Substrate Channeling in an Artificial Metabolon: A Molecular

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Substrate Channeling in an Artificial Metabolon: A Molecular Dynamics Blueprint for an Experimental Peptide Bridge Yuanchao Liu, David P. Hickey, Jing-Yao Guo, Erica Earl, Sofiene Abdellaoui, Ross D. Milton, Matthew S Sigman, Shelley D. Minteer, and Scott Calabrese Barton ACS Catal., Just Accepted Manuscript • DOI: 10.1021/acscatal.6b03440 • Publication Date (Web): 27 Feb 2017 Downloaded from http://pubs.acs.org on February 27, 2017

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Substrate Channeling in an Artificial Metabolon: A Molecular Dynamics Blueprint for an Experimental Peptide Bridge

‡

Yuanchao Liua, ‡David P. Hickeyb, Jing-Yao Guob, Erica Earla, Sofiene Abdellaouib, Ross D. Miltonb, Matthew S.

Sigman*b, Shelley D. Minteer*b, Scott Calabrese Barton*a a

Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan

48824, United States b

Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States

ABSTRACT: Natural enzyme cascades utilize electrostatic guidance as an effective technique to control the diffusion of charged reaction intermediates between catalytic active sites in a process known as substrate channeling. However, limited understanding of channeling mechanisms has abated the application of this technique in artificial catalytic cascades. In this work, we utilize molecular dynamics simulations to describe the transport of anionic intermediates (e.g., oxalate and glucose-6-phosphate) on a theoretical cationic α-helix peptide bridge, and identify rules for molecular-level design of electrostatic channeling. These simulations allowed us to elucidate a surface diffusion mechanism whereby the anionic intermediate undergoes discrete hydrogen bonding interactions along adjacent cationic residues on the peptide bridge. Using MD simulations as a foundational blueprint, we synthesized an enzyme complex using a poly(lysine) peptide chain as a cationic bridge between glucose-6phosphate dehydrogenase and hexokinase. Stopped-flow lag time experiments demonstrate the ability of the artificially linked enzyme complex to facilitate electrostatic substrate channeling,

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while an analogous neutral poly(glycine)-bridged complex was used as a control to isolate proximity effects from artificial substrate channeling. KEYWORDS electrostatic diffusion, enzyme cascade, surface diffusion, glucose-6-phosphate dehydrogenase, hexokinase, poly lysine Introduction Naturally-occurring biological pathways utilize transient supramolecular complexes, known as metabolons, to catalyze several step-wise reactions in a single pot (the cell), while synthesizing a wide range of complex molecules from simple precursors or oxidizing a fuel within a metabolic pathway.1,2 Many metabolons have evolved to facilitate a phenomenon known as substrate channeling, which is precisely defined by the direct transfer of a reaction product from one catalytic active site to the next in a series of sequential catalytic reactions without equilibrating into the bulk reaction media.3-5 This creates a direct path between sequential proteins and prevents inhibition caused by binding of intermediates to unproductive active sites. Several recent studies have sought to apply this natural phenomenon to artificial reaction cascades;6-8 however, introducing substrate channeling into artificially fixed enzyme complexes is complicated by both the precise molecular control required, and the lack of experimental tools to detect whether or not substrate channeling is taking place. Therefore, methodology to aid in the design and fabrication of artificial substrate channels is greatly needed. Natural channeling mechanisms utilize intramolecular tunnels9,10, chemical swing arms11,12, spatial organization13-15 and electrostatic guidance16-20 to facilitate bound diffusion of reaction intermediates. These mechanisms highlight an important distinction between substrate channeling, which occurs through bound or restricted diffusion, and active site proximity alone. Brownian motion simulations indicate that the concentration profile for most small molecule

