starting point for further tailoring
of the system that could eventually be used in the cascade reaction
(Figure S1b). With the aim to achieve best
possible partitioning and to investigate the effect of the polymers
on enzyme kinetics, we chose comparatively high polymer concentration
of 10 w/v % PEG and 15 w/v % Dex (Figure S2). This total polymer concentration of 25 w/v % is well above the
binodal and guaranteed high compositional diversity among top and
bottom phases.[45] The influence of polymer
molar masses on HRP K values in PEG–DexATPS
is summarized in the inset of Figure S2 in the Supporting Information. It is clear that enzyme preference
to one or the other phase can be adjusted simply by changing polymer
molar mass. While in the system with low PEG and high Dex molar mass
(system used in the proceeding parts of the study) that granted K values of HRP higher than 1, HRP preferred a lower Dex
phase in the case of low Dex and high PEG molar mass.Upon optimization
of the polymer system for efficient cross-partitioning
of enzymes, we investigated the influence of buffer concentration,
together with background salt. As displayed in Figure a, varying NaCl concentration while maintaining
buffer at a fixed concentration can induce serious changes in partitioning
coefficients, namely, best cross-partitioning, or highest difference
of K of HRP and UO was achieved at salt concentrations
around 50 mm. Any further salt addition “pushed”
UO to the top, that is, the PEG-rich phase, while similar phenomena
occurred also at lower salt levels. In contrast, partitioning of HRP
was not significantly affected by these variations. At the same time,
the importance of buffer concentration on partitioning was observed.
While HRP partitioning was not affected, elevated phosphate buffer
concentrations notably decreased the efficiency by increasing the K value of UO. The difference in the appearance of the partitioning
graph in phosphate or NaCl buffer can be attributed to variations
in ionic strength. Overall, phosphate and NaCl buffer showed a rather
opposite partitioning trend for HRP, which can be related to Hofmeister
series. Namely, pH 7.8 phosphate buffer contains sodium cations that
counter negatively charged hydrogenphosphate and dihydrogenphosphate
anions. Both of these anions are highly hydrated and are placed at
the opposite side of Hofmeister series in comparison to chloride.
This is the main reason why increasing NaCl concentration promotes
desired protein partitioning, while lowering phosphate buffer concentration.
Therefore, further investigations regarding the correlation of Hofmeister
series with enzyme partitioning were performed. According to our results,
the ATPS composed of 10 w/v % PEG 3k and 15 w/v % Dex 500k with the
50 mm background salt concentration and pH 7.8 set by 5 mm phosphate buffer was identified as an ideal environment for
an investigation of the cascade reaction.
Figure 1
Partitioning coefficient
(K) of HRP (orange dots)
and UO (blue squares) in the presence of various NaCl (a) or phosphate
buffer (b) concentrations.
Partitioning coefficient
(K) of HRP (orange dots)
and UO (blue squares) in the presence of various NaCl (a) or phosphate
buffer (b) concentrations.
Hofmeister Series
Next, the cross-partitioning of positively
charged HRP and negatively charged UO in the PEG–DexATPS was
followed at pH 7.8 in the presence of different salts (Figure ). By using sodium salts with
different anions or chloride salts with various cations, both anionic
and cationic Hofmeister series were probed. In addition, we tried
to extend the classical series with a 1-butyl-3-methylimidazolium
(BMIM+) cation, a common constituent of numerous ionic
liquids.[46−49] All measurements were performed at three different salt concentrations
varying from 1 to 50 mm.
Figure 2
Partitioning coefficient (K) of HRP (circles)
and UO (squares) in the presence of various anion (a) and cation (b)
concentrations. The Na+ cation was used as a counter-ion
for each anion, while Cl– was utilized as the anion
for investigation of the cation effect. All experiments were performed
at pH 7.8 and 5 mm phosphate buffer concentration.
