Molecular Mechanisms of Membrane Curvature Sensing by a Disordered Protein

Abstract

The ability of proteins to sense membrane curvature is essential for the initiation and assembly of curved membrane structures. Established mechanisms of curvature sensing rely on proteins with specific structural features. In contrast, it has recently been discovered that intrinsically disordered proteins, which lack a defined three-dimensional fold, can also be potent sensors of membrane curvature. How can an unstructured protein sense the structure of the membrane surface? Many disordered proteins that associate with membranes have two key physical features -a high degree of conformational entropy and a high net negative charge. Binding of such proteins to membrane surfaces results simultaneously in a decrease in conformational entropy and an increase in electrostatic repulsion by anionic lipids. Here we show that each of these effects gives rise to a distinct mechanism of curvature sensing. Specifically, as the curvature of the membrane increases, the steric hindrance between the disordered protein and membrane is reduced, leading to an increase in chain entropy. At the same time, increasing membrane curvature increases the average separation between anionic amino acids and lipids, creating an electrostatic preference for curved membranes. Using quantitative imaging of membrane vesicles, our results demonstrate that long disordered amino acid chains with low net charge sense curvature predominately through the entropic mechanism. In contrast, shorter, more highly charged amino acid chains rely largely on the electrostatic mechanism. These findings provide a roadmap for predicting and testing the curvature sensitivity of the large and diverse set of disordered proteins that function at cellular membranes.

Clathrin Senses Membrane Curvature

“…For his-Δh0ENTH and his-clathrin, the number of bound proteins increased monotonically as the vesicle diameter increased, Figure 1e. This trend is consistent with our previous results 13,14 and is attributed to larger vesicles having greater surface area, thus providing increased capacity for protein binding.…”

“…This level of curvature sensitivity is much greater than the sensitivity observed when clathrin was recruited directly to the membrane by its histidine tag, Figure 4c triangles. Furthermore, the curvature sensitivity observed for clathrin in the presence of epsin1 was substantially greater than that of the amphiphysin1-clathrin system, Figure 3, or any protein we have previously studied 13,14 . What is responsible for the enhanced sensitivity of the epsin1-clathrin system?…”

“…Using TIRF microscopy, all vesicles with diameters less than 200 nm were illuminated uniformly in the evanescent field, Supplementary Figure S1. The number of proteins bound to each vesicle was estimated by dividing the total protein fluorescence intensity by the calibrated fluorescence intensity of a single Alexa Fluor 647-labeled protein, as validated previously 13,14 , Supplementary Figure S2. For his-Δh0ENTH and his-clathrin, the number of bound proteins increased monotonically as the vesicle diameter increased, Figure 1e.…”

“…These wedge-like motifs are thought to sense the presence of gaps between lipid head groups, which are more abundant on highly curved membrane surfaces 11 . Additionally, intrinsically disordered regions, which are found in most adaptor proteins 3,12 , are potent sensors of membrane curvature through a combination of entropic 13 and electrostatic mechanisms 14 .…”

An implicit lipid model for efficient reaction-diffusion simulations of protein binding to surfaces of arbitrary topology

“…Our implicit lipid model can be combined with complex networks of protein-protein interactions to efficiently simulate a range of systems involving reversible localization to the membrane, such as models of clathrin-cage assembly [7,35,61,62] and cell polarization [37], or experiments involving binding and oligomerization on membranes [63]. Integration with continuum models of surfaces, as we have done here, is especially critical for developing quantitative and dynamical models of membrane dynamics as they are driven by proteins [64][65][66][67].…”