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Modeling of solvent flow effects in enzyme catalysis under physiological conditions
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View: Figures


Image of FIG. 1.
FIG. 1.

The open conformation of phosphoglycerate kinase showing the (right) N- and (left) C-terminal domains of the protein. The N-terminal domain binds 3-phosphoglycerate and 1,3-bisphosphoglycerate, while the C-terminal domain binds MgATP and MgADP.

Image of FIG. 2.
FIG. 2.

(Left) Open conformation of the network model of PGK showing the approach of bPG to the binding pocket of the enzyme. (Right) Protein conformation after substrate binding has resulted in hinge closing to form the closed form.

Image of FIG. 3.
FIG. 3.

Structure of the model used in the simulation of the diffusive encounters of the substrate with the enzyme and the full dynamics in the enzyme vicinity. The outer circle denotes the spherical volume with radius r 2 containing a single enzyme molecule, while the inner-most circle with radius r 1 denotes the spherical volume around the enzyme within which a full dynamical calculation is carried out. Within r 1 the dynamical evolution is followed until the substrate leaves the spherical volume with radius r i > r 1 or binds and reacts.

Image of FIG. 4.
FIG. 4.

The simulated value of the diffusion coefficient compared to the estimated time-dependent diffusion coefficient, D(t), in Eq. (6), versus t −1/2 for an isolated Brownian particle with mass ratio = 10, ρ = 10, k B T = 1/3, σ = 0.5. From the fit of the data, the value of the diffusion coefficient is D = 0.063 in units of ℓ2/τ.

Image of FIG. 5.
FIG. 5.

Plot of Eq. (13) for the self-diffusion coefficient D for a tagged particle in the penetrating solvent model as a function of the mass ratio μ. From this figure we see that if the mass ratio is selected to be μ ≈ 28.5 the value of the diffusion coefficient in the penetrating solvent model, D ≈ 0.063, matches that in the explicit interaction model.

Image of FIG. 6.
FIG. 6.

Probability densities for the time of substrate binding (black), closing time of the protein (red), and the overall cycle time (blue). These probability densities were constructed from analytical fits to the simulation data for the full solvent model as a function of time. The results are for simulation conditions μ = 10, k B T = 1/3, ρ = 10, with a solvent-bead interaction σ = 0.5 ℓ, corresponding to σ = 2.5. Points on the curves are chosen to indicate statistical uncertainties in the construction of the densities.

Image of FIG. 7.
FIG. 7.

Time series showing the reduction in the number of solvent particles in the vicinity of the bPG substrate as it binds to the enzyme. The red curves show the number of solvent particles in the cell containing the bPG substrate as a function of time, while the black curves denote the distance of the substrate to the enzyme binding site. (Top) σ = 0.5, (bottom) σ = 0.7.

Image of FIG. 8.
FIG. 8.

Probability densities P(t) for the time of substrate binding (top panel), enzyme closing time (middle panel), and total reaction cycle time (bottom panel). The black curves correspond to results for the interacting solvent model, the red curves correspond to the results for the penetrating solvent model with hydrodynamics, and the blue curves are the results for the penetrating solvent model without hydrodynamics.

Image of FIG. 9.
FIG. 9.

Probability density P conv(t) of the substrate conversion time to products versus time expressed in milliseconds for the explicit solvent model. The other models yield essentially identical results since the substrate conversion is determined primarily by diffusion when the substrate is at physiological concentrations.

Image of FIG. 10.
FIG. 10.

Absorption time probability density versus time. The top panel is the absorption time for the absorption onto an inner sphere at r 1 = 7 starting from a radial distance r = 10 in length units ℓ. The bottom panel shows the absorption time density (top) and cumulative distribution (bottom) for the outer sphere, where the outer absorbing sphere radius is set to be r 2 = 31.6 and r = 10.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Modeling of solvent flow effects in enzyme catalysis under physiological conditions