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intermediates is elevated only to a distance of ~1 nm from the neutral catalytic active site where it was generated.21 This suggests that channeling by proximity is only effective when adjacent catalytic active sites are extremely close to one-another. However, this minimum distance increases 10-fold in the presence of guided electrostatic interactions,18 suggesting that intermediate diffusion may be controlled over relatively large molecular distances through the incorporation of an electrostatic surface. Electrostatic guidance refers to interactions between a charged intermediate and an oppositely charged pathway that is exposed to solvent, and provides possibly the most straightforward channeling method for application in a synthetic enzyme complex. Over the past two decades, molecular dynamics (MD) simulations have aided the study of electrostatic channeling mechanisms in enzymatic super-complexes.17,20,22 This has been demonstrated in the case of naturally occurring enzyme complexes (such as the Krebs cycle and the electron transport chain), where MD simulations, combined with experimental results, indicated that restricted diffusion of intermediates caused by electrostatic interactions provided transfer efficiencies up to ~80% (compared to < 10% for that caused by non-electrostatic interaction).22 In this context, MD simulation has emerged as a technique that enables the study of electrostatic interactions that may be inaccessible to experimental detection limits or are impractical for computationally expensive techniques, such as density functional theory (DFT). Additionally, these simulations provide a platform for exploring possible substrate/intermediate combinations by describing their interaction with a computationally-optimized electrostatic surface. Using the mechanism of electrostatic guidance as an inspiration, we envisioned the use of a charged oligopeptide bridge as a molecular construct to facilitate restricted diffusion of a charged intermediate between two sequential catalytic sites. Herein, we describe the use of MD


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simulations to design and optimize a series of theoretical cationic α-helix peptides, and quantify their ability to transport charged intermediates across the theoretical surface. These optimized theoretical conditions were applied to design and construct a simple template for preparing an artificial metabolon capable of inducing restricted diffusion of a charged reaction intermediate between two sequential enzymes in a metabolic pathway. These studies provide the basis for utilizing MD simulations to strategically design synthetic substrate channeling cascades and develop a detailed understanding of the range and limitations of artificial electrostatic substrate channeling.

Results and Discussion MD Simulations Describing Adsorption and Transfer Mechanism In order to design and optimize a theoretical electrostatic channeling surface, we first needed to identify quantifiable characteristics to associate to an effective substrate channel. The electrostatic adsorption of small charged molecules on biomolecular surfaces is due to local potential energy minima caused by non-specific electrostatic interactions about the bio-interface. Mechanistically, local potential energy minimization competes with kinetic exchange between the small charged molecule and water molecules, resulting in a dynamic adsorption and desorption process that allows for bound diffusion across the charged biological surface. With this mechanism as a pretext, we used MD simulations to study the bound diffusion of a single molecule biological intermediate, oxalate, across a charged peptide surface in terms of either adsorption energy (Eads) or average coulombic energy (Ecoul). Eads is calculated based on the proximal density of oxalate around individual peptide residues (the radial distribution function (RDF)), and describes interactions caused by hydrogen bonding and polarization of the


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interacting groups, while Ecoul is the average coulombic energy over the entire simulation time, indicating the degree of electrostatic interaction between charges. RDF data provides information about the proximity of an electrostatic interaction based on both discrete hydrogen bonding interactions and cumulative adsorption energy. Here, we use the RDF of oxalate about a polypeptide chain to determine the probability that oxalate will be stabilized at a given distance from the electrostatic peptide surface. The corresponding RDF diagrams for oxalate with the charged amino acid residues of interest (lysine, Lys; arginine, Arg; and histidine, His, Figure 1A) indicate a high density of interactions occurring at ~0.15 nm for cationic Arg- and Lys-containing peptides. This corresponds closely to the minimum van der Waals’ distance associated with hydrogen bonding between anionic oxalate and either the εammonium of Lys or the guanidinium of Arg (NH+···-O).23 Using umbrella sampling, adsorption energies of 28.6 ± 0.6 kJ mol-1 and 12.5 ± 0.4 kJ mol-1 were obtained for dual-peptide adsorption on Arg-6Ala and Lys-3Ala bridges, respectively. For adsorption of a single peptide on the Lys3Ala bridge, an energy of 9.1 ± 0.5 kJ mol-1. In contrast, peptide surfaces containing His residues primarily interact with oxalate at a distance between 0.5 and 1.5 nm, which extends beyond the short-range cut-off distance for dissociation (1 nm). These results indicate a fast adsorption process and a contacting surface diffusion process. MD simulations describing coulombic energy during diffusion of oxalate along a poly(arginine) peptide chain reveal several discrete energy states that dictate its surface mobility. The corresponding coulombic energy diagram (Figure 1B) suggests that oxalate primarily exists in either a singly- or dually-bound ionic conformation with adjacent Arg residues (Figure 1C), or in an unbound state (resulting in coulombic energy of -390 kJ mol-1, -190 kJ mol-1 and 0 kJ mol1

, respectively). As oxalate travels across the peptide surface, the coulombic energy diagram


