Andreas Englert1, Julian F Vogel1, Tim Bergner2, Jessica Loske1, Max von Delius1. 1. Institute of Organic Chemistry, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany. 2. Central Facility for Electron Microscopy, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany.
Abstract
A growing number of out-of-equilibrium systems have been created and investigated in chemical laboratories over the past decade. One way to achieve this is to create a reaction cycle, in which the forward reaction is driven by a chemical fuel and the backward reaction follows a different pathway. Such dissipative reaction networks are still relatively rare, however, and most non-enzymatic examples are based on the carbodiimide-driven generation of carboxylic acid anhydrides. In this work, we describe a dissipative reaction network that comprises the chemically fueled formation of phosphoramidates from natural ribonucleotides (e.g., GMP or AMP) and phosphoramidate hydrolysis as a mild backward reaction. Because the individual reactions are subject to a multitude of interconnected parameters, the software-assisted tool "Design of Experiments" (DoE) was a great asset for optimizing and understanding the network. One notable insight was the stark effect of the nucleophilic catalyst 1-ethylimidazole (EtIm) on the hydrolysis rate, which is reminiscent of the action of the histidine group in phosphoramidase enzymes (e.g., HINT1). We were also able to use the reaction cycle to generate transient self-assemblies, which were characterized by dynamic light scattering (DLS), confocal microscopy (CLSM), and cryogenic transmission electron microscopy (cryo-TEM). Because these compartments are based on prebiotically plausible building blocks, our findings may have relevance for origin-of-life scenarios.
A growing number of out-of-equilibrium systems have been created and investigated in chemical laboratories over the past decade. One way to achieve this is to create a reaction cycle, in which the forward reaction is driven by a chemical fuel and the backward reaction follows a different pathway. Such dissipative reaction networks are still relatively rare, however, and most non-enzymatic examples are based on the carbodiimide-driven generation of carboxylic acid anhydrides. In this work, we describe a dissipative reaction network that comprises the chemically fueled formation of phosphoramidates from natural ribonucleotides (e.g., GMP or AMP) and phosphoramidate hydrolysis as a mild backward reaction. Because the individual reactions are subject to a multitude of interconnected parameters, the software-assisted tool "Design of Experiments" (DoE) was a great asset for optimizing and understanding the network. One notable insight was the stark effect of the nucleophilic catalyst 1-ethylimidazole (EtIm) on the hydrolysis rate, which is reminiscent of the action of the histidine group in phosphoramidase enzymes (e.g., HINT1). We were also able to use the reaction cycle to generate transient self-assemblies, which were characterized by dynamic light scattering (DLS), confocal microscopy (CLSM), and cryogenic transmission electron microscopy (cryo-TEM). Because these compartments are based on prebiotically plausible building blocks, our findings may have relevance for origin-of-life scenarios.
At the molecular level, life is an out-of-equilibrium
state that
is characterized by a multitude of interconnected networks, each exhibiting
complex collective behavior.[1] The emergent
functions of such systems can only be maintained as long as energy
is dissipated in the form of the consumption of high-energy molecules
that stem from metabolic pathways.[2] In
some cases, most notably in actin filaments[3] and microtubules,[4] chemical fuels drive
the self-assembly of organic building blocks into transient supramolecular
scaffolds. Over the past decade, researchers in the field of systems
chemistry[5−7] have begun to mimic these natural processes, using
artificial building blocks, chemical fuels,[8] and catalysts.[9−13]Because the most widespread fuels in nature are adenosine
triphosphate
(ATP) and guanosine triphosphate (GTP), it is not surprising that
artificial dissipative self-assemblies (DSA) have been realized with
these compounds.[14−19] However, research on artificial systems is not limited to natural
fuels. Pioneering work by Boekhoven, Eelkema, and van Esch[20,21] for instance has been based on the alkylating agent dimethyl sulfate.
