Membrane lipids interact with proteins in a variety of ways, ranging from providing a stable membrane environment for proteins to being embedded in to detailed roles in complicated and well-regulated protein functions. Experimental and computational advances are converging in a rapidly expanding research area of lipid–protein interactions. Experimentally, the database of high-resolution membrane protein structures is growing, as are capabilities to identify the complex lipid composition of different membranes, to probe the challenging time and length scales of lipid–protein interactions, and to link lipid–protein interactions to protein function in a variety of proteins. Computationally, more accurate membrane models and more powerful computers now enable a detailed look at lipid–protein interactions and increasing overlap with experimental observations for validation and joint interpretation of simulation and experiment. Here we review papers that use computational approaches to study detailed lipid–protein interactions, together with brief experimental and physiological contexts, aiming at comprehensive coverage of simulation papers in the last five years. Overall, a complex picture of lipid–protein interactions emerges, through a range of mechanisms including modulation of the physical properties of the lipid environment, detailed chemical interactions between lipids and proteins, and key functional roles of very specific lipids binding to well-defined binding sites on proteins. Computationally, despite important limitations, molecular dynamics simulations with current computer power and theoretical models are now in an excellent position to answer detailed questions about lipid–protein interactions.

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.

Our results provide a molecular glimpse of the complexity of lipid-protein interactions, with po-27 tentially far reaching implications for the overall organization of the cell membrane.

G protein-coupled receptors (GPCRs) are membrane-bound proteins that depend on their lipid environment to carry out their physiological function. Combined efforts from many theoretical and experimental studies on the lipid-protein interaction profile of several GPCRs hint at an intricate relationship of these receptors with their surrounding membrane environment, with several lipids emerging as particularly important. Using coarse-grained molecular dynamics simulations, we explore the lipid-protein interaction profiles of 28 different GPCRs, spanning different levels of classification and conformational states and totaling to 1 ms of simulation time. We find a close relationship with lipids for all GPCRs simulated, in particular, cholesterol and phosphatidylinositol phosphate (PIP) lipids, but the number, location, and estimated strength of these interactions is dependent on the specific GPCR as well as its conformational state. Although both cholesterol and PIP lipids bind specifically to GPCRs, they utilize distinct mechanisms. Interactions with PIP lipids are mediated by charge-charge interactions with intracellular loop residues and stabilized by one or both of the transmembrane helices linked by the loop. Interactions with cholesterol, on the other hand, are mediated by a hydrophobic environment, usually made up of residues from more than one helix, capable of accommodating its ring structure and stabilized by interactions with aromatic and charged/polar residues. Cholesterol binding to GPCRs occurs in a small number of sites, some of which (like the binding site on the extracellular side of transmembrane 6/7) are shared among many class A GPCRs. Combined with a thorough investigation of the local membrane structure, our results provide a detailed picture of GPCR-lipid interactions. Additionally, we provide an accompanying website to interactively explore the lipid-protein interaction profile of all GPCRs simulated to facilitate analysis and comparison of our data.

The functional activity of membrane proteins is carried out in a complex lipid environment. Increasingly, it is becoming clear that lipids are an important player in regulating or generally modulating their activity. A routinely used method to gain insight into this interplay between lipids and proteins are Molecular Dynamics (MD) simulations, since they allow us to study interactions at atomic or near-atomic detail as a function of time. A major bottleneck, however, is analyzing and visualizing lipid–protein interactions, which, in practice, is a time-demanding task. Here, we present ProLint (www.prolint.ca), a webserver that completely automates analysis of MD generated files and visualization of lipid–protein interactions. Analysis is modular allowing users to select their preferred method, and visualization is entirely interactive through custom built applications that enable a detailed qualitative and quantitative exploration of lipid–protein interactions. ProLint also includes a database of published MD results that have been processed through the ProLint workflow and can be visualized by anyone regardless of their level of experience with MD. The automated analysis, feature-rich visualization, database integration, and open-source distribution with an easy to install process, will allow ProLint to become a routine workflow in lipid–protein interaction studies.