A bottom-up approach to understanding protein layer formation at solid-liquid interfaces

Abstract

A common goal across different fields (e.g. separations, biosensors, biomaterials, pharmaceuticals) is to understand how protein behavior at solid-liquid interfaces is affected by environmental conditions. Temperature, pH, ionic strength, and the chemical and physical properties of the solid surface, among many factors, can control microscopic protein dynamics (e.g. adsorption, desorption, diffusion, aggregation) that contribute to macroscopic properties like time-dependent total protein surface coverage and protein structure. These relationships are typically studied through a top-down approach in which macroscopic observations are explained using analytical models that are based upon reasonable, but not universally true, simplifying assumptions about microscopic protein dynamics. Conclusions connecting microscopic dynamics to environmental factors can be heavily biased by potentially incorrect assumptions. In contrast, more complicated models avoid several of the common assumptions but require many parameters that have overlapping effects on predictions of macroscopic, average protein properties. Consequently, these models are poorly suited for the top-down approach. Because the sophistication incorporated into these models may ultimately prove essential to understanding interfacial protein behavior, this article proposes a bottom-up approach in which direct observations of microscopic protein dynamics specify parameters in complicated models, which then generate macroscopic predictions to compare with experiment. In this framework, single-molecule tracking has proven capable of making direct measurements of microscopic protein dynamics, but must be complemented by modeling to combine and extrapolate many independent microscopic observations to the macro-scale. The bottom-up approach is expected to better connect environmental factors to macroscopic protein behavior, thereby guiding rational choices that promote desirable protein behaviors.

Keywords: Adsorption; Desorption; Interfacial diffusion; Protein aggregation; Single-molecule.

Figure 1

Semilog plot of cumulative distributions for desorption and diffusion of Fg on fused silica at different temperatures. (A) In the cumulative surface residence time distribution, p(t) is the probability of observing a surface residence time longer than t. On these semilog axes, a single first-order desorption process would appear as straight line with slope equal to the negative of the rate constant. Significant deviations from this behavior are observed at all temperatures. (B) In the cumulative squared-displacement distribution, C(ΔR²,Δt) is the probability of observing a squared-displacement that exceeds ΔR² in a given time window (Δt) where Δt = 0.2 s. When C(ΔR²,Δt) is plotted on a logarithmic scale as a function of ΔR²/(4Δt), random-walk diffusion with a single diffusion coefficient (D) appears as a straight line with slope of −1/D. Multiple diffusive modes are required to describe these data. Reprinted from reference 90 with permission from the Biophysical Society.

Figure 2

Indirect effects of environmental conditions on protein-protein interactions. In each example, protein-protein interactions are more important in the scenario shown to the right of the dashed line. The environment can increase the frequency of protein-protein interactions by higher surface coverage due to faster adsorption and/or slower desorption (A) or faster diffusion caused by smaller corrugations in the surface interaction potential (B). The tendency of a protein-protein interaction to result in aggregation can depend on orientation bias due to anisotropic protein-surface interactions (C) or protein denaturation due to strong protein-surface interactions (D).

Figure 3

Schematic depiction of the Minton model. Different reversible pathways include: (1) direct deposition of monomer onto the surface, (2) incorporation of monomer into clusters by interfacial diffusion, and (3) piggyback deposition of monomers directly into clusters. Transition states are shown with open circles for each pathway. Pathways (2) and (3) can occur for clusters with aggregation number i. Reprinted from reference 49 with permission from the Biophysical Society.

Figure 4

Illustration of the bottom-up and top-down approaches to understanding interfacial protein dynamics. In the bottom-up approach (left), microscopic dynamics are observed directly. These observations are used to develop a model that predicts the macroscopic behavior. Comparisons between prediction and macroscopic behavior can suggest refinements to the model and new experiments at the microscopic level. In the top-down approach (right), a model incorporating a small number of dynamic processes is chosen to describe macroscopic experiments. The resulting model fit to the data yields parameters for the microscopic dynamics that depend on a correct initial choice of model.