Other fuels used to generate DSA include dithionate,[22] perborate,[23] cyclodextrin,[24] acetic anhydride,[25] and amino acids.[26] Arguably, the most
popular chemical fuels in artificial dissipative networks, especially
in those that do not require enzyme catalysis, are carbodiimides such
as 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC).[27] Although the prebiotic plausibility of carbodiimide
fuels is questionable,[28−30] EDC is valuable as a model reagent because of its
water solubility, slow background reaction with water, and its exceptionally
well-understood reaction profile.[31] For
these reasons, EDC has been successfully used in a large number of
recent studies on transient behavior. Dynamic vesicles have been formed
by EDC-fueled self-assembly[32] and were
shown to give rise to selective behavior due to phase separation.[33] Comparable systems were shown to facilitate
transient macrocyclization (including host–guest chemistry),[34,35] time-controlled tuning of polymer properties,[36,37] control over molecular emission,[38] and
fueled self-regulating hydrogels.[39−41] In all cases, EDC is
used to transform carboxylic acids into the corresponding anhydrides.Furthermore, carbodiimides recently fueled the ratcheted and directional
motion of molecular machines.[42,43] Mimicking nature’s
dissipative systems can be crucial to understanding the origins of
life, which requires out-of-equilibrium systems featuring self-replication,
metabolism, and compartmentalization.[23,44,45] Recently, phosphoramidates have been identified as
a compound class with prebiotic relevance. For example, efficient
non-enzymatic primer extension of 3′-NP DNA[46,47] and template-directed synthesis of 3N′-5P′-RNA[48] have been reported (self-replication). Moreover,
it was shown that prebiotically plausible stereoselective aminoacyl-RNA
synthesis[28] and even ribosome-like translation[49] (metabolism) contain phosphoramidate intermediates.
Both the formation[50−53] and the hydrolysis[54−57] as well as the self-assembly[58,59] of phosphoramidates
have been studied individually. However, a fueled reaction network
based on phosphoramidates and DSA controlled by this chemistry is
elusive.Herein, we present a dissipative reaction cycle, in
which phosphoramidates
are formed by the EDC-driven reaction of natural ribonucleotides with
primary amines (Figure A). The hydrolytic backward reaction proceeds under relatively mild
conditions, as long as it is catalyzed by 1-ethylimidazole (EtIm).
Both the forward and the backward reaction were investigated by a
broad design of experiments (DoE) approach (Figure B), which not only helped optimize the two
fundamental processes but also revealed unexpected interactions between
system parameters. Establishing ideal conditions for the full reaction
cycle allowed us to demonstrate the transient self-assembly of rudimentary
compartments comprising prebiotically plausible building blocks.
Figure 1
Reaction
scheme and outline. (A) General scheme of the dissipative
reaction cycle. (B) Objectives of this study and role of design of
experiments (DoE). (C) Detailed reaction scheme of the reaction network,
including the forward reaction from monophosphates NMP to phosphoramidates
PA (red arrows), hydrolysis pathways (blue arrows), and side reactions
(black arrows). B: nucleobase.
Reaction
scheme and outline. (A) General scheme of the dissipative
reaction cycle. (B) Objectives of this study and role of design of
experiments (DoE). (C) Detailed reaction scheme of the reaction network,
including the forward reaction from monophosphates NMP to phosphoramidates
PA (red arrows), hydrolysis pathways (blue arrows), and side reactions
(black arrows). B: nucleobase.
Results and Discussion
Phosphoramidate-Based Reaction Cycle
The reaction network
comprises a three-step forward reaction from natural ribonucleotide
monophosphate (NMP) to the corresponding phosphoramidate (PA, red
arrows in Figure C).
First, the phosphate group of the NMPs reacts with the chemical fuel
1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) to form an activated
phosphate (EDC-NMP). Richert and co-workers have shown that this intermediate
is highly sensitive to hydrolysis, while 1-ethylimidazole (EtIm) is
used as a catalyst to capture the EDC-NMP and form a more stable EtIm-activated
phosphate (EtIm-NMP).[60]Nucleophilic
attack of a primary amine leads to the formation of a phosphoramidate.