Partitioning coefficient (K) of HRP (circles)
and UO (squares) in the presence of various anion (a) and cation (b)
concentrations. The Na+ cation was used as a counter-ion
for each anion, while Cl– was utilized as the anion
for investigation of the cation effect. All experiments were performed
at pH 7.8 and 5 mm phosphate buffer concentration.As displayed in Figure , efficiency of the cross-partitioning was
comparable for
all ions at low concentration; however, by increasing the concentration,
the hydrophobicity of the ions (position in Hofmeister series) started
to influence the cross-partitioning. While positively charged HRP
tended to be less sensitive to the type of the salt and salt concentration,
UO partitioning coefficient strongly depended on both factors. We
observed a significant increase of UO partitioning coefficient starting
from 0.2 for SCN– up to 0.7 for the most hydrophilic
citrate anion. The dashed line connecting 50 mm case for
two enzymes of the same salt type (resolution), show-cases a notable
drop by decreasing anion hydrophobicity. Most hydrophobic anions generated
highest cross-partitioning, whereas opposite effects were observed
for cations (Figure b). More hydrophobic cations caused both enzymes to distribute equally
in the phases with K values close to 1, while the
most hydrophilic Li+ proved to be most effective for separating
these enzymes in two phases. The latter effect was more pronounced
for UO than in the case of HRP. The importance of the presence of
the background electrolyte type was attributed to the partitioning
of the ions themselves. More hydrophobic ions tend to prefer the less
hydrophilic PEG-rich phase (water-poor phase in comparison to the
water-rich Dex phase) and therefore induced a more negative Donnan
potential between the two phases.[20,50,51]
Enzymatic Activity in the ATPS
To
leverage the Donnan
potential in a beneficial way, we chose a pH at which HRP and UO have
opposite net charges. In such systems, two enzymes tend to partition
in the opposite phases. Adverse effects in the case of very hydrophilic
negatively charged anions, such as citrate, are attributed to a preferred
partitioning of the anion in the Dex-rich phase that can induce undesired
Donnan potential in our system. The same phenomenon occurred for cations,
however, this time using more hydrophilic cations, such as Li+, that prefer the Dex phase, with higher water content, and
caused the desired Donnan effect as well as promoted partitioning
of HRP in the upper phase and UO in the lower phase. Consequently,
using more hydrophilic cations and more hydrophobic anions resulted
in a negative Donnan potential that showed, together with different
chemical compositions of the phases, significant electrochemical potential
for the most efficient cross-partitioning of the two enzymes. It should
be noted that we chose NaCl at 50 mm as a background electrolyte
for all further investigations as a compromise between appropriate
concentrations to cause significant partitioning, while suppressing
interference with the native protein structure.In order to
probe enzymatic activity of the HRP–UO enzyme cascade (Scheme ), an
ATPS composed of 10 w/v % PEG 3k and 15 w/v % Dex 500k, 50 mm NaCl concentration, and pH 7.8 set by 5 mm phosphate buffer
was prepared. In addition, 0.2 U/mL (where U, enzymatic unit, stands
for an amount of enzyme that catalyzes conversion of 1 μmol
of the substrate per minute) of both UO (100 mg/mL) and HRP (5 mg/mL)
was added, and the entire system was allowed to equilibrate overnight.
Subsequently, the ability of the PEG-rich and Dex-rich phases as environments
for the cascade reaction (oxidation of uric acid followed by an oxidation
of the dye i.e., detected spectrophotometrically) was tested and compared
to the case of the absence of polymer (Figure ).
Scheme 2
Two Cascade Reactions Investigated in the Current
Study
Both of them starting from the
oxidation of uric acid, which is followed by subsequent formation
of the colored product from either ABTS or guaiacol oxidation.
Figure 3
Enzyme cascade kinetics performed in Milli-Q
water (grey squares),
Dex-rich bottom phase (blue squares), and PEG-rich top phase (red
squares). Two phases were obtained after equilibration overnight.
All experiments were done at pH 7.8 set by 5 mm phosphate
buffer and 50 mm NaCl as a background electrolyte.
Enzyme cascade kinetics performed in Milli-Q
water (grey squares),
Dex-rich bottom phase (blue squares), and PEG-rich top phase (red
squares). Two phases were obtained after equilibration overnight.
All experiments were done at pH 7.8 set by 5 mm phosphate
buffer and 50 mm NaCl as a background electrolyte.