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exhibits a period of rapid flux in energy between each state (highlighted as the shaded region of the coulombic energy diagram, Figure 1B). During this process, the randomly oriented kinetic exchange between oxalate and either water molecules or adjacent charged sites results in oxalate “jumping” to a neighboring site (Figure 1C). The resulting energy-jumping region of coulombic energy diagram serves as a qualitative indication that diffusion is bound across the oligopeptide surface. Additionally, the combined RDF and coulombic energy simulation results suggest a diffusion mechanism that utilizes distinct hydrogen bonding interactions between oxalate and basic amino acid residues, which adds significant detail to previously reported mechanisms of locally restricted diffusion along an electrostatic field.17


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Figure 1. (A) Radial Distribution Function (RDF) diagrams of oxalate around peptide surface and the corresponding diagrams for oxygen atoms and carbon atoms individually (inset). (B) Short-range coulombic energy diagrams between oxalate (-2) and peptide composed of combinations of Ala and Arg (blue), Lys (red) and His (green). The shaded area in Lys peptide figure (red) represents a frequent surface diffusion process (video in ESI-2). (C) Representative MD simulations corresponding to the plateau region in coulombic energy diagram between Lys side chains (blue) and oxalate (red). Typical system configuration and coulombic energy diagram for glucose 6-phosphate (D), oxalate (E) and glyoxylate (F).


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Simulating Channeling Bridge One of the primary benefits of MD simulations is the opportunity to rapidly explore several theoretical electrostatic peptide bridges that would be experimentally impractical to screen. Using coulombic energy diagrams as described above, we sought to identify optimal cationic peptide residues to facilitate electrostatic channeling of an anionic intermediate. By tuning the type of charged amino acid (Arg, Lys, His), we studied the electrostatic interactions of various peptide structures with a dianionic oxalate molecule. Despite being highly polarized, neutral His (pKa = 6.04) peptides do not allow for ionic stabilization and therefore do not enable any adsorption of oxalate. Both Arg and Lys have the same +1 net charge, but Lys has a much weaker polarization degree (Figure S1) and H-bonding capability compared to Arg.24 Additionally, the volume of Lys’s ε-amino group is smaller than Arg’s guanidinium group and thus is not able to sterically block bulk water molecules as effectively. This resulted in stronger interactions between oxalate and water molecules, and increased kinetic exchange with the bulk media. Consequently, oxalate molecules displayed a shorter adsorption time fraction (tads) on Lys-containing peptides than their Arg counterparts (Figure 1B). Despite exhibiting shorter tads, Lys peptides displayed a unique transport phenomenon in the exceptionally high number of jumps exhibited in the coulombic energy diagram. Highlighted by the shaded area of the energy diagram in Figure 1B, the coulombic energy jumps 20 times over the 17 ns timeframe and moves frequently on the peptide surface (as seen in the movie provided in Supporting Information ESI-2). Assuming a distance between adjacent Lys residues of 0.6 nm


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(see Footnote), this results in a surface diffusivity of 2.1 × 10-6 cm2 s-1. [Footnote1: The Lys residue is separated by three alanine (Ala) residues, resulting in 0.6 nm between adjacent Lys residues along the α-helix axis (assuming a normal helical increment of 0.15 nm per residue).25] This value is reduced by one order of magnitude compared to typical bulk diffusivity of small molecules (10 - 20 × 10-6 cm2 s-1). However, the time scale for intermediate diffusion, τD = L2/D, remains small (τD = 0.48 µs for L = 10 nm) as compared to kinetic time constants of ~0.01 s for turnover numbers of ~100 s-1. This suggests that enzyme kinetics remain rate controlling and that reduced intermediate diffusion will not reduce overall rates.

Figure 2. Impact of Lys fraction (A,D) and thermodynamic adsorption energy (Eads) (B,E) on surface diffusivity (Dsurf) and adsorption time (tads). Impact of Eads on transport efficiency, characterized by tads*Dsurf (C,F). Panels A-C correspond to oxalate, and panels D-F are for G6P. Error bars for all parameters represent the standard deviation of 10 parallel simulations for each individual system.