As we describe below in more details, the conversion of the PA back
to the EtIm-NMP plays a crucial role in the (mild) hydrolysis of the
PA, which is required to close the dissipative reaction cycle. Hydrolysis
can occur for all intermediates along the forward reaction (blue arrows
in Figure C). Additionally,
some side reactions need to be taken into account (black arrows in Figure C). The activated
phosphates can undergo a nucleophilic attack with the 2′- or
3′-hydroxy group of another ribose to form a phosphodiester
(pNpN) or with a second 5′-phosphate to form a pyrophosphate
(NppN). As previously reported by our group, pyrophosphates can form
rapidly and are kinetically stable toward hydrolysis,[61] which makes this side-reaction a problematic kinetic sink,
while phosphodiesters hydrolyze more readily and played a negligible
role in this study. Finally, we observed small amounts of O-acyl- and N-acyl-adducts (EDC-PA) between
PA and EDC. The full chemical structures of these side products are
depicted in Scheme S1. We refer to EDC
as a “chemical fuel” to signify that the difference
in free energy between EDC and the corresponding urea waste (EDU)
is used to maintain a certain function,[8] e.g., the generation of transient vesicles. In a proof-of-principle
experiment, we show that continuous addition of the fuel leads to
an out-of-equilibrium steady state, where fuel is dissipated over
a long period of time (see the Supporting Information, Section VII).The three main objectives of this work are
illustrated in Figure B. We first aimed
to investigate and optimize the PA hydrolysis and PA formation reactions
individually. Parameter optimization for chemical reactions can be
a time-consuming and difficult endeavor, especially when parameters
are involved that interact with each other and thus cannot be considered
independently. Classical “One Factor at a Time” (OFAT)
strategies are not able to reveal such interactions and do not lead
to efficient experimental designs.[62,63] Particularly,
in dissipative systems that possess two different reaction pathways,
an OFAT approach might not be advisable. Computer-assisted design
tools such as design of experiments (DoE) are well suited for the
efficient evaluation of systems made up of interacting parameters
and are applied in modern synthesis laboratories for the characterization
and optimization of reactions.[64,65] Furthermore, by conducting
a sufficient number of runs, response surface methods (RSM) can be
used within a DoE to establish a predictive description model. To
the best of our knowledge, the use of DoE has not yet been reported
in the context of dissipative reaction cycles.Our second objective
was to combine the findings of both DoEs to
find a parameter set that satisfies all criteria to allow several
reaction cycles upon chemical fueling. Finally, we aimed to use the
reaction cycle for the dissipative self-assembly of prebiotically
relevant compartments.
Optimizing and Understanding the Hydrolysis of Phosphoramidates
To establish a dissipative reaction cycle, the backward reaction
should proceed at a reasonable rate. We used the hydrolysis reaction
depicted in Figure A as a reference system to evaluate whether phosphoramidates of primary
amines fulfill this requirement. Bn-GMP was chosen as a model compound,
and the respective reactions were performed at 25 mM concentration.
The amount of Bn-GMP was determined by HPLC (with an internal standard)
and was monitored throughout experiments to assess the degree of hydrolysis.
To determine general trends and construct a predictive description
model, we applied the DoE methodology to this system. More specifically,
the reactions were performed in two blocks according to a central
composite design (CCD) (Table S3), correlating
the influence of the input factors temperature, pH, EtIm concentration,
and time with the amount of unhydrolyzed Bn-GMP. The description model
and the respective fit statistics are shown and discussed in detail
in the Supporting Information (Section
IV).
Figure 2
(A) Reaction scheme of Bn-GMP hydrolysis. (B) One-factor diagrams
and interaction diagrams with 95% confidence intervals (shaded area).
The constant parameter sets for each diagram are indicated. General
trends observed for the hydrolysis reaction are summarized at the
bottom. (C) Predicted response surface of the hydrolysis reaction.
The flags C1, C2, and C3 indicate the experimental confirmation runs
of the model. (D) Table of the confirmation runs, comparing the experimentally
observed values with the response predicted by DoE.
(A) Reaction scheme of Bn-GMP hydrolysis. (B) One-factor diagrams
and interaction diagrams with 95% confidence intervals (shaded area).
The constant parameter sets for each diagram are indicated. General
trends observed for the hydrolysis reaction are summarized at the
bottom. (C) Predicted response surface of the hydrolysis reaction.
