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.

Figure 1:. Amino acid chain length impacts curvature sensing by disordered proteins.

(a) Schematic of the assay used for curvature sensing. (b) Lipid, protein, and merged fluorescence images of extruded vesicles with either GFP or AP180CTD bound to their surfaces. Yellow boxes highlight vesicles with different fluorescent intensities, which correspond to vesicles with different diameters. Surface plots represent the distribution of pixel intensities for puncta within the yellow boxes. Vesicles contained 87.8% DOPC, 10% DGS-NTA, 2% DP-EG10-biotin, and 0.2% Texas Red-DHPE. AP180CTD truncation mutants were labeled with ATTO-488 dye. The scale bars represent 3 μm. Only diffraction limited (< 250 nm diameter) SUVs were analyzed and fit with point spread functions (see methods). Blue arrows denote representative vesicles that were not diffraction limited and therefore were not included in our analysis. (c) The average number of proteins bound to each vesicle as a function of vesicle diameter for populations of vesicles exposed to either GFP or one of the AP180CTD truncation mutants. (d) The relative partitioning of GFP and AP180CTD truncation mutants as a function of SUV diameter. (e) The difference in protein-membrane binding energy per protein for proteins binding to SUVs of 25 nm diameter versus proteins binding to SUVs of 200 nm diameter, as a function of the length of the AP180CTD truncation mutant. The dashed line corresponds to scaling of ΔG_rel predicted by polymer theory. The concentration of all proteins in c-d was 10 nM. Sample buffer consisted of 25 mM HEPES and 150 mM NaCl (pH = 7.4). All data in c-d is presented as a 5 nm-increment moving average of the raw data (>1000 data points). Each error bar represents the standard error of the mean within each bin. Each bin contains from 20–300 data points acquired cumulatively from three independent replicates. Error bars in (e) represent propagated errors from Equation 1.

Figure 2:. Ionic strength impacts the hydrodynamic radius of AP180CTD.

(a) The impact of ionic strength on chain hydrodynamic radius and flexibility. (b-d) Normalized correlation curves for AP180CTD truncation mutants acquired from fluorescence correlation spectroscopy (FCS). The solution buffer was held at a constant pH of 7.4 using 25 mM HEPES. The NaCl concentrations used were (b) 10 mM, (c) 150 mM, and (d) 1000 mM. Correlation data is represented by symbols and was acquired from three independent replicates. The best fit lines are represented by solid curves. (e) Estimates of the protein hydrodynamic radius based on FCS data for each of the AP180CTD truncation mutants at various sodium chloride concentrations. (f) Effective Kuhn Length (l_k) and number of segments (N) of AP180CTD truncation mutants for each ionic strength condition. Dashed lines in f serve as guides to the eye. Error bars in e represent the standard deviation of the mean value obtained from three independent replicates. Error bars in f represent the propagated errors from Supporting Equations S1 and S2. P values in e were calculated using unpaired, two-tailed Student’s t tests. * P < 0.05, ** P < 0.01

Figure 3:. Ionic strength influences curvature sensing by AP180CTD.

(a) The relative partitioning of GFP and AP180CTD truncation mutants as a function of SUV diameter in buffer containing of 25 mM HEPES and 1000 mM NaCl (pH = 7.4). (b) The fractional change in curvature sensitivity for GFP and AP180CTD truncation mutants in buffer containing 1000 mM NaCl - K_rel values for 25 nm diameter SUVs in Fig. 3a (1000 mM sodium chloride) divided by K_rel values for 25 nm diameter SUVs in Fig. 1d (150 mM sodium chloride). (c) The difference in protein-membrane binding energy per protein for proteins binding to SUVs of 25 nm diameter versus proteins binding to SUVs of 200 nm diameter, as a function of the length of the AP180CTD truncation mutant. The dashed line corresponds to scaling of ΔG_rel predicted by polymer theory. (d) The relative partitioning of GFP and AP180CTD truncation mutants as a function of SUV diameter in buffer containing 25 mM HEPES and 10 mM NaCl (pH = 7.4). (e) The fractional change in curvature sensitivity for GFP and AP180CTD truncation mutants in buffer containing 10 mM NaCl - K_rel values for 25 nm diameter SUVs in Fig. 3d (10 mM sodium chloride) divided by K_rel values for 25 nm diameter SUVs in Fig. 1d (150 mM sodium chloride). (f) The difference in protein-membrane binding energy per protein for proteins binding to SUVs of 25 nm diameter versus proteins binding to SUVs of 200 nm diameter, as a function of the length of the AP180CTD truncation mutant. The dashed line corresponds to scaling of ΔG_rel predicted by polymer theory. All data in a and d is presented as a moving average of the raw data in 5 nm-increments (>1000 data points). Error bars in a, b, d, and e represents the standard error of the mean. Each bin in a and d contains 10–311 data points acquired cumulatively from three independent replicates. Each condition in b and e contains 25 to 311 data points. Error bars in c and f represent propagated errors from Equation 1. P values in b and d were calculated using unpaired, two-tailed Student’s t tests. No significance = n.s, * P < 0.05, ** P < 0.01, *** P < 0.001

Figure 4:. Curvature sensing relies on an interplay between electrostatic and entropic mechanisms.

(a) Schematic of the entropically-driven curvature sensing mechanism. (b) Curvature sensitivity of AP180CTD as a function of sodium chloride concentration. (c) Schematic of the electrostatically-driven curvature sensing mechanism. Curvature sensitivities of (d) AP180CTD-1/3 and (e) AP180CTD-2/3 as a function of sodium chloride concentration. Data in figures 4b, 4d, and 4e were replotted from figures 1d, 3a, and 3d. in the 25 to 200 nm SUV diameter range.

Figure 5:. Curvature sensing is impacted by membrane surface potential.

(a) Average measured values of zeta potential for SUVs containing pure DOPC, a lipid mixture (87.8 mol% DOPC, 10 mol% DGS-NTA, 2 mol% DP-EG10-Biotin, and 0.2 mol% Texas Red-DHPE), a lipid mixture with 1% DOPS (86.8 mol% DOPC, 10 mol% DGS-NTA, 2 mol% DP-EG10-Biotin, 1 mol% DOPS, and 0.2 mol% Texas Red-DHPE), and a lipid mixture with 2% DOPS (85.8 mol% DOPC, 10 mol% DGS-NTA, 2 mol% DP-EG10-Biotin, 2 mol% DOPS, and 0.2 mol% Texas Red-DHPE). The relative partitioning of (b) AP180CTD-1/3, (c) AP180CTD-2/3, and (d) AP180CTD as a function of SUV diameter. SUVs contained 0–2 mol% DOPS in buffer that contained 25 mM HEPES and 150 mM NaCl (pH = 7.4). (e) The fractional change in curvature sensitivity for AP180CTD truncation mutants on SUVs containing either 1% or 2% DOPS - K_rel values for 20 nm diameter SUVs containing DOPS (Figs. 5b–d) divided by K_rel values for 20 nm diameter SUVs lacking DOPS (Figs. 5b–d). Error bars in a represent the standard error of the mean (N=36 for each condition). All data in b-d is presented as a moving average of the raw data in 5 nm increments (>1000 data points). All error bars in b-e represent the standard error of the mean within each bin. Each bin in b-d contains between 10 and 311 data points acquired cumulatively from three independent replicates. Each condition in e contains 98 to 198 data points. P values in a and e were calculated using unpaired, two-tailed Student’s t tests. * P < 0.05, ** P < 0.01, *** P < 0.001