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Understanding Intermediate Impact on Channeling through MD Simulations With the insights afforded above by coulombic energy diagrams, we sought to identify characteristics that allow various charged intermediates to efficiently channel reaction intermediates. To this end, MD simulations were performed on three common biological intermediates, glucose-6-phosphate (G6P), oxalate, and glyoxylate (Figure 1D-F and Figure S4). Both oxalate and G6P possess a -2 charge, however, G6P has a much more localized charge with respect to its molecular volume. Coulombic energy diagrams of oxalate and G6P indicate that they both readily adsorb (tads = 71 ± 7 % and 85 ± 8 %, respectively) and diffuse across the peptide bridge through a double association mechanism. However, surface interactions changed dramatically in the case of the singly charged glyoxylic acid. Despite having a similar structure to that of oxalate, hardly any double association (1.2 ± 0.6 % time fraction) can be seen in the corresponding coulombic diagram and the intermediate remains either singly associated with Lys or desorbed (tads = 12 ± 4 %). Significant desorption of glyoxylic acid arises because its single anionic site does not allow for simultaneous coordination between adjacent Lys residues. Therefore, glyoxylate depends on a dissociative jumping mechanism between residues that increases the probability of desorption from the peptide surface. This suggests that singlycharged reaction intermediates should be avoided as substrate channeling targets due to a high propensity for dissociation with the charged peptide chain. Defining the Balance between Adsorption Time and Surface Mobility In an ideal electrostatic channeling system, the substrate should have a strong adsorption and thus a long adsorption time. At the same time, the intermediate molecule should be very mobile on the peptide surface. However, as discussed above, strong interactions may decrease the intermediate’s mobility on the peptide surface. To further study the relationship between


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surface adsorption and mobility, the adsorption energy and diffusivity were quantified for oxalate on Lys peptides. Simulated electrostatic interactions between oxalate and the peptide bridge were described in terms of the absorption energy (Eads, described above), the fraction of time that the intermediate is adsorbed (tads), and the diffusivity along the surface of the peptide chain. The one dimensional surface diffusivity (Dsurf) is defined by the jumping distance to the nearest charged residue, Ljump, and the rate of coulombic energy jumping, Γ (hops s-1), so that

(1) ∗ ∗Γ

In this way, Dsurf can be correlated to tads through Eads to describe surface mobility of the intermediate and the probability of desorption from the peptide chain.

Figure 3. (A) Illustration of the proposed channeling complex using a poly(lysine) bridge as an electrostatic surface between hexokinase (HK; PDB 3VF6) and glucose-6-phosphate dehydrogenase (G6PDH; PDB 4LGV). (B) Experimental reaction scheme used to study


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electrostatic channeling of the charged intermediate (glucose-6-phosphate) across a cationic peptide bridge. (C) A sample absorbance plot highlighting the determination of experimental lag time (τ) for complexes containing a 4 nm cationic bridge (K5), neutral bridge (G5), or free enzymes.

MD simulations of both oxalate and G6P were analyzed in terms of Dsurf, and tads, where Eads was controlled by varying the ratio of charged Lys residues to neutral Ala residues (Figure 2A & 2D). These simulations demonstrate that Eads for oxalate is enhanced with increasing Lys fraction, from ~0 kJ mol-1 to 8.91 kJ mol-1 (Figure S6). As a result, tads increased gradually from 31%, reaching a maximum of 90% for oxalate, while tads for G6P ranged from 67% to 97%. This suggests that a higher fraction of Lys is required to prevent desorption of oxalate from the peptide surface, while G6P is not likely to desorb under any of the conditions studied. Additionally, we found that both oxalate and G6P maintained a high Γ (~1 times per ns) for all Lys-based peptides with the exception of Lys1-Ala6, wherein the separation of neighboring Lys residues is too great to allow for a double association diffusion mechanism. This resulted in a consistently high Dsurf for most conditions. However, according to Equation 1, Ljump affects Dsurf exponentially, where Dsurf decreases substantially for peptide chains where the Lys fraction is too high. This is due to persistent double and triple association of the intermediate to proximal Lys residues that dramatically slow diffusion across the peptide chain. To provide an optimization metric to balance adsorption energy with surface mobility, we took the product of tads × Dsurf as a measure of transport efficiency. The resulting plots of tads × Dsurf vs. Eads (Figure 2C & 2F) indicates that transport efficiency reaches maximum for peptides where the Lys fraction is slightly less than saturated (i.e., Lys6-Ala1). The simulated transport


