Identifying optimal lipid raft characteristics required to promote nanoscale protein-protein interactions on the plasma membrane

Abstract

The dynamic lateral segregation of signaling proteins into microdomains is proposed to facilitate signal transduction, but the constraints on microdomain size, mobility, and diffusion that might realize this function are undefined. Here we interrogate a stochastic spatial model of the plasma membrane to determine how microdomains affect protein dynamics. Taking lipid rafts as representative microdomains, we show that reduced protein mobility in rafts segregates dynamically partitioning proteins, but the equilibrium concentration is largely independent of raft size and mobility. Rafts weakly impede small-scale protein diffusion but more strongly impede long-range protein mobility. The long-range mobility of raft-partitioning and raft-excluded proteins, however, is reduced to a similar extent. Dynamic partitioning into rafts increases specific interprotein collision rates, but to maximize this critical, biologically relevant function, rafts must be small (diameter, 6 to 14 nm) and mobile. Intermolecular collisions can also be favored by the selective capture and exclusion of proteins by rafts, although this mechanism is generally less efficient than simple dynamic partitioning. Generalizing these results, we conclude that microdomains can readily operate as protein concentrators or isolators but there appear to be significant constraints on size and mobility if microdomains are also required to function as reaction chambers that facilitate nanoscale protein-protein interactions. These results may have significant implications for the many signaling cascades that are scaffolded or assembled in plasma membrane microdomains.

FIG. 1.

Example output from a Monte Carlo simulation. The graph shows the fraction of proteins on the membrane that are present in rafts as simulation time increases. The simulations were run to equilibrium. ρ is the ratio of diffusion rates inside and outside rafts. In this set of simulations, rafts make up 25% of the plasma membrane and are mobile. Raft diameter is 14 nm. Note there is aggregation of proteins in rafts when there is a reduced diffusion rate in rafts (i.e., when ρ is <1).

FIG. 2.

Equilibrium concentrations of proteins in lipid rafts. As in Fig. 1, proteins have no intrinsic affinity for rafts; association is driven by the parameter ρ. Simulations were run for four values of ρ, with rafts occupying 10 to 50% of the plasma membrane with diameters of 6 to 50 nm. The equilibrium concentration of proteins in lipid rafts depends modestly on ρ in both cases and weakly on raft diameter.

FIG. 3.

The coefficient of diffusion of proteins in the presence of lipid rafts. As in Fig. 1, proteins have no intrinsic affinity for rafts; association is driven by the parameter ρ. Simulations were run for four values of ρ, with rafts occupying 10 to 50% of the plasma membrane with diameters of 6 to 50 nm. D is estimated from mean squared deviation data using equation 2.

FIG. 4.

Collision rates in the presence of lipid rafts. Total collision rates between proteins were recorded during each of the simulations summarized in Fig. 2 and 3. As in Fig. 1, proteins have no intrinsic affinity for rafts; association is driven by the parameter ρ. Simulations were run for four values of ρ, with rafts occupying 10 to 50% of the plasma membrane with diameters of 6 to 50 nm. The total collision rate is the approximate number of collisions per unit time at each point in the simulation. (Note that this includes collisions taking place inside and outside rafts.)

FIG. 5.

In silico FRAP experiments. Four FRAP experiments are shown on a model plasma membrane in which rafts are not present; rafts cover 50% of the total area, are mobile, have 14-nm diameters, and slow down the diffusion of proteins by a factor of 0.5 (ρ = 0.5); or rafts cover 25% of the total area, have 14-nm diameters and slow down diffusion by a factor of 0.5 (ρ = 0.5); or rafts represent 25% of the membrane and proteins are excluded from rafts (p_rr = 1). Note the bleaching step at 500 time steps. The t_0.5 for recovery was extracted from the recovery curves (2, 9). See the supplemental material for QuickTime movies of two of these experiments.

FIG. 6.

Raft affinity as a parameter to increase intermolecular collisions. The collision rate between 1,000 randomly distributed proteins (on a 500-nm by 368-nm membrane) was measured with the reflection parameter for raft affinity (p_rn) set to 0 or 1 (the minimum and maximum values). The simulations were run for multiple raft areas. When ρ equals 1 (i.e., the local diffusion rate inside rafts is the same as in nonraft regions), increasing raft affinity produces a significant increase in collision rate (top panel and table), an effect that becomes more pronounced as raft area decreases. In contrast, the effect of changing p_rn from 0 to 1 is much less dramatic if ρ is already optimal for promoting collisions (i.e., ρ = 0.5; middle panel and table), although an effect is still seen when the raft area is low (10%). The amplification of collision rate is the ratio of collision rates when p_rn = 1 and p_rn = 0.