PtdIns(4,5)P2 stabilizes active states of GPCRs and enhances selectivity of G-protein coupling.

G-protein-coupled receptors (GPCRs) are involved in many physiological processes and are therefore key drug targets1. Although detailed structural information is available for GPCRs, the effects of lipids on the receptors, and on downstream coupling of GPCRs to G proteins are largely unknown. Here we use native mass spectrometry to identify endogenous lipids bound to three class A GPCRs. We observed preferential binding of phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P2) over related lipids and confirm that the intracellular surface of the receptors contain hotspots for PtdIns(4,5)P2 binding. Endogenous lipids were also observed bound directly to the trimeric Gαsßγ protein complex of the adenosine A2A receptor (A2AR) in the gas phase. Using engineered Gα subunits (mini-Gαs, mini-Gαi and mini-Gα12)2, we demonstrate that the complex of mini-Gαs with the ß1 adrenergic receptor (ß1AR) is stabilized by the binding of two PtdIns(4,5)P2 molecules. By contrast, PtdIns(4,5)P2 does not stabilize coupling between ß1AR and other Gα subunits (mini-Gαi or mini-Gα12) or a high-affinity nanobody. Other endogenous lipids that bind to these receptors have no effect on coupling, highlighting the specificity of PtdIns(4,5)P2. Calculations of potential of mean force and increased GTP turnover by the activated neurotensin receptor when coupled to trimeric Gαißγ complex in the presence of PtdIns(4,5)P2 provide further evidence for a specific effect of PtdIns(4,5)P2 on coupling. We identify key residues on cognate Gα subunits through which PtdIns(4,5)P2 forms bridging interactions with basic residues on class A GPCRs. These modulating effects of lipids on receptors suggest consequences for understanding function, G-protein selectivity and drug targeting of class A GPCRs.

PyLipID: A Python Package for Analysis of Protein-Lipid Interactions from Molecular Dynamics Simulations.

Lipids play important modulatory and structural roles for membrane proteins. Molecular dynamics simulations are frequently used to provide insights into the nature of these protein-lipid interactions. Systematic comparative analysis requires tools that provide algorithms for objective assessment of such interactions. We introduce PyLipID, a Python package for the identification and characterization of specific lipid interactions and binding sites on membrane proteins from molecular dynamics simulations. PyLipID uses a community analysis approach for binding site detection, calculating lipid residence times for both the individual protein residues and the detected binding sites. To assist structural analysis, PyLipID produces representative bound lipid poses from simulation data, using a density-based scoring function. To estimate residue contacts robustly, PyLipID uses a dual-cutoff scheme to differentiate between lipid conformational rearrangements while bound from full dissociation events. In addition to the characterization of protein-lipid interactions, PyLipID is applicable to analysis of the interactions of membrane proteins with other ligands. By combining automated analysis, efficient algorithms, and open-source distribution, PyLipID facilitates the systematic analysis of lipid interactions from large simulation data sets of multiple species of membrane proteins.

Relative Affinities of Protein-Cholesterol Interactions from Equilibrium Molecular Dynamics Simulations.

Specific interactions of lipids with membrane proteins contribute to protein stability and function. Multiple lipid interactions surrounding a membrane protein are often identified in molecular dynamics (MD) simulations and are, increasingly, resolved in cryo-electron microscopy (cryo-EM) densities. Determining the relative importance of specific interaction sites is aided by determination of lipid binding affinities using experimental or simulation methods. Here, we develop a method for determining protein-lipid binding affinities from equilibrium coarse-grained MD simulations using binding saturation curves, designed to mimic experimental protocols. We apply this method to directly obtain affinities for cholesterol binding to multiple sites on a range of membrane proteins and compare our results with free energies obtained from density-based equilibrium methods and with potential of mean force calculations, getting good agreement with respect to the ranking of affinities for different sites. Thus, our binding saturation method provides a robust, high-throughput alternative for determining the relative consequence of individual sites seen in, e.g., cryo-EM derived membrane protein structures surrounded by an array of ancillary lipid densities.