Two Cascade Reactions Investigated in the Current
Study
Both of them starting from the
oxidation of uric acid, which is followed by subsequent formation
of the colored product from either ABTS or guaiacol oxidation.In this case, partitioning of the enzymes took place
according
to the K values from Figure . Accordingly, by addition of 0.2 U/mL of
both UO and HRP to our ATPS, the PEG-rich phase absorbs approximately
0.264 U/mL of HRP and 0.066 U/mL of UO, while in the Dex-rich phase,
0.132 U/mL of HRP and 0.333 U/mL of UO are present. This is a complex
system with an unforeseeable activity of both phases; however, because
of the significantly denser Dex-rich phase, we expected to observe
lower activities in that phase solely because of the hydrodynamic
effect. Nevertheless, we observed opposite results. Both PEG and Dex
phases exhibited lower Vmax (maximum rate
of the reaction) values of 0.03 and 0.34 μmol/s, respectively,
in comparison to 0.75 μmol/s for the phosphate buffer system.
The main cause of the overall lower activity behavior was attributed
to partitioning, which, however, did not explain the high enzymatic
activity of the lower Dex-phase and barely measureable activity of
the upper PEG-phase. The values regarding the Michaelis–Menten
fit are collated in Table S1. It is evident
that the upper ATPS phase has one order of magnitude lower Vmax, although, all Km are similar. Thus, the observed behavior could be explained by the
ability of the PEG polymer to interfere with hydrophobic interactions
inside the protein structure and partially denature enzymes, as previously
reported for some specific interaction or crowding agents with substrates.[5,52]In pursuance of understanding the previously described discrepancies
in enzymatic activity of the two phases, we performed several probes
investigating reaction rates of cascade reaction in pure PEG or Dex
solution at different concentrations without a formation of the ATPS
but with the same ionic strength and pH values as in ATPS assays.
For this purpose, 0.2 U of both enzymes were added to the reaction
mixture together with 0.03 mm of uric acid, 50 mm of guaiacol, or 1 mm ABTS and varying concentrations of
either PEG or Dex. Initial rates of this reaction were normalized
by the rate of the reaction without any polymer and plotted versus
the polymer concentration (Figure ). As a result, even elevated Dex concentration did
not alter the rate of the reaction significantly for both substrates
(with an exception of 18 w/v % for ABTS as a substrate). In contrast,
already 10 w/v % of the PEG rate of the cascade reaction dropped close
to 80% for guaiacol and almost to 0% for ABTS. Alongside with our
findings that PEG lowers the rate of the reaction, the different degree
of inhibition depending on the substrate type remained to be studied.
Figure 4
Relative
activity of the enzyme cascade, measured by absorbance
of guaiacol or the ABTS oxidation product in the presence of either
PEG 3k (squares) or Dex 500k (circles). All experiments were performed
at pH 7.8 set by 5 mm phosphate buffer and 50 mm NaCl as a background electrolyte.
Relative
activity of the enzyme cascade, measured by absorbance
of guaiacol or the ABTS oxidation product in the presence of either
PEG 3k (squares) or Dex 500k (circles). All experiments were performed
at pH 7.8 set by 5 mm phosphate buffer and 50 mm NaCl as a background electrolyte.
CD Spectroscopy of Enzymes in Different Media
CD spectroscopy
proved to be a useful tool to investigate any conformational changes
in the protein secondary structure. Namely, CD spectra of both enzymes
were recorded in the range from 200 to 250 nm in phosphate buffer
and in the presence of 10 w/v % of either PEG or Dex (Figure ). Both enzyme spectra exhibited
characteristic maxima at 208 and 220 nm, typical for proteins with
a high content of an α-helical structure. Upon addition of 10
w/v % of any of the two polymers, no significant shift of the peaks
was observed. Signal intensity was also maintained providing a proof
of a preserved enzyme secondary helical structure upon polymer addition.
Thus, the variations in activity of the enzymes in the ATPS did not
stem from the enzyme denaturation or any interaction between polymers
and enzymes but can be solely attributed to the interaction between
substrates and polymers.