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efficiency for a Lys-saturated peptide (Lys10-Ala0) was decreased due to the insufficient surface mobility, while the low transport efficiency of Lys1-Ala6 was due to poor adsorption time. Collectively, MD simulations indicate that an electrostatic channeling surface with high (but less than saturated) charge density and a polyanionic intermediate would strike a balance between surface mobility and adsorption energy to allow for efficient electrostatic substrate channeling. Synthesis of an Experimental Peptide Bridge Using MD simulations as a foundation, we sought to construct an experimental system that would allow us to evaluate the conclusions predicted in the simulated system. Previous studies of glucose reactivity with hexokinase (HK) and glucose-6-phosphate dehydrogenase (G6PDH) demonstrated that the enzymes could be covalently modified without negatively influencing their activity;26 therefore, we selected the reaction of glucose with HK and G6PDH as a model system to introduce an artificial channeling bridge. This enzyme cascade utilizes HK with adenine triphosphate (ATP) to phosphorylate glucose to G6P, which is subsequently oxidized by G6PDH with nicotinamide adenine dinucleotide phosphate (NADP+) as a terminal oxidant. Therefore, by measuring the absorbance corresponding to NADPH formation, we were able to indirectly monitor the activity of HK or directly measure the activity of G6PDH. These enzymes provide a well-known metabolic sequence from the glycolytic pathway that is not known to promote substrate channeling in vivo.27,28 Additionally, this reaction sequence provides G6P as a charged intermediate, which allows for the comparison of our experimental findings with those suggested by MD modeling. Based on the simulated oligopeptide chains, we synthesized an electrostatic bridge consisting of a poly(Lys) oligopeptide. In order to minimize homodimerization, we utilized a convergent synthetic approach based on a copper-free azide-alkyne Huisgen cycloaddition (Cu-


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free click chemistry, described further in the ESI). HK and G6PDH each contain only one solvent-accessible cysteine residue; therefore, we utilized these residues as anchors to attach a heterodifunctionalized cross-linker, dibenzyl cyclooctyne maleimide (DBCOM) onto G6PDH and a homodifunctionalized bismaleimide(ethylene glycol) (BM(PEG)2) onto HK (Figure 3). The maleimide-modifed HK was subsequently reacted with a synthetic peptide chain containing an N-terminal cysteine (Cys) and an azide-modified Lys residue at the C-terminal (Cys-Lys5Lys-N3). Combining azide-modified HK with cyclooctyne-modified G6PDH resulted in the exclusive formation of HK-[amino acid bridge]-G6PDH pairs. As described at the outset, the theoretical diffusion length of an intermediate from a catalytic center ranges from 1 nm to 10 nm (in the presence of a neutral or electrostatic surface, respectively). With this in mind, we aimed to prepare a bridge whose length falls within this range.

Simple density functional theory (DFT) computations indicated that the anchors,

BM(PEG)2 and DBCOM, contribute 1.6 nm and 1.3 nm, respectively. Therefore, a peptide chain of seven amino acids was chosen to provide a total bridge length of ~4 nm (a more thorough description of these calculations is provided in the Supporting Information). Two sets of oligopeptides were prepared consisting of either five lysine residues (K5, C-KKKKK-KN3) or five glycine residues (G5, C-GGGGG-KN3), with the latter acting as a neutral control bridge to distinguish electrostatic channeling from proximity effects. While this synthetic approach allows for selective cross-coupling of HK with G6PDH, it also requires the installation of neutral components to either end of the electrostatic bridge. Therefore, in order to balance the total Lys density across the channeling bridge (as discussed in the simulation results), the peptide chain was constructed using only Lys residues between the


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terminal Cys and azide-modified Lys residues. This results in an effective Lys density of ~28%, which is comparable to the theoretical optimum Lys density of 25%. Experimental Determination of Lag Time With the channeling complex in hand, we aimed to provide experimental validation to the results of the MD simulations. One of the most common methods for studying substrate channeling involves measuring the lag time required to reach steady-state flux of the reaction intermediate (G6P).8 This lag time (τ) was determined experimentally by stopped-flow injection analysis, in which the absorbance of a solution containing both enzymes was measured following an injection of glucose. From this plot, the maximum change in absorbance is extrapolated to the time (τ) at which absorbance = 0 (Figure 3). In order to determine the extent of substrate channeling in the artificially cross-linked enzyme complex, we compared the lag times resulting from an injection of glucose with that resulting from an injection of the intermediate, G6P, for the cationic (K5) and neutral (G5) complexes, as well as the free enzymes. Upon direct injection of G6P, steady-state flux is reached immediately with both free and bridge-modified G6PDH (ESI Figure S7); however, the injection of glucose results in a dramatic delay prior to reaching steady-state. Additionally, the observed lag time for K5 (τ = 70 ± 6 sec) was much shorter than either G5 or the free enzyme mixture (τ = 105 ± 1 sec and τ = 103 ± 10 sec, respectively). It should be noted that, despite the proximity afforded by its neutral bridge, the lag time for G5 is much closer to the free enzyme than that of K5. This indicates that the short τ exhibited by K5 is likely due to electrostatic channeling as opposed to a proximity effect. To further distinguish the effects of active site proximity from electrostatic channeling, we studied the lag time for varying concentrations of free enzyme, K5 and G5. High enzyme


