Compositional versatility enables biologically inspired reverse micelles for study of protein–membrane interactions

The study of membranes and their associated proteins is critical for understanding cellular processes. <i>In vitro</i> investigations utilizing membrane models often have limitations in their biological relevance due to the dissimilarity of experimentally compatible membrane mimetics to biological membranes. Development of membrane models that better mimic cellular membranes enables more biologically accurate observations of membrane associated proteins. In this work, we present upper tolerance concentrations for a range of lipids incorporated into reverse micelles (RMs), confirmed with dynamic light scattering (DLS). A breadth of lipid incorporation enabled biologically inspired RMs to be formulated based on the molar ratios of lipids present in eukaryotic membrane leaflets. Three systems were formulated matching lipid compositions of the inner leaflet of the plasma membrane (PM-RM), the outer mitochondrial membrane leaflet (MI-RM), and the outer rough endoplasmic reticulum membrane leaflet (ER-RM). The biologically-inspired RM formulations were characterized using DLS and cryo-electron microscopy (cryo-EM) and were found to have favorable properties for protein encapsulation. All three biologically inspired RM formulations effectively encapsulated fatty acid binding protein 4 (FABP4), a protein which shuttles fatty acids between membranes, confirmed by NMR. Also presented in this work is the first known high-resolution observation of the membrane-bound state of sterol carrier protein 2 (SCP2), a protein responsible for transporting an array of lipids between membranes. SCP2 was successfully encapsulated within all three RM systems, enabling NMR observation of the membrane interface of SCP2. The tolerances and formulations reported here allow for tailoring of RMs to mimic specific cellular membranes and will enhance studies of protein interactions with lipids and membranes among other investigations.