Figure 5
CD spectra of HRP (a) and UO (b) in the absence
of any polymer
(black squares) and in the presence of PEG 3k (red triangles) or Dex
500k (green dots).
CD spectra of HRP (a) and UO (b) in the absence
of any polymer
(black squares) and in the presence of PEG 3k (red triangles) or Dex
500k (green dots).
Substrate Diffusion Coefficient
in Different Media
Therefore, we next set out to investigate
the substrate mobility
in the respective medium via measurements of diffusion. In order to
measure the translational diffusion of substrates in the absence and
presence of polymers, we employed DOSY-NMR. Higher concentrations
of the polymer showed to significantly influence the measurement and
simply screen the peaks that originate from the substrate molecules.
In addition, this PEG dose was enough to induce 15 and 90% drop of
activity for guaiacol and ABTS as the substrates, respectively. The
obtained results are displayed in Figure , where a distribution of the diffusion coefficients
(D) is plotted against characteristic chemical shifts.
For the better overview of the results, diffusion coefficients are
given in Table S2 together with a percentage
of the translational diffusion decrease. We expected a higher decrease
of the diffusion for the more viscous Dex media as it is projected
by the Stokes–Einstein equation. In spite of that, diffusion
coefficient is not always proportional to viscosity because of possible
short-range interaction that occurs.[53] In
this scenario, diffusion is governed partially by media viscosity
but majorly by specific short-range interaction.[54]
Figure 6
DOSY-NMR of guaiacol (a) and ABTS (b) in the absence of any polymers
(red signal) and in the presence of 10 w/v % of PEG (green signals)
or Dex (blue signals).
DOSY-NMR of guaiacol (a) and ABTS (b) in the absence of any polymers
(red signal) and in the presence of 10 w/v % of PEG (green signals)
or Dex (blue signals).As revealed by our studies,
a drop in diffusion in Dex media of
30 and 39% for guaiacol and ABTS, respectively, could be explained
by the increased viscosity of the system. Nevertheless, these values
are not comparable to 44 and 52% drop of the same compounds in media
that are composed of PEG as a crowding agent, which provided for a
strong indication of specific attractive forces between both guaiacol–PEG
and ABTS–PEG that influence the diffusion. A small shift in
the proton signal from 0.95 to 1.09 was attributed to a stronger ABTS–PEG
interaction that can potentially cause a decrease in diffusion coefficient,
and subsequently, in reduced ability of HRP to oxidize this substrate.
A similar shift has not been observed in guaiacol–PEG mixture.
Taken together, interactions between PEG and both substrates can decrease
the rate of enzymatic reaction significantly. Similar interactions
are not present among complex-branched polysaccharides, such as Dex,
and substrates, which ensured high enzyme catalyzed reaction rates.
Conclusions
In conclusion, the partitioning of the two enzymes,
HRP and UO,
was investigated in different ATPSs with variations in ionic strength
and the type of added salt. High molar mass Dex and low molar mass
PEG were chosen as ATPS constituents because of the most efficient
cross-partitioning of previously mentioned cascade enzymes. The effect
of the ion hydrophobicity on partitioning of proteins is described
in terms of the Hofmeister series. Moreover, kinetics of cascade reactions
in crowded environments were investigated. Discrepancies in reaction
rates between the PEG-rich and Dex-rich phase were observed that could
not be explained by changes in native enzyme confirmation as proven
proved by CD measurements. Because the difference in activity was
dependent on the type of the utilized HRP substrate, this phenomenon
was correlated to different translational diffusion coefficients of
the different substrates (guaiacol and ABTS) in solutions of high
polymer concentration. The associated understanding of the enzyme
partitioning and influences on the kinetics of enzymatic reactions
in the crowded ATPS will serve as an important tool for separation
chemistry and has several implications for understanding and dynamically
regulating enzymatic reactivity in macromolecular crowded environments
such as cells.
Authors: Daniel C Dewey; Christopher A Strulson; David N Cacace; Philip C Bevilacqua; Christine D Keating Journal: Nat Commun Date: 2014-08-20 Impact factor: 14.919