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concentrations afford small average distances between adjacent enzyme complexes so that the amount of time required for G6P to diffuse between adjacent active sites is similar to the time required for diffusion between intramolecular active sites. Therefore, variable enzyme concentration was used to control the average distance between adjacent enzyme complexes. A comparison of the lag time exhibited by K5, G5 and free enzymes at several concentrations (Figure 4) indicates that τ for K5 and G5 is identical to the free enzymes at high enzyme concentration, where the average distance between adjacent enzymes is small. As the enzyme concentration is decreased for each complex, the corresponding τ increases; however, τ for K5 increases considerably less as a function of enzyme concentration than either G5 or the free enzyme solutions. The overall increase in lag time for K5 and G5 with decreasing enzyme concentration reflects inefficiencies in the synthetic procedure and suggests that free, uncomplexed enzyme remains in both the K5 and G5 solutions (this is confirmed by SDS-PAGE, provided in the Supporting Information). Thus, the lag time of the free enzyme was used as a point of reference to highlight the variation in τ between K5 and G5 and to isolate the source of variation in τ from the free enzyme. The lag time for K5 and G5 relative to the free enzymes (Figure 4, inset) indicate that τ does not significantly differ between G5 and free enzyme solutions at any of the studied concentrations (i.e. they both increase at a similar rate as a function of enzyme concentration), while K5 deviates significantly at lower enzyme concentrations (τ relative = -32 ± 11 sec and τ relative = -55 ± 21 sec at 10 and 4 µg mL-1, respectively). Moreover, the presence of the K5 or G5 bridge does not significantly change the activity of G6PDH (ESI Figure S7A) or that of the complete complex, based on the steady-state slope observed in Figure S7B. These results eliminate the possibility that the bridge or linking


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molecules increase bulk substrate or intermediate concentrations near the enzyme-bridge complex. Instead, the cationic bridge appears to provide a sufficient electrostatic channeling interaction to decrease the time required to reach steady-state by nearly 60 seconds, a 30% decrease, whereas no effective decrease was observed for proximity effects provided by the uncharged G5 bridge.

Figure 4. Lag time of free HK/G6PDH (Free), the cationic bridge complex (K5) and the neutral bridge complex (G5) at various enzyme concentrations using 1.4 mM phosphate, pH 7.0, 37 °C. The difference in lag time of each complex from the free enzymes is shown in the inset. Error bars represent one standard deviation; * 0.05 > p > 0.01; ** 0.01 > p > 0.001; *** 0.001 > p.

Correlating Simulations and Experiments through Lys Density and Ionic Strength MD simulations of oligopeptide bridges containing varying Lys densities suggest that an oversaturation of Lys residues will result in increased Eads and therefore decrease surface mobility. We investigated this conclusion experimentally by preparing oligopeptides containing 15 Lys residues (K15, C-K15-KN3). This resulted in a modest increase in overall bridge length (~6.2 nm compared to ~4.0 nm for K5); however, due to the number of sequential Lys residues,