The flags C1, C2, and C3 indicate the experimental confirmation runs
of the model. (D) Table of the confirmation runs, comparing the experimentally
observed values with the response predicted by DoE.The observed trends are depicted schematically
in Figure B. An increase
in temperature,
a more acidic pH, longer reaction times, and an increased amount of
EtIm result in faster hydrolysis. The one-factor diagrams and interaction
diagrams in Figure B show the quantitative effect of the individual factors on the response
for different factor settings. While the correlation of reaction progress
with temperature and time is unremarkable, the effect of pH is more
interesting. In principle, we can explain this finding by the higher
degree of protonation of the basic nitrogen atoms in the phosphoramidate
and the phosphorimidazolide intermediate. This protonation is known
to affect the rate of elimination of the leaving group in this “SN2-like” reaction.[55] The
pronounced influence of the EtIm amount on the hydrolysis was surprising,
even though it had been reported previously that imidazole derivatives
can play a role in the hydrolysis of phosphoramidates.[57] EtIm as a hydrolysis catalyst offers a great
advantage compared to other degradation reactions of dissipative reaction
cycles since it serves as an additional parameter allowing one to
fine-tune the reaction.One benefit of DoE analysis is that
factors are not treated independently,
and it therefore accounts for the fact that adjusting one factor may
be highly dependent on the setting of another factor. We observed
this behavior clearly in the pH/temperature and EtIm/temperature interactions
depicted in Figure B (right-hand side). The more the red and blue slopes in the interaction
diagrams differ from each other, the stronger the interaction. For
instance, increasing the pH at 60 °C leads to starkly increased
hydrolysis when compared to the moderate increase observed at 40 °C.
Because such interactions are likely to exist also in other reaction
networks, we believe the DoE methodology can generally be applied
to help identify suitable reaction conditions.We chose DoE
based on RSM designs for the analysis of the system
because this approach not only reveals trends within the recorded
data set but also allows predictions based on the description model.
This predictive capacity is demonstrated in the temperature-pH-response
surface shown in Figure C. As mentioned above, the goal of this DoE was to achieve nearly
complete hydrolysis of the reference compound within a reasonable
timeframe. Therefore, three confirmation runs (C1–C3) were
performed under conditions where a Bn-GMP amount below 15% after 24
h was predicted by the model. The mean values of the experimentally
observed Bn-GMP amount of three parallel measurements are depicted
in Figure D. All three
confirmation runs are in good agreement with the predictions and within
the interval of the prediction error, thus corroborating that we can
predict diverse conditions for reasonably fast hydrolysis.
Optimizing and Understanding the EDC-Driven Formation of Phosphoramidates
After evaluating the hydrolysis of phosphoramidates (backward reaction),
we turned our attention to the forward reaction. To ensure comparability,
we used the same model compounds as above, and the only crucial difference
from the hydrolysis reaction is the presence of EDC (Figure A). Again, the amount of Bn-GMP
was determined by HPLC and used as a response to evaluate the efficiency
of the reaction. This response was correlated with the five input
factors pH, temperature, concentration of GMP, time, and equivalents
of EDC. The factor settings, the experimental design, and the respective
fit statistics of the description model are shown in the Supporting Information (Section V). As shown
in Figure B, increasing
the pH, equivalents of EDC, and the concentration of GMP results in
a more efficient Bn-GMP formation. The impact of these factors is
illustrated in Figure B in the one-factor and interaction diagrams. Especially, the pH
turned out to influence the reaction tremendously (steep curve in Figure B, top left). Under
acidic conditions, the protonated state of the amine is favored, which
impedes its effective nucleophilicity. Additionally, as the hydrolysis
of Bn-GMP proceeds faster at a lower pH, it is reasonable to assume
that the reactive intermediates (EDC-GMP and EtIm-GMP) also hydrolyze
faster at a lower pH. The influence of EDC and GMP can be explained
accordingly. Increasing the amount of the activation agent EDC counterbalances
the hydrolysis of reactive intermediates, while a higher GMP concentration
naturally increases the forward rate (according to collision theory).
Figure 3
(A) Reaction
scheme of Bn-GMP formation. (B) One-factor diagrams
and interaction diagrams with 95% confidence intervals (shaded area).
All experiments were performed containing five equivalents of EtIm.
Further parameter sets held constant are stated for each diagram.
