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. 2014:5:3034.
doi: 10.1038/ncomms4034.

Dynamic heterogeneity controls diffusion and viscosity near biological interfaces

Sander Pronk  1 , Erik Lindahl  1 , Peter M Kasson  2
Affiliations

Dynamic heterogeneity controls diffusion and viscosity near biological interfaces

Sander Pronk et al. Nat Commun. 2014.

Abstract

At a nanometre scale, the behaviour of biological fluids is largely governed by interfacial physical chemistry. This may manifest as slowed or anomalous diffusion. Here we describe how measures developed for studying glassy systems allow quantitative measurement of interfacial effects on water dynamics, showing that correlated motions of particles near a surface result in a viscosity greater than anticipated from individual particle motions. This effect arises as a fundamental consequence of spatial heterogeneity on nanometre length scales and applies to any fluid near any surface. Increased interfacial viscosity also causes the classic finding that large solutes such as proteins diffuse much more slowly than predicted in bulk water. This has previously been treated via an empirical correction to the solute size: the hydrodynamic radius. Using measurements of quantities from theories of glass dynamics, we can now calculate diffusion constants from molecular details alone, eliminating the empirical correction factor.

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Conflict of interest statement

ADDITIONAL INFORMATION

Competing Financial Interests: The authors declare no competing financial interests.

Figures

Fig. 1
Fig. 1. Exchange events and local diffusion
(a) Exchange events: persistence times tp and exchange times tx for 3 particles p1, p2 and p3, starting from an arbitrary point in time. (b) Two grid cells of size Δx with local hydrodynamic mobility μi and local per-particle diffusive flux j between the two halves.
Fig. 2
Fig. 2. Near-surface viscosity and diffusion
Localized viscosities from average persistence times 〈tp〉 (black, solid), and inverse diffusion constants from exchange times 〈tx〉 (blue, dashed), as a function of z distance (normal to the surface) for a number of simulated systems. Error bars show standard error estimates. For reference, the local density ρ(z) (in arbitrary units) is shown in gray. For the Lennard-Jones system, the distances are in units of σ.
Fig. 3
Fig. 3. Diffusion-viscosity decoupling
Decoupling is shown by γ > 1. Ratios are plotted as a function of surface charge on a flat silica surface, a Lennard-Jones fluid near a surface, and water on a lipid bilayer. Ratios are computed for the fluid layer close to the surface. Error bars show estimates for the standard error of the ratio means. The inset shows the same data, plotted according to 〈tp〉∝〈tx2〉/〈tx〉 (see Methods section).
Fig. 4
Fig. 4. Locally varying near-surface viscosity
Local persistence times 〈tp〉 versus decoupling ratio γ, of water molecules close to the surface of the protein ubiquitin. The correlation coefficient between the two is 0.97; the persistence times are for water molecules within 0.85 nm from the nearest atom, and averaged per residue. Error bars show standard error estimates. Inset: local persistence time 〈tp〉 mapped onto the surface of lysozyme.
Fig. 5
Fig. 5. Mobility decrease near proteins
Inverse relative viscosity as a function of distance from the protein center of mass for 4 proteins calculated from simulation. The arrows locate the bare radius R of the proteins, the lines are fits to a sigmoid function, used for integration of values r > R.
See this image and copyright information in PMC

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