Impact of protein corona morphology on nanoparticle diffusion in biological fluids: insights from a mesoscale approach

TL;DR

This work tackles nanoparticle diffusion in crowded biological fluids by introducing a coarse-grained mesoscale model that integrates protein corona morphology with macromolecular crowding. By comparing explicit raspberry-like PC representations against equivalent hydrodynamic single-sphere models within Brownian Dynamics, the authors reveal that PC morphology and crowding strongly influence diffusion, and that an accessible-surface-area descriptor $r_ ext{eff}$ can unify disparate modeling approaches. The study demonstrates systematic deviations from single-sphere predictions that scale with the tracer–crowder size ratio and volume fraction, and shows that the commonly used $D_t$-derived size estimates can be misleading in crowded, heterogeneous environments. These insights have practical implications for interpreting diffusion measurements, estimating PC thickness, and guiding nanomaterial design in nanomedicine, especially in polydisperse protein-rich media.

Abstract

Nanoparticles (NPs) demonstrate considerable potential in medical applications, including targeted drug delivery and diagnostic probes. However, their efficacy depends on their ability to navigate through the complex biological environments inside living organisms. In such environments, NPs interact with a dense mixture of biomolecules, which can reduce their mobility and hinder diffusion. Understanding the factors influencing NP diffusion in these environments is key to improving nanomedicine design and predicting toxicological effects. In this study, we propose a computational approach to model NP diffusion in crowded environments. We introduce a mesoscale model that accounts for the combined effects of the Protein Corona (PC) and the crowded medium on NP movement. By including volume-exclusion interactions and modelling the PC both explicitly and implicitly, we identify key macromolecular descriptors that affect NP diffusion. Our results show that the morphology of the PC can significantly affect the diffusion of NPs, and the role of the occupied volume fraction and the size ratio between tracers and crowders are analysed. The results also show that approximating large macromolecular assemblies with a hydrodynamic single-sphere model leads to inexact diffusion estimates. To overcome the limitations of single-sphere representations, a strategy for an accurate parametrization of NP-PC systems using a single-sphere model is presented.

Figures (16)

• Figure 1: a-b: representation of the NP-PC complex and the protein size distribution of the corona for the models P1 (a) and P2 (b). c-d: representation of the NP-PC complex and the protein size distribution of the corona for the simplified corona models P1-S (c) and P2-S (d). e: from left to right, graphical representation of MS NP, MM NP and ML NP models. f: the three final systems investigated in this work were designed with different molar ratios of small (green), medium (red) and large (blue) proteins in the corona, resulting in three qualitatively different shapes. From left to right, the relative concentration ratio of the corona proteins is 1:1:0 (P3), 2:1:1 (P4), and 1:1:2 (P5). (g) Example of a simulation box containing plasma proteins at a total volume fraction of $\phi_\mathrm{tot} = 0.3$. The protein types and their relative concentrations are based on experimental plasma composition data.
• Figure 2: (a) Normalized, translational diffusivities for all the proteins in solution in the P1$_\mathrm{t1}$ (grey circles and P2$_\mathrm{t1}$ (gold circles) systems. Black stars indicate the protein diffusivities averaged over all the 10 systems discussed here. (b) Normalized, translational diffusivities for all the spatial arrangements (4+4) of P1 and P2 NPs (grey and gold triangles, respectively) and simplified representations (pentagons), with error bars.
• Figure 3: Main: normalized, translational diffusivities for the NPs as function of their hydrodynamic radius ($r_\mathrm{H}$). Filled and empty circles indicate RB and equivalent h-SS representations for systems with same colors, respectively. Inset: diffusivities for the RB models normalized over the equivalent h-SS ones, as function of their hydrodynamic radius $r_\mathrm{H}$.
• Figure 4: Normalised, translational diffusivities for the NPs as function of their effective radius ($r_\mathrm{eff}$). See text for derivation.
• Figure 5: Diffusivity of P1 RB in polydisperse plasma medium normalised over the diffusivities of the same tracer in different mono-crowded media as function of the crowders' size $r_\mathrm{cr}$.