Dataset Information

A Database of Predicted Binding Sites for Cholesterol on Membrane Proteins, Deep in the Membrane.

ABSTRACT: The outer membranes of animal cells contain high concentrations of cholesterol, of which a small proportion is located deep within the hydrophobic core of the membrane. An automated docking procedure is described that allows the characterization of binding sites for these deep cholesterol molecules on the membrane-spanning surfaces of membrane proteins and in protein cavities or pores, driven by hydrogen bond formation. A database of this class of predicted binding site is described, covering 397 high-resolution structures. The database includes sites on the transmembrane surfaces of many G-protein coupled receptors; within the fenestrations of two-pore K+ channels and ATP-gated P2X3 channels; in the central cavities of a number of transporters, including Glut1, Glut5, and P-glycoprotein; and in deep clefts in mitochondrial complexes III and IV.

SUBMITTER: Lee AG

PROVIDER: S-EPMC6084638 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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A Database of Predicted Binding Sites for Cholesterol on Membrane Proteins, Deep in the Membrane.

Lee Anthony G AG

Biophysical journal 20180626 3

The outer membranes of animal cells contain high concentrations of cholesterol, of which a small proportion is located deep within the hydrophobic core of the membrane. An automated docking procedure is described that allows the characterization of binding sites for these deep cholesterol molecules on the membrane-spanning surfaces of membrane proteins and in protein cavities or pores, driven by hydrogen bond formation. A database of this class of predicted binding site is described, covering 39 ...[more]

PMID: 30007584

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Project description:BackgroundMany integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available.ResultsWe present a method for predicting which residues are in protein-protein binding sites within the transmembrane regions of membrane proteins. The method uses a Random Forest classifier trained on residue type distributions and evolutionary conservation for individual surface residues, followed by spatial averaging of the residue scores. The prediction accuracy achieved for membrane proteins is comparable to that for non-membrane proteins. Also, like previous results for non-membrane proteins, the accuracy is significantly higher for residues distant from the binding site boundary. Furthermore, a predictor trained on non-membrane proteins was found to yield poor accuracy on membrane proteins, as expected from the different distribution of surface residue types between the two classes of proteins. Thus, although the same procedure can be used to predict binding sites in membrane and non-membrane proteins, separate predictors trained on each class of proteins are required. Finally, the contribution of each residue property to the overall prediction accuracy is analyzed and prediction examples are discussed.ConclusionGiven a membrane protein structure and a multiple alignment of related sequences, the presented method gives a prioritized list of which surface residues participate in intramembrane protein-protein interactions. The method has potential applications in guiding the experimental verification of membrane protein interactions, structure-based drug discovery, and also in constraining the search space for computational methods, such as protein docking or threading, that predict membrane protein complex structures.

Dataset Information

A Database of Predicted Binding Sites for Cholesterol on Membrane Proteins, Deep in the Membrane.

Publications

A Database of Predicted Binding Sites for Cholesterol on Membrane Proteins, Deep in the Membrane.

Similar Datasets

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