Quantitative analysis of molecular partition towards lipid membranes using surface plasmon resonance

Abstract

Understanding the interplay between molecules and lipid membranes is fundamental when studying cellular and biotechnological phenomena. Partition between aqueous media and lipid membranes is key to the mechanism of action of many biomolecules and drugs. Quantifying membrane partition, through adequate and robust parameters, is thus essential. Surface Plasmon Resonance (SPR) is a powerful technique for studying 1:1 stoichiometric interactions but has limited application to lipid membrane partition data. We have developed and applied a novel mathematical model for SPR data treatment that enables determination of kinetic and equilibrium partition constants. The method uses two complementary fitting models for association and dissociation sensorgram data. The SPR partition data obtained for the antibody fragment F63, the HIV fusion inhibitor enfuvirtide, and the endogenous drug kyotorphin towards POPC membranes were compared against data from independent techniques. The comprehensive kinetic and partition models were applied to the membrane interaction data of HRC4, a measles virus entry inhibitor peptide, revealing its increased affinity for, and retention in, cholesterol-rich membranes. Overall, our work extends the application of SPR beyond the realm of 1:1 stoichiometric ligand-receptor binding into a new and immense field of applications: the interaction of solutes such as biomolecules and drugs with lipids.

Figure 1. Characterization of the lipid surface formed on a L1 sensor chip.

(A) Confocal images of flow cell (FC) 1 and 2 formed on the L1 sensor chip surface (XY plane) using 10x magnification objectives. POPC SUV labelled with either Rho-PE (control) or Rho-PE and CF were deposited on FC1 and 2, respectively. A 20 μm white scale bar is included. (B) Individual confocal images of FC1 (top) and 2 (bottom) collected at the respective white square ROI in A using a 20x magnification objective, emphasizing the flow cell boundaries. Red (Rho-PE), green (CF) and merge channels are depicted for both A and B. (C) Z-stack image of the L1 sensor chip covered with POPC SUV labelled with Rho-PE and CF. YZ orthogonal projections were produced in order to evaluate the relative Z-position of the lipid membrane and CF probe. A vertical yellow line is located in the middle of the Z projection (40 μm). Red (Rho-PE), green (CF) and merge channels are depicted. A normalized Z plot profile is also shown locating the region of the lipid deposition. (D–H) FRAP analysis of the circular ROI according to the settings explained in the Supplementary Fig. S3. Rho-PE and CF fluorescent signals were collected from the ROI and normalized to the signal on the non-bleached area. The relative fluorescence recovery () is thus shown to evaluate the lateral diffusion of the Rho-PE fluorescent lipids of the bilayer. Briefly, a time series of 4x Zoom 512 × 512 pixels images using a 20x magnification objective were taken: 10 frames were collected before bleaching of the 20 μm radius circular ROI (yellow) with 100 iterations (approx. 40 sec.) of 100% of 488, 514 and 561 laser intensity. After bleaching, 90–150 images were acquired according to the lipid mixture studied (D – POPC, E - POPC:Chol (2:1), F - POPC:Chol:SM (1:1:1), G - POPC:DPPC (1:1), H - POPC (control)) due to fluorescence recovery/sample burning percentage. Representative confocal images are presented in order of their acquisition time. Error bars correspond to the standard deviation of the mean. Experiments were performed in triplicate.

Figure 2

Application of the steady-state model to F63 (A), ENF (B) and KTP (C) SPR lipid membrane (POPC SUV) interaction data. Left: SPR sensorgrams having association time intervals of 200 s and dissociation time intervals of 800 s (truncated to emphasize the association phase and dissociation decay). Right: Data fitting with the partition formalisms (equation (S22) and (S23)). RU_S values were collected from individual F63, ENF and KTP sensorgrams at 200 s of the association phase for each solute concentration. RUs values were computed relative to the RU_L values at each concentration. The dashed line highlights regions close to linearity, as depicted in each inset. Equation (S22) was fitted to the full concentration range while equation (S23) was fitted to the data in each inset. The presented results represent one of three independent replicates.

Figure 3. HRC4 membrane interactions studied using SPR and analysing the data using the steady-state and dissociation models.

SPR sensorgrams of HRC4 interacting with deposited POPC (A), POPC:Chol (2:1) (B) and POPC:Chol:SM (1:1:1) (C) SUV. The association time was 200 s and the dissociation time was 800 s in each sensorgram. Association and dissociation phases are separated by a vertical dashed line. (D) HRC4 partition extent towards POPC, POPC:Chol (2:1) and POPC:Chol:SM (1:1:1) SUV. RU_S values were collected from individual HRC4 sensorgrams at 200 s and divided by RU_L at each concentration. The curves correspond to one representative replicate of the fit of equation (S22) to the experimental data. (E) Sensorgram dissociation data treatment with equation (S31). Each fitted curve (equation (S31)) contains data relative to an individual time point from the dissociation phase. The presented data is relative to the experiments with POPC membranes. (F) HRC4 fractional dissociation from POPC, POPC:Chol (2:1) and POPC:Chol:SM (1:1:1) SUV. Membrane associated HRC4 fractions, S_L, were plotted as a function of the dissociation time. The curves correspond to the best fit of equation (1) to one of three independent replicates. The respective residuals plots are represented. Experiments were performed in triplicate.