In the full factorial representation (cube), the data given here correspond
to a temperature of 40 °C and an 8 mM GMP concentration. The
general trends of the PA formation reaction are summarized at the
bottom. (C) Predicted response surface of the formation reaction.
The flags C1, C2, and C3 indicate the experimental confirmation runs
of the model. (D) Table of the confirmation runs, comparing the experimentally
observed values with the response predicted by DoE.
(A) Reaction
scheme of Bn-GMP formation. (B) One-factor diagrams
and interaction diagrams with 95% confidence intervals (shaded area).
All experiments were performed containing five equivalents of EtIm.
Further parameter sets held constant are stated for each diagram.
In the full factorial representation (cube), the data given here correspond
to a temperature of 40 °C and an 8 mM GMP concentration. The
general trends of the PA formation reaction are summarized at the
bottom. (C) Predicted response surface of the formation reaction.
The flags C1, C2, and C3 indicate the experimental confirmation runs
of the model. (D) Table of the confirmation runs, comparing the experimentally
observed values with the response predicted by DoE.In the case of the input factors temperature and
time, no general
trend could be established, as shown in the first interaction diagram
of Figure B (for further
time interactions, see Figure S8). The
inverse slopes of the two graphs indicate that high Bn-GMP yields
are observed after long reaction times at relatively low temperatures,
while high yields can also be observed after short reaction times
at relatively high temperatures. Such a behavior is characteristic
of dissipative reaction cycles as the backward reaction dominates
earlier at high temperatures. Moreover, we found that side reactions
are also capable of diminishing the yield of the reaction, as illustrated
in the second interaction diagram (Figure B).Even though EDC reacts preferentially
with the phosphate, a high
excess of this reagent leads to the formation of EDC adducts (as shown
in the reaction scheme in Figure C) and therefore an otherwise unexplainable decrease
in Bn-GMP formation. The importance of multifactor interactions is
further highlighted by the cubic representation of the parameters
time, EDC, and pH (cube in Figure B).[66] Only careful adjustment
of all three parameters allowed a considerable increase of the reaction
efficiency. Figure C shows the EDC-pH-response surface of the investigated reaction
as predicted by DoE. As with the hydrolysis reaction, confirmation
runs (C1–C3) were conducted at conditions where the reaction
yields were predicted to be above 50%. All three confirmation runs
agree with the predictions and are within the interval of the prediction
error (Figure D).
Closing the Dissipative Reaction Cycle
By conducting
the DoEs, we found general system trends and description models. With
these results in hand, our next objective was to use the reference
system to establish a dissipative reaction cycle by combining the
above findings. Figure A shows a contour plot that combines both models and was used to
identify conditions suited for an efficient dissipative reaction cycle.
Specifically, the green-shaded area represents conditions under which
the PA hydrolysis occurs nearly completely within 24 h, but the PA
formation is still quite effective (yield higher than 50%). To test
the validity of the model, we fueled the dissipative system four times
with EDC under these conditions (55 °C, pH = 6.1; see Table in Figure A). Figure B shows the relative amount
of Bn-GMP over four cycles, validating that the predicted conditions
are suited to establish a dissipative reaction cycle. It must be noted,
however, that side products such as the pyrophosphate GppG form and
accumulate over the course of several reaction cycles. For applications
targeting many cycles in dissipative systems, the exploration of catalysts
that enable the hydrolysis of pyrophosphates may be necessary.[61]
Figure 4
Combination of PA hydrolysis and PA formation (DoE). (A)
Graphical
optimization of temperature and pH for a dissipative reaction cycle.
The desired conditions allow for hydrolysis below 10% of the residual
Bn-GMP within 24 h (red lines) and sufficient (over 50%) Bn-GMP formation
(blue lines) at the same time. The table specifies the remaining system
parameters. (B) Four dissipative reaction cycles under optimized conditions.
Relative amounts of Bn-GMP (red), GMP (blue, dotted line), and GppG
(light gray) as determined by HPLC. Error bars indicate standard deviation
based on triplicate reaction runs. Data points are connected to guide
the eye.
Combination of PA hydrolysis and PA formation (DoE). (A)
Graphical
optimization of temperature and pH for a dissipative reaction cycle.
The desired conditions allow for hydrolysis below 10% of the residual
Bn-GMP within 24 h (red lines) and sufficient (over 50%) Bn-GMP formation
(blue lines) at the same time. The table specifies the remaining system
parameters. (B) Four dissipative reaction cycles under optimized conditions.
Relative amounts of Bn-GMP (red), GMP (blue, dotted line), and GppG
(light gray) as determined by HPLC. Error bars indicate standard deviation
based on triplicate reaction runs. Data points are connected to guide
the eye.
Transient Self-Assembly
Having optimized the dissipative
reaction cycle, we wondered whether the combination of an alkylamine
with a monophosphate could give rise to the transient self-assembly
of compartments (Figure A). Due to the simplicity of all building blocks (except perhaps
the fuel), such a system may have prebiotic relevance.[28,47,49] Furthermore, the use of an alkylamine
rather than benzylamine demonstrates the relatively broad scope of
the approach. Using alkylamines required only moderate adjustment
of the reaction conditions, probably due to differences in pKa.[67] We dissolved
the natural ribonucleotide adenosine 5’-monophosphate (AMP)
(75 mM) in a buffer, containing 5 equiv EtIm and added 1.5 equiv heptylamine.
After adjusting the pH to 6.5 (at 55 °C) we obtained a clear,
molecularly dissolved solution. Addition of 2 equiv EDC led to the
rapid formation of the corresponding phosphoramidate (C7-PA), as determined by quantitative HPLC (Figures B and S12). After
1.5 h, 60% of the AMP was converted to C7-PA. As soon as
the fuel was depleted, C7-PA began to hydrolyze, and its
amount dropped below 10% within 24 h. These results imply that the
“sweet spot” identified in Figure A for benzyl amine is also viable for alkyl
amines with only minor adjustments, and the stage was therefore set
for the coupling of dissipative cycles with hydrophobic self-assembly.
As shown in Figure B, we were able to demonstrate that this reaction cycle can be (re-)fueled
at least four times. The main side-reaction is the formation of adenosine
pyrophosphate (AppA). While the reaction was performed at 55 °C,
we were only able to observe transient self-assembly at lower temperatures,
e.g., at 25 °C. When quickly cooling the reaction mixture to
25 °C after 1.5 h, we obtained a turbid solution, and dynamic
light scattering (DLS) revealed the formation of aggregates with an
average hydrodynamic diameter of around 2.6 μm (Figures C,D and S13). After 24 h, the solutions were clear at 25 °C,
and DLS indicated that the aggregates had vanished upon hydrolysis. Figure C shows the hydrodynamic
diameter as an average of five measurements, corroborating the good
reversibility of the self-assembly during four reaction cycles. Finally,
confocal laser scanning microscopy (CLSM) with 2.5 μM “Cumarin
153” (C153) as a fluorescent dye was used to directly visualize
the transient formation of aggregates (Figures E,F and S14).
We scanned the sample along the whole z-axis to obtain a three-dimensional
image of the solution (the color code in Figure E indicates the depth of the vesicle along
the z-axis). While we initially observed no aggregates,
a large number of spherical objects with diameters of approx 1–5
μm were visible 1.5 h after the addition of EDC. After 24 h,
when most of the C7-PA had hydrolyzed, there were only
very few aggregates left. Figure F shows an enlarged two-dimensional CLSM image (after
1.5 h) of two aggregates, which would be consistent with a vesicular
or colloidal structure. Cryogenic transmission electron microscopy
(cryo-TEM, Figures S15 and S16) is rather
indicative of vesicles. Irrespective of the aggregate structure, the
DLS and CLSM measurements reveal how the phosphoramidate-based reaction
cycle we introduce herein can lead to compartmentalization under primordial
conditions with prebiotically plausible building blocks such as ribonucleotides[68] and primary amines.[69] Our finding that the activation chemistry proceeded most efficiently
at 55 °C, whereas compartments were only present at 25 °C,
is interesting in the context of studies on thermophoresis, which
showed that temperature gradients (e.g., at hydrothermal vents)[70] can lead to the enrichment of prebiotic molecules[71] and large oligomers[72] as well as the fission of lipid vesicles that can serve as a model
for protocells.[73] Despite the use of EDC
as a model reagent, our work therefore suggests that temperature gradients
could have prebiotic relevance not only for physical or simple chemical
processes but also for the regulation of individual processes in chemical
reaction cycles and for the dynamic kinetic stability of dissipative
compartments.
Figure 5
Transient self-assembly of compartments. (A) Reaction
scheme of
the EDC-driven reaction cycle. (B) Four dissipative reaction cycles
(performed at 55 °C). Relative amounts of C7-PA (red),
AMP (blue, dotted line), and AppA (light gray) as determined by HPLC.
Error bars indicate standard deviation based on triplicate reaction
runs. Data points are connected to guide the eye. (C) DLS measurements
at different time points during the four cycles (measured at 25 °C).
The average distributions in mean hydrodynamic diameter of five parallel
measurements are shown. (D) Average size of the aggregates at different
time points as measured by DLS. Error bars indicate one standard deviation.
Dotted lines are displayed to guide the eye. Inlets show clear solutions
in the non-aggregated and turbid solutions in the aggregated state,
respectively (at 25 °C). (E) 3D-CLSM images during one reaction
cycle, measured at 25 °C and along the whole z-axis. The color code indicates the depth of the aggregates in the
sample. (F) 2D-CLSM image of vesicular structures after 1.5 h with
magnification. As a fluorescent dye 2.5 μM C153 was used.
Transient self-assembly of compartments. (A) Reaction
scheme of
the EDC-driven reaction cycle. (B) Four dissipative reaction cycles
(performed at 55 °C). Relative amounts of C7-PA (red),
AMP (blue, dotted line), and AppA (light gray) as determined by HPLC.
Error bars indicate standard deviation based on triplicate reaction
runs. Data points are connected to guide the eye. (C) DLS measurements
at different time points during the four cycles (measured at 25 °C).
The average distributions in mean hydrodynamic diameter of five parallel
measurements are shown. (D) Average size of the aggregates at different
time points as measured by DLS. Error bars indicate one standard deviation.
Dotted lines are displayed to guide the eye. Inlets show clear solutions
in the non-aggregated and turbid solutions in the aggregated state,
respectively (at 25 °C). (E) 3D-CLSM images during one reaction
cycle, measured at 25 °C and along the whole z-axis. The color code indicates the depth of the aggregates in the
sample. (F) 2D-CLSM image of vesicular structures after 1.5 h with
magnification. As a fluorescent dye 2.5 μM C153 was used.
Conclusions
We report an out-of-equilibrium reaction
cycle based on natural
ribonucleotides and their corresponding phosphoramidates. For the
first time, the software-assisted tool design of experiments (DoE)
was used to optimize and better understand a fuel-driven chemical
reaction cycle. For instance, we discovered a strong influence of
1-ethylimidazole on the hydrolysis of phosphoramidates. Such catalysis
is reminiscent of the action of the histidine group in phosphoramidase
enzymes (e.g., HINT1)[74] and has been used
strategically before by others.[25,60] In the case of the
phosphoramidate formation, we identified the pH value as the most
important parameter and revealed interesting time/temperature interactions
that are characteristic of dissipative systems. Furthermore, we were
able to identify a “sweet spot” set of reaction conditions
for both the forward and the backward reaction to enable an efficient
dissipative reaction cycle. In all cases, we found the modeled predictions
to be in good agreement with our experimental results. We expect that
the DoE approach will become more commonplace in systems chemistry
due to its ability to reveal useful insights into chemical systems
governed by a complex set of parameters.We also designed and
realized a system capable of undergoing transient
self-assembly. While both self-replicating and metabolic phosphoramidate-based
systems have been reported recently,[28,47,49] to the best of our knowledge, we report the first
example of transient compartmentalization using this particular chemistry.
Despite some limitations related to the fuel (EDC) and the accumulation
of a side product (pyrophosphate), this work therefore underscores
the potential importance of phosphoramidates in prebiotic chemistry.
Authors: S M Gryaznov; D H Lloyd; J K Chen; R G Schultz; L A DeDionisio; L Ratmeyer; W D Wilson Journal: Proc Natl Acad Sci U S A Date: 1995-06-20 Impact factor: 11.205
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