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the local Lys density increased to effectively 100%. Additionally, as above, a neutral control oligopeptide was prepared with 15 Gly residues (G15, C-G15-KN3). A plot of the lag time exhibited by each enzyme complex reveals a significantly longer τ for K15 (92 ± 3 sec) than for K5 (56 ± 11 sec). While both resulted in shorter τ than either G5, G15 or free enzymes, this suggests a loss in electrostatic channeling efficiency from K5 to K15. In order to determine whether these lag time differences are the result of electrostatic channeling, we measured τ under variable ionic strength. According to MD simulations (provided in the ESI), electrostatic efficiency should decrease dramatically when the ionic strength is increased to only 100 mM. Comparison of experimental τ for each complex under variable bulk ionic strengths (Figure 5) reveals that both cationic complexes, K5 and K15, exhibit significantly shorter lag times under low ionic strength than their neutral counterparts. The reduction in lag time for K5 compared to free enzyme at zero ionic strength was 50%, compared to 30% as shown in Figure 4. However, this discrepancy in lag time dissipates under increasing bulk ionic strength so that in the presence of 100 mM NaCl, there is no difference in the lag time exhibited by K5 or K15 compared to G5, G15 or free enzyme. This is consistent with the simulation results above, suggesting that the Ecoul between anionic intermediate and cationic bridge decreases while diffusion of the intermediate into the bulk solution increases. The increased bulk ionic strength allows low-energy ion pairs to be formed with the charged intermediate, thus lowering the stabilization afforded by the electrostatic bridge.


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Figure 5. Lag time of the free enzymes, 5xLysine (K5), 15xLysine (K15), 5xGlycine (G5), and 15xGlycine (G15) complexes using 1.4 mM citrate with variable ionic strength, where ionic strength is controlled by the concentration of NaCl. Experiments were performed by adding 275 mM glucose to 10 µg mL-1 of enzyme (complex) at pH 7.0 and 37 °C. Error bars represent one standard deviation; * 0.05 > p > 0.01; ** 0.01 > p > 0.001; *** 0.001 > p.

Additionally, it should be noted that the lag time exhibited by K5 under low and intermediate ionic strength (0 – 50 mM NaCl) is consistently lower than that of K15, suggesting that persistent double and triple associations between G6P and the Lys-dense K15 decrease Dsurf and, consequently, decrease the electrostatic channeling efficiency tads × Dsurf). Taken together, these results support the MD simulation findings and suggest that optimal electrostatic substrate channeling occurs with low bulk ionic strength and using a short, highly-charged channeling bridge. Conclusions MD simulations were used to explore electrostatic surface interactions between cationic α-helix peptides and negatively charged reaction intermediates. Negatively charged oxalate was


ACS Catalysis

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found to undergo a surface diffusion mechanism across cationic poly(Arg) and poly(Lys), whereby oxalate jumps between discrete hydrogen bonding interactions between proximal charged residues. By varying the composition of the simulated peptide bridge and the characteristics of the diffusing intermediate, we were able to define several rules for designing electrostatic substrate channels. Specifically, Lys residues were found to provide a balance of intermediate adsorption and surface diffusivity that allow for efficient electrostatic channeling while preventing dissociation of the intermediate into the bulk. Additionally, simulations suggested that a dianionic intermediate is required for the double associative diffusion mechanism that prevents desorption from the peptide surface. Using these simulation-derived design principles as a foundational blueprint, we synthesized an enzyme complex by covalently modifying HK and G6PDH with a poly(Lys) bridge. The synthetically cross-linked enzyme complex was shown to facilitate electrostatic substrate channeling by decreasing the lag time required to reach steady state with respect to the intermediate from 102 ± 10 sec for a mixture of the unmodified enzymes to 56 ± 11 sec for the Lys-bridged enzymes. The study of synthetic channeling complexes allowed us to identify low enzyme concentration and low ionic strength as ideal experimental conditions to observe electrostatic substrate channeling. Our ongoing work includes molecular simulations of the complete enzyme-bridge complex, accounting for both transport and kinetic limitations.

ASSOCIATED CONTENT Supporting Information. Materials & Methods, MD simulation parameters and complete simulation analysis, description of transport variables, procedure for synthesis of channeling


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ACS Catalysis

complex, experimental controls. This material is available free of charge via the Internet at http://pubs.acs.org. AUTHOR INFORMATION Corresponding Author *Scott Calabrese Barton, [email protected] *Shelley D. Minteer, [email protected] *Matthew S. Sigman, [email protected] Author Contributions ‡These authors contributed equally. ACKNOWLEDGMENT We gratefully acknowledge support from Army Research Office MURI (#W911NF1410263) via The University of Utah. Oligonucleotides were synthesized by the DNA/Peptide Facility, part of the Health Sciences Center Cores at the University of Utah. Computational work in support of this research was performed at Michigan State University’s High Performance Computing Facility. REFERENCES (1)

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Substrate Channeling in an Artificial Metabolon: A Molecular

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