Protein Partitioning into Ordered Membrane Domains: Insights from Simulations.

Cellular membranes are laterally organized into domains of distinct structures and compositions by the differential interaction affinities between various membrane lipids and proteins. A prominent example of such structures are lipid rafts, which are ordered, tightly packed domains that have been widely implicated in cellular processes. The functionality of raft domains is driven by their selective recruitment of specific membrane proteins to regulate their interactions and functions; however, there have been few general insights into the factors that determine the partitioning of membrane proteins between coexisting liquid domains. In this work, we used extensive coarse-grained and atomistic molecular dynamics simulations, potential of mean force calculations, and conceptual models to describe the partitioning dynamics and energetics of a model transmembrane domain from the linker of activation of T cells. We find that partitioning between domains is determined by an interplay between protein-lipid interactions and differential lipid packing between raft and nonraft domains. Specifically, we show that partitioning into ordered domains is promoted by preferential interactions between peptides and ordered lipids, mediated in large part by modification of the peptides by saturated fatty acids (i.e., palmitoylation). Ordered phase affinity is also promoted by elastic effects, specifically hydrophobic matching between the membrane and the peptide. Conversely, ordered domain partitioning is disfavored by the tight molecular packing of the lipids therein. The balance of these dominant drivers determines partitioning. In the case of the wild-type linker of activation of T cells transmembrane domain, these factors combine to yield enrichment of the peptide at Lo/Ld interfaces. These results define some of the general principles governing protein partitioning between coexisting membrane domains and potentially explain previous disparities among experiments and simulations across model systems.