Dataset Information

Direct Measurement of the Structure of Reconstituted High-Density Lipoproteins by Cryo-EM.

ABSTRACT: Early forms of high-density lipoproteins (HDL), nascent HDL, are formed by the interaction of apolipoprotein AI with macrophage and hepatic ATP-binding cassette transporter member 1. Various plasma activities convert nascent to mature HDL, comprising phosphatidylcholine (PC) and cholesterol, which are selectively removed by hepatic receptors. This process is important in reducing the cholesterol burden of arterial wall macrophages, an important cell type in all stages of atherosclerosis. Interaction of apolipoprotein AI with dimyristoyl (DM)PC forms reconstituted (r)HDL, which is a good model of nascent HDL. rHDL have been used as an antiathersclerosis therapy that enhances reverse cholesterol transport in humans and animal models. Thus, identification of the structure of rHDL would inform about that of nascent HDL and how rHDL improves reverse cholesterol transport in an atheroprotective way. Early studies of rHDL suggested a discoidal structure, which included pairs of antiparallel helices of apolipoprotein AI circumscribing a phospholipid bilayer. Another rHDL model based on small angle neutron scattering supported a double superhelical structure. Herein, we report a cryo-electron microscopy-based model of a large rHDL formed spontaneously from apolipoprotein AI, cholesterol, and excess DMPC and isolated to near homogeneity. After reconstruction we obtained an rHDL structure comprising DMPC, cholesterol, and apolipoprotein AI (423:74:1 mol/mol) forming a discoidal particle 360 Å in diameter and 45 Å thick; these dimensions are consistent with the stoichiometry of the particles. Given that cryo-electron microscopy directly observes projections of individual rHDL particles in different orientations, we can unambiguously state that rHDL particles are protein bounded discoidal bilayers.

SUBMITTER: Murray SC

PROVIDER: S-EPMC4775836 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Direct Measurement of the Structure of Reconstituted High-Density Lipoproteins by Cryo-EM.

Murray Stephen C SC Gillard Baiba K BK Ludtke Steven J SJ Pownall Henry J HJ

Biophysical journal 20151230 4

Early forms of high-density lipoproteins (HDL), nascent HDL, are formed by the interaction of apolipoprotein AI with macrophage and hepatic ATP-binding cassette transporter member 1. Various plasma activities convert nascent to mature HDL, comprising phosphatidylcholine (PC) and cholesterol, which are selectively removed by hepatic receptors. This process is important in reducing the cholesterol burden of arterial wall macrophages, an important cell type in all stages of atherosclerosis. Interac ...[more]

PMID: 26743047

Similar Datasets

Project description:Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on all but the most pristine density maps (<2.5 Å resolution). Here we introduce a deep learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict Cα atoms along a protein's backbone structure. The cascaded-CNN (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a protein's structure. This model predicts secondary structure elements (SSEs), backbone structure, and Cα atoms, combining the results of each to produce a complete prediction map. The cascaded-CNN is a semantic segmentation image classifier and was trained using thousands of simulated density maps. This method is largely automatic and only requires a recommended threshold value for each protein density map. A specialized tabu-search path walking algorithm was used to produce an initial backbone trace with Cα placements. A helix-refinement algorithm made further improvements to the α-helix SSEs of the backbone trace. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map protein sequences onto Cα traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6 Å and 4.4 Å resolution. It outperformed several state-of-the-art prediction methods including Rosetta de-novo, MAINMAST, and a Phenix based method by producing the most complete predicted protein structures, as measured by percentage of found Cα atoms. This method accurately predicted 88.9% (mean) of the Cα atoms within 3 Å of a protein's backbone structure surpassing the 66.8% mark achieved by the leading alternate method (Phenix based fully automatic method) on the same set of density maps. The C-CNN also achieved an average root-mean-square deviation (RMSD) of 1.24 Å on a set of 50 experimental density maps which was tested by the Phenix based fully automatic method. The source code and demo of this research has been published at https://github.com/DrDongSi/Ca-Backbone-Prediction.

Dataset Information

Direct Measurement of the Structure of Reconstituted High-Density Lipoproteins by Cryo-EM.

Publications

Direct Measurement of the Structure of Reconstituted High-Density Lipoproteins by Cryo-EM.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets