Lipid-Protein Interactions Are Unique Fingerprints for Membrane Proteins.

This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. Research Article Cite This: ACS Cent. Sci. 2018, 4, 709−717 Lipid−Protein Interactions Are Unique Fingerprints for Membrane Proteins Valentina Corradi,† Eduardo Mendez-Villuendas,† Helgi I. Ingólfsson,‡ Ruo-Xu Gu,† Iwona Siuda,† Manuel N. Melo,‡ Anastassiia Moussatova,† Lucien J. DeGagné,† Besian I. Sejdiu,† Gurpreet Singh,† Tsjerk A. Wassenaar,‡ Karelia Delgado Magnero,† Siewert J. Marrink,‡ and D. Peter Tieleman*,† † Centre for Molecular Simulation and Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada ‡ Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands S Supporting Information * ABSTRACT: Cell membranes contain hundreds of different proteins and lipids in an asymmetric arrangement. Our current understanding of the detailed organization of cell membranes remains rather elusive, because of the challenge to study fluctuating nanoscale assemblies of lipids and proteins with the required spatiotemporal resolution. Here, we use molecular dynamics simulations to characterize the lipid environment of 10 different membrane proteins. To provide a realistic lipid environment, the proteins are embedded in a model plasma membrane, where more than 60 lipid species are represented, asymmetrically distributed between the leaflets. The simulations detail how each protein modulates its local lipid environment in a unique way, through enrichment or depletion of specific lipid components, resulting in thickness and curvature gradients. Our results provide a molecular glimpse of the complexity of lipid−protein interactions, with potentially far-reaching implications for our understanding of the overall organization of real cell membranes. ■ INTRODUCTION Typical cell membranes are composed of hundreds of different lipid types that are asymmetrically distributed between the two leaflets. Embedded are many different membrane proteins, covering an estimated membrane area as large as 30% at a lipid/ protein ratio of about 50−100.1 The variety in cell membrane components gives rise to complex lipid−protein interplay.2,3 Lipids do not simply provide the matrix where proteins are embedded but can actively participate in the regulation of protein activity, trafficking, and localization.3 Proteins can either bind lipids specifically, where a clear binding site for a given lipid can be identified, or nonspecifically, where lipids act as a medium, and physical properties like thickness, fluidity, or curvature regulate protein function.4,5 The characterization of lipid−protein interactions is a key factor in our understanding of the organizational principles of cell membranes. Several experimental techniques are available to probe these interactions.6 X-ray crystallography and electron crystallization can be used to identify lipids strongly bound to proteins as these lipids have to survive the crystallization process.7,8 Lipid binding to membrane proteins can also be studied using fluorescence methods9 or by mass-spectrometry on isolated lipid−protein complexes.10 The recent development of extraction of membrane proteins from their native environment using nanodiscs is very promising in this regard.11,12 Nevertheless, these techniques mainly capture strong interactions, and although some are quantitative, they do not give high spatial resolution. © 2018 American Chemical Society Computational approaches on the other hand, such as molecular dynamics (MD) simulations, can provide such details and have been extensively used to study lipid−protein interplay.13−16 In particular the use of coarse-grain (CG) models allows simulation of reversible binding and unbinding events and identify both strong and weakly binding lipids.17,18 So far, most computational studies have been restricted to model membrane systems with a few lipid types. The recent modeling of a complex plasma membrane mixture containing more than 60 different lipids, however, has opened the way to probe lipid−protein interplay in a more realistic membrane environment.19−22 Here, we extend this work by analyzing the lipids around 10 different classes of plasma membrane proteins. We find that each protein forms its own unique lipid shell, which gives rise to a complex and nonuniform perturbation of local membrane properties (“fingerprint”). The results show a rich variety of lipid−protein interactions and protein effects on membrane properties, emphasizing the importance of not just tightly bound lipids but the overall structure of the lipid− protein matrix. ■ RESULTS AND DISCUSSION Our simulation setup consists of a membrane patch containing around 6000 lipids, represented by the CG Martini force field.23 Received: March 6, 2018 Published: June 13, 2018 709 DOI: 10.1021/acscentsci.8b00143 ACS Cent. Sci. 2018, 4, 709−717 Research Article ACS Central Science Figure 1. Unique lipid environments for different membrane proteins. (A) Simulation setup, consisting of a plasma membrane model containing 63 different lipid types with four membrane proteins embedded. (B) View of the local lipid environment around AQP1, displaying lipids within a distance cutoff of 0.7 nm from the protein surface. (C) Lipid depletion−enrichment (D−E) index in the case of AQP1, obtained from the last 5 μs of a 30 μs long simulation, and averaged over the four AQP1 molecules (error bars indicate standard deviation). The D−E index is computed by dividing the lipid composition of the first 0.7 nm shell by the bulk membrane composition. Values larger than 1 indicate enrichment of a given lipid group, while values smaller than 1 indicate depletion. (D) D−E index matrix, with average depletion (blue)/enrichment (red) for 10 different membrane proteins (the calculation for additional distance cutoffs and corresponding standard deviations are shown in Table S1). The COX-1 D−E index values for the negatively charged lipids of the lower leaflet have been omitted because they are difficult to interpret given the partial insertion of the protein only in the upper leaflet (see the note in Table S1). Lipid classes considered are phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidic acid (PA), diacyl-glycerol (DG), lyso-PC (LPC), sphingomyelin (SM), ceramide (CER), phosphatidylinositol (PI), phosphatidylinositol-(bi, tri)phosphate (PIPs), ganglioside (GM), cholesterol (CHOL), polyunsaturated (PU), fully saturated (FS), and others. The system measures ca. 42 × 42 nm in the lateral dimensions, and contains 63 different lipid types distributed asymmetrically between the leaflets. The membrane composition is based on previous work,20 and models a prototypical plasma membrane. The outer leaflet